Annual Iranian Accounting Conference Proceedings

The 10 th Annual Iranian Accounting Conference 

Proceedings 

Alzahra university 

May 23 and 24, 2012 

Tehran, Alzahra University

Alzahra University 


May 23 and 24, 2012 


ISBN: 978-600-5002-53-9

Table of content 

firm Characteristics And Stock Price Reaction To Sox 404 Compliance ...... 1 

Earnings Management In Delisted Companies: Case Of Iran..................... 49 

Benchmarking The Research Productivity Of Accounting Doctorates….74 

The Usefulness Of Activity Based Costing And Traditional Costing In 

Banking Industry: Evidence From A Bank In Iran .................................. 139 

Empirical Examination Of The Relationship Between "Intellectual Capital" 

With " The Operating Cash Flow And Weighted Average Cost Of Capital" 

........................................................................................................... 150 

The Competitive Pricing Of Audit Services In A Non-Big 4 Market........ 171 

Market Incentive’s Ability To Predict Forecasts Management................. 204


The 10th Annual Iranian Accounting Conference 

May 23 and 24, 2012 

Firm Characteristics And Stock Price Reaction To 

Sox 404 Compliance 

Abstract 

Zabihollah Rezaee � 

The University of Memphis, Professor of Accountancy 

Reza Espahbodi 

Professor of Accounting, Indiana University South Bend 

Pouran Espahbodi & Hassan Espahbodi 

Professors of Accountancy, Western Illinois University 

This study examines the stock price reaction to the internal control reporting 

as required under Section 404 of the Sarbanes-Oxley Act of 2002 for three 

distinct groups of firms. After controlling for the general stock price movements, 

we find that stock returns are most negative for firms that delay filing of their 

internal control reports, continue to be negative for firms with ineffective internal 

controls, and are positive for firms with effective internal controls. The decrease 

in stock prices of the first two groups is more pronounced for those with a lower 

return on assets, higher growth rate in sales, and no prior disclosure of their 

internal controls weaknesses. Our results indicate that market participants value 

the reliability of financial information resulted from Section 404 compliance 

irrespective of firm size and debt proportion, consistent with the SEC recent 

release of Section 404 compliance for smaller public companies. 

Keywords: Capital market; Security price reaction; Corporate governance; 

Internal control. 

JEL Classification: G14; G18; M42. 

*Corresponding Author: zrezaee@memphis.edu 

The authors would like to thank Lynn Turner for his invaluable comments and 

suggestions and the Glass Lewis & Co. for data assistance.


I. Introduction 


May 23 and 24, 2012 

Failure of internal controls to prevent and detect high-profile 

financial scandals contributed to the passage of the Sarbanes-Oxley 

Act of 2002 (hereafter SOX) that requires “reports on internal control 

over financial reporting under Section 404” (hereafter SOX 404 

compliance). SOX 404 compliance, pursuant to the Securities and 

Exchange Commission (SEC) rules and Auditing Standard (AS) No. 2 

of the Public Company Accounting Oversight Board (PCAOB 2004), 

requires public disclosure of management’s assessment of the 

effectiveness of internal control, the auditor’s report on the fairness of 

management’s assessment, and the auditor’s opinion on the 

effectiveness of internal control. 1 These requirements provide a 

unique research setting to investigate the value-relevance of SOX 404 

compliance, which should be of interest to policymakers, regulators, 

financial statements users, and researchers in the United States and 

abroard. 

SOX 404 compliance has been controversial. 2 On the one hand, 

SOX 404 compliance has been criticized for its excessive costs, 

stealing the focus of key executives away from strategic thinking, 

inhibiting risk-taking and reducing innovation and creativity, and 

increasing the cost of being public and thus hampering the growth of 

the financial market. On the other hand, compliance has been praised 

for preventing/reducing cases of improprieties, improving the 

accuracy and reliability of financial reporting, and restoring investor 

confidence in financial reporting by public companies. For example, 

the 2008 Glass Lewis study reports a positive impact of Section 404 

compliance on the accuracy and reliability of financial reporting as 

evidenced by a substantial decline in the number of financial 

restatements, disclosed material weaknesses, and auditor adverse 

opinion on internal control since 2005 (Glass Lewis 2008). Gordon et 

al. (2006) show that SOX had a positive impact on the voluntary 

2


disclosure of information security activities by corporations. Such 

security is “an implicit requirement of the internal control structure 

and procedures mandated by Sections 302 and 404 of SOX” (Gordon 

et al. 2006, 507) which improves the reliability of financial reports. 

Variations of SOX 404 compliance have also been adopted worldwide 

in countries such as Canada, China, France, and Japan (Healey 2007). 

In this paper, we examine the relation between SOX 404 

compliance and the behavior of stock returns (proxy for the equity 

market reaction). Identifying the nature and extent of this relation 

contributes to the ongoing debate in the regulatory, business, and 

academic communities over the costs and benefits of SOX 404 

compliance. After controlling for the general stock price movements, 

we find that stock returns are most negative for firms that delay filing 

of their internal control reports (delinquent firms), continue to be 

negative for firms with ineffective internal controls, and are positive 

for firms with effective internal controls. This finding suggests that 

while SOX 404 compliance is rather costly, investors value and 

reward effective internal control (reliable financial data). 

Our results also show that the decrease in stock prices of the first 

two groups of firms is more pronounced for those with a lower return 

on assets, higher growth rate in sales, and no prior disclosure of their 

internal control weaknesses. This suggests that the capital markets 

perceive control weaknesses for different groups of firms differently, 

and distinguishes our paper from others that emphasize the type of 

control problems.The fact that size and debt-to-asset ratio are not 

significantly related to the stock price reaction to SOX 404 

compliance suggests that market participants value the reliability of 

financial information irrespective of the firm size and debt proportion. 

Overall, our results are consistent with and support recent initiatives 

by lawmakers for not making SOX 404 compliance optional for 

smaller firms, and by regulators (SEC) and standard setters (PCAOB) 

3



May 23 and 24, 2012 

for maintaining in AS No. 5 the principles and benefits of SOX 404 

compliance to protect investors, while making it more cost-effective, 

efficient, and scalable. 

This study is not the only one to address internal control reporting 

as required by SOX. There are quite a few recent and concurrent 

studies in this area, each making a unique contribution to the 

literature, but they are all different from ours. One set of papers (Ge 

and McVay 2005, Doyle et al. 2007a, Ashbaugh-Skaife et al. 2007, 

and Zhang et al. 2007) deals with the characteristics of firms reporting 

internal control weaknesses/deficiencies. We use many of the 

characteristics (variables) that are found to be significant determinants 

of internal control weaknesses across these studies in testing our 

hypotheses. A second set of papers (Ashbaugh-Skaife et al. 2008, and 

Doyle et al. 2007b) posits that an effective internal control system 

reduces both intentional and unintentional misstatements in financial 

reports, and shows that effective internal control increases the quality 

of accruals and thus enhances the reliability of financial reporting. 

Ashbaugh-Skaife et al. (2006) take the reliability issue one step 

further and hypothesize that the increase in reliability of financial 

reporting decreases the information risk to investors and thus reduces 

the risk (both idiosyncratic and systematic) and the cost of equity 

capital. 3 They conclude that their results are consistent with the 

assertion that effective internal control is valued by the capital market. 

We take an alternative approach to testing the value-relevance of SOX 

404 compliance by examining the stock price changes around the 

internal control reporting date. Schwert (1981) suggests that the 

analysis of market data is more powerful than other methods because 

price movements incorporate all value-relevant information as it 

becomes available. Most companies also admit in their SEC filings 

that failure to achieve and maintain effective internal control could 

result in a negative market reaction. 

4


Four papers (De Franco et al. 2005; Beneish et al. 2008; 

Hammersley et al. 2008; and Cheng et al. 2006) examine and find a 

negative market reaction to the disclosure of internal control 

weaknesses under Section 302 or Section 404. (Beneish et al. find a 

significantly negative market reaction to Section 302 disclosures, but 

no market response to Section 404 disclosures.) Hammersley et al. 

study the market reaction to Section 302 of SOX, and the papers by 

De Franco et al. and Beneish et al. span over both Section 302 and 404 

regimes. Wilkinson and Clements (2006) examine the market reaction 

to “early” filing of CEO certification under Section 302; they find no 

reaction to early filing for the market as a whole, but show that the 

market reaction is influenced by the existence of corporate governance 

mechanisms. 

The reporting by management of internal control deficiencies 

under Section 302 is motivated by certain firm characteristics 

(Ashbaugh-Skaife et al. 2007), while SOX 404 compliance requires 

both an auditor’s report on the fairness of management’s assessment 

and an auditor’s opinion on the effectiveness of internal control. As 

Lord & Benoit (2006a) report, only one in twelve (eight) companies 

with ineffective Section 404 controls reported ineffective Section 302 

controls in the previous year (quarter). Thus, combining disclosure of 

internal control weaknesses under the two regimes (or only 

considering those under Section 302) biases the market reaction 

results as many companies did not report their internal control 

weaknesses. 4 To the extent that only firms with severe internal control 

problems reported weaknesses under Section 302, the market reaction 

to the disclosure of such weaknesses would be overstated, while the 

differential market impact on the two groups (those with reported 

weaknesses in their internal control or no reported weaknesses) would 

be understated. 

5



May 23 and 24, 2012 

By contrast, we focus exclusively on SOX 404 compliance, 

pursuant to the SEC-related rules and PCAOB AS No. 2, which has 

been criticized for high compliance costs, insufficient guidance for 

internal control assessment and audit, and a potentially negative 

impact on the U.S. capital market competitiveness. Our study, 

therefore, does not suffer from the bias mentioned above. However, 

since it is very possible that the market’s assessment of risk 

incorporates Section 302 reports and expectations about internal 

control risks based on observable firm characteristics such as 

corporate governance mechanisms (as suggested by Ashbaugh-Skaife 

et al. 2006, Wilkinson and Clements 2006, and Zhang et al. 2007), our 

results are biased against finding a significant market reaction to 

internal control weaknesses. 5 Thus, our finding of a significant market 

reaction to disclosure of internal control weaknesses under Section 

404 confirms that the market had not (at least fully) adjusted for these 

weaknesses prior to such disclosures, and that Section 404 disclosures 

have information content above and beyond those under Section 302. 

This finding is in contrast to Beneish et al. (2008) and may be 

attributed to the power of our test, as discussed in the methodology. 

While Cheng et al. (2006) also focus on Section 404 exclusively, 

our study is different from theirs and those of De Franco et al. (2005), 

Beneish et al. (2008), and Hammersley et al. (2008) in at least two 

other ways (besides the methodology and thus the power of our test, 

discussed in Section IV). First, none of the above studies examines the 

value relevance of delinquency in internal control reporting (reflecting 

increased uncertainty in the market). Second, we focus on firm 

characteristics rather than the type of reported control weaknesses 

(e.g., Hammersley et al. 2008). We investigate the firm-specific 

characteristics that could possibly attenuate or exacerbate the market 

reaction to disclosed control deficiencies. We posit that a material 

weakness does not have the same consequence for every firm and thus 

6


the market perceives disclosed control weaknesses differently 

depending on the firm characteristics. 

The remainder of the paper is organized as follows. Section II 

includes a discussion of SOX 404 compliance. Our hypotheses are 

presented in Section III. Section IV explains the sample design, data, 

and methodology. Section V presents the results, and the summary 

and implications are discussed in Section VI. 

II. SOX 404 COMPLIANCE 

SOX, among other things, requires executive certification of 

internal control’s effectiveness under Section 302, and evaluation and 

reporting on the effectiveness of internal control under Section 404. 

Executive certification of internal control reporting under Section 302 

requires that management document the effectiveness of internal 

control through testing related control activities, and specify inherent 

limitations. 

Management’s reporting of internal control weaknesses, however, 

was not judged to be effective in a report to the SEC by Lord & 

Benoit (2006a). That report concluded that only one in twelve (eight) 

companies with ineffective Section 404 controls reported ineffective 

Section 302 controls in the previous year (quarter). Similarly, Glass 

Lewis & Co. (2005) reports that about 87 percent of companies that 

disclosed internal control deficiencies in the first three months of 2005 

(in compliance with Section 404) certified the effectiveness of their 

internal controls in the previous quarter (under Section 302). 

Section 404 is intended to improve the effectiveness of the design 

and operation of internal control, and compliance with Section 404 is 

required for large U.S. public companies (with equity market 

capitalization in excess of $75 million, known as accelerated filers) 

7



May 23 and 24, 2012 

for fiscal years ending on or after November 15, 2004. For other 

public companies (nonaccelerated filers), the first management report 

will be due after June 15, 2010. On October 2, 2009, the SEC 

announced that small public companies with less than $75 milion 

maket capitalization will begin complying with Section 404 after June 

2010 (SEC, 2009). 

Section 404 requires that management state its responsibility for 

designing and maintaining effective internal control and assess the 

effectiveness of such control as of the end of the company’s most 

recent fiscal year. More importantly, Section 404 requires that the 

independent auditor attest to and report on management’s assessment 

of the company’s internal control, and evaluate and opine on the 

effectiveness of internal control. SOX 404 compliance is thus more 

costly than Section 302 compliance. However, the additional cost is 

associated with the benefit of independent assurance on the 

effectiveness of internal control over financial reporting and hence the 

protection provided to investors. Section 404, therefore, allows a more 

powerful test of the value-relevance of internal control reporting as 

compared to Section 302. If investors believe that compliance with 

Section 404 imposes a dead weight cost on firms, then all firms should 

experience a negative market reaction. On the other hand, if investors 

value the effectiveness of internal control more than the imposed cost, 

firms with effective internal controls should experience a positive 

market reaction. 

Section 404 directs the PCAOB to issue guidance on the auditor 

report on internal control. This guidance is contained in PCAOB AS 

No. 2, “An Audit of Internal Control over Financial Reporting 

Performed in Conjunction with an Audit of Financial Statements.” 

Based on AS No. 2, in expressing an opinion on internal control, the 

independent auditor must perform tests of internal control to evaluate: 

(1) management’s assessment of the effectiveness of internal control 

8


by gathering sufficient competent evidence about both the process 

used and the conclusion reached by management; and (2) the 

effectiveness of both the design and operation of internal control. Any 

deficiencies found in internal control must be evaluated in terms of 

their possible effects on misstatements of an account balance or 

disclosure. 

The auditor opinion on internal control may be: (1) an unqualified 

opinion when there is no material weakness in internal control; (2) an 

adverse opinion when there is at least one material weakness; or (3) a 

disclaimer of opinion when the auditor cannot express an opinion due 

to scope limitation. Note that there may be some circumstances where 

management concludes that the company’s internal control is 

ineffective, so the auditor issues an unqualified opinion on 

management’s assessment but renders an adverse opinion on the 

effectiveness of internal control. Theoretically, there may also be 

situations where management reports that the company’s internal 

control is effective, but the independent auditor discovers material 

weaknesses and issues an adverse opinion on the effectiveness of the 

company’s internal control. Empirically, however, available data 

suggest that, with a few exceptions, management and auditors are 

always in agreement on the effectiveness of internal control. 

Based on the existence and nature of their compliance with Section 

404, therefore, firms can be classified into three groups, as depicted in 

Figure 1. Group 1 consists of firms whose management or auditors did 

not report on internal control in a timely manner (delinquent 

reporters). Group 2 includes those who received adverse or disclaimer 

opinions on the effectiveness of their internal control due to identified 

material weaknesses or scope limitation. The final category, Group 3, 

consists of firms with no material weaknesses in their internal control, 

whose auditors’ opinions on internal control effectiveness are 

unqualified. 

9


III. Hypotheses 


May 23 and 24, 2012 

Insert Figure 1 

The SEC has decided not to take any immediate actions (impose 

fines or penalties) against a public company with reported ineffective 

internal control, provided that the audit opinion on the company’s 

financial statement is unqualified and all other reporting obligations 

are satisfied on a current and timely basis. Thus, companies with 

reported ineffective internal control would not likely be prevented 

from registering stocks or offering debt. Nevertheless, noncompliant 

companies are expected to be subject to more scrutiny by regulators, 

standard-setters, the public, and the press because of potential 

problems with reliability and transparency of financial statements and 

management’s integrity; significant costs to remediate internal control 

problems; higher risk of hostile takeovers; higher cost of debt capital; 

and higher audit fees. Thus, as suggested by Bear Stearns (2004), 

“investors ordinarily should demand a higher risk premium when 

investing in a company that has ineffective or deficient controls.” The 

higher discount rate should result in reduced stock prices for these 

firms. 

In general, the equity market is expected to react negatively when: 

(1) either management or the auditor does not report on internal 

control in a timely manner, i.e., when firms are delinquent in reporting 

on their internal control; or (2) management and/or the auditor identify 

one or more material weaknesses in internal control or a scope 

limitation exists, i.e., when firms have ineffective internal control. We 

predict the most negative stock market reaction for the first group 

(delinquent firms) for several reasons. First, delinquent firms are more 

likely to have material weaknesses that are difficult to remediate. 

According to Moody’s (2006), delinquent filers have been largely 

unsuccessful in remediation of their problems. In our sample, of the 

10


50 firms in Group 1, all of whom subsequently received an 

adverse/disclaimer of opinion on the effectiveness of their internal 

control, 38 continued to receive an adverse/disclaimer opinion in the 

following year. Second, late reporting on internal control creates 

uncertainty in the markets, potentially having an adverse effect on 

stock prices. Third, delinquent filers often face loan covenant 

violations and thus either lose access to capital markets or incur an 

increase in cost of capital. This is evident from about 20 Moody’s 

rating actions in the first year of internal control reporting, the vast 

majority of which involved delinquent firms (Moody’s 2006). Finally, 

delinquent firms may become subject to adverse regulatory 

consequences and sanctions by the SEC for violations of their 

exchange listing rules, and breach of the covenants under credit 

agreements (defaulting on bank loans.( 

The negative equity market reaction for Group 2 should be less 

pronounced than that for Group 1 since market participants are more 

likely to be aware of the extent and nature of internal control 

weaknesses. Furthermore, Group 2 firms are more likely to be 

successful in remediation of their internal control weaknesses 

(Moody’s 2006). In our sample, of the 50 firms in Group 2, only nine 

received an adverse/disclaimer of opinion on their internal control 

effectiveness in the following year. 

Effective internal controls enhance investor confidence to the 

extent that they help users assess the reliability and quality of financial 

statements. Capital markets view firms with effective internal controls 

(Group 3 firms) as a sign that these companies are “in control” in 

managing their risk, achieving their business objectives, and creating 

sustainable shareholder value. 6 The market reaction to SOX 404 

compliance for Group 3, however, depends on whether the market 

expects or rewards effective internal control. If the market expects 

effective internal control and reliable financial reporting from all 

11



May 23 and 24, 2012 

public companies, there should be no market reaction (a negative 

market reaction may be observed for this group if market participants 

believe Section 404 places a dead weight cost on companies). On the 

other hand, if the market rewards effective internal control (values 

effective internal controls more than the imposed cost), the market 

reaction should be positive. A priori, we believe that the market 

expects effective internal control (reliable financial reports), so no 

market reaction is predicted for Group 3. These predictions can be 

summarized by the following hypotheses. 

Hypothesis 1: Firms that did not have their internal control 

evaluated either by management or the auditors on 

a timely basis (Group 1, delinquent firms) 

experienced the most significantly negative market 

reaction. 

Hypothesis 2: Firms that received adverse or disclaimer 

opinions on their internal control (Group 2) 

experienced 

reaction. 

a significantly negative market 

Hypothesis 3: Firms that reported effective internal control 

(Group 3) experienced no market reaction. 

The extent of the negative market reaction to receiving an 

adverse/disclaimer opinion (Group 2 firms) or to delinquency in 

reporting (Group 1 firms) on the effectiveness of internal control is 

not expected to be the same for every firm. This reaction depends on 

prior disclosure, remediation plans, and firms’ attributes. Prior 

disclosure refers to any disclosure by a company about its internal 

control weaknesses prior to the effective date of Section 404. Prior 

disclosure may have occurred through reporting on internal controls 

12


under Section 302 (requiring company executives to certify in their 

10-K and 10-Q filings the effectiveness of internal control), through 

various filings with the SEC, or through news releases. Since capital 

market participants may view prior disclosure as management’s ability 

or willingness to remediate internal control weaknesses on a timely 

basis, and since the stock prices of these firms may have already 

partially impounded the effect of the upcoming material weaknesses 

when current weaknesses are the same as those previously disclosed, 

we test the following hypothesis. 

Hypothesis 4: Firms that disclosed their internal control 

weaknesses prior to reporting on the effectiveness 

of their internal control in compliance with SOX 

404 experienced less significant decreases in their 

stock prices. 

Among the firms that identified material weaknesses in their 

internal control, stock prices of those with remediation plans should 

be less adversely affected because these companies portray to the 

market the will to correct their weaknesses. 7 That market values 

remediation is a reasonable conjecture because firms that remediated 

their reported material weaknesses in year two of SOX 404 

compliance outperformed (experienced higher stock prices than) those 

that continued to show material weaknesses (Lord and Benoit 2006b). 

However, this conjecture might not be true if the market does not 

believe that control problems can be remediated given the 

availability/adequacy of resources or the firm attributes, including 

management’s credibility. Thus, our next hypothesis is as follows. 

Hypothesis 5: Firms that reported remediation plans to correct 

identified material weaknesses in their internal 

control prior to or when reporting on the effectiveness 

of their internal control in compliance with SOX 404 

13



May 23 and 24, 2012 

may have experienced less significant decreases in 

their stock prices. 

Restatements of prior years’ earnings resulting from discovery of 

errors and fraud by management, the auditor, or the regulators are 

often associated with inadequate or ineffective internal control. Glass 

Lewis & Co. (2007) reports that about 66 percent of firms that restated 

in 2006 also disclosed material weaknesses. Also, firms that have 

recently restated their financial statements portray a lower earnings 

quality and a higher probability that their internal controls and 

auditors do not identify and correct errors and irregularities in the 

future, requiring further restatements. 8 We test these arguments with 

the following hypothesis. 

Hypothesis 6: Firms that restated their earnings in the recent past 

experienced more significant decreases in their stock 

prices. 

One of the arguments against SOX was that it may reduce or delay 

access to the capital markets (The Committee on Capital Markets 

Regulation, 2006). Moody’s, for example, took a negative rating 

action on about 20 percent of firms that disclosed internal control 

weaknesses or were delinquent in Section 404 compliance (Moody’s, 

2006). If access or terms of access to the capital market is affected, the 

contracting cost hypothesis suggests that firms that are close to 

violating their debt covenants will have a more difficult time raising 

capital (Watts and Zimmerman 1978, 1986, & 1990). We use a debtto-asset 

ratio to proxy for the existence and tightness of debt covenant 

restrictions. Similar proxies have been used in prior research (see, e.g., 

Leftwich 1981 and Salatka 1989). In addition, Duke and Hunt (1990) 

find support for debt ratios as proxies for the existence and tightness 

14


of the two most prevalent types of accounting based debt covenant 

restrictions (retained earnings and working capital). The following 

hypothesis is tested. 

Hypothesis 7: Firms with higher fractions of debt-to-assets 


prices. 

Another argument against SOX was the excessive cost of 

compliance with Section 404, especially for smaller firms (The 

Committee on Capital Markets Regulation 2006). Larger and resource 

rich companies usually maintain more sophisticated control systems 

and are less likely to suffer from control problems. This argument 

suggests that smaller firms and those with inadequate resources 

(negative/low profitability or negative cash flows) may have more 

difficulty maintaining an effective internal control system or 

correcting their internal control weaknesses (including but not limited 

to segregation of duties). We measure availability/adequacy of 

resources by the sign of cash flow 9 and return on assets. Size is 

measured by the natural logarithm of the market value of common 

equity. 10 The conjecture that larger and more resource-rich companies 

are better able to absorb the extra cost of SOX 404 compliance and 

have more resources to remediate material weaknesses leads to the 

following hypothesis. 

Hypothesis 8: Larger firms and those with adequate resources 

measured in terms of higher return on assets and 

positive free cash flow experienced less significant 

decreases in their stock prices. 

Both the difficulty and the cost of developing and subsequently 

maintaining an effective internal control system increase with the 

15



May 23 and 24, 2012 

complexity of the firm and the required controls (Ashbaugh-Skaife, et 

al. 2007). We measure complexity in three ways: (1) involvement in 

acquisition activity; (2) an above average number of business 

segments; and (3) high growth in sales. 11 Developing an effective 

internal control system for different companies, different segments, 

and managing growth is definitely more difficult and costly. The 

likelihood that the market fears or considers that maintenance and 

remediation of internal controls will be relatively more difficult for 

firms with higher complexity leads to the following hypothesis. 

Hypothesis 9: Firms with high complexity measured in terms of 

involvement in acquisition activities, a greater 

number of segments, and higher growth rates in sales 


prices. 

IV. Sample, Data, And Methodology 

Sample 

Firms in Group 1 are identified by a Glass Lewis & Co. 

publication (2005) and by a search through the Edgar database. 12 A 

list of firms in Groups 2 and 3 are obtained from the Audit Analytics 

database. The following criteria are imposed for firms in all groups: 

(1) they should be listed on the DataStream daily returns and the 

Compustat Annual Industrial files; (2) they must have the required 

firm-specific data (discussed later) available in Compustat, Audit 

Analytics, or Edgar databases; (3) they must not be financial 

institutions or utility companies; and (4) the delinquency 

announcement or internal control reporting date could not be 

simultaneous with that of other SEC filings such as 10-K reports or 

other announcements, except for announcements by delinquent firms 

regarding the late filing of their 10-K reports due to not having (fully) 

16


assessed the effectiveness of their internal controls. 13 The first two 

criteria are designed to ensure data availability to test our hypotheses. 

The third criterion excludes financial institutions and utilities because: 

(a) these firms’ equity prices may react differently to SOX 404 

compliance 14 ; and (b) one of the required firm-specific data is not 

available for these companies. The last criterion ensures that the 

market reaction to SOX 404 compliance is not confounded by other 

events or 10-K information such as earnings announcements or audit 

opinions on financial statements. 15 

The above criteria result in a sample of 50 firms for Group 1. To 

ensure that no confounding event occurred during the announcement 

period and to gather some of the firm characteristics (e.g., prior 

disclosure and remediation announcements), we search through all 

news releases and SEC filings in the Edgar database. To reduce this 

search and to be consistent with Group 1, we randomly select 

companies one by one from our list of Groups 2 and 3 until a sample 

of 50 firms that meet our selection criteria is identified for each group. 

We also check the Audit Analytics and Edgar databases to ensure that 

no firm in Group 3 had reported any material weaknesses under 

Section 302 requirements; otherwise, the market reaction to SOX 404 

compliance for this group may be a correction of the previous market 

reaction to reporting material weaknesses. The SIC distribution of the 

subsamples is fairly similar (Group 1 includes more service firms and 

fewer manufacturers, while Group 2 has a few more retailers, than the 

other groups). 

Data 

To test the first three hypotheses, daily return data are collected 

from the DataStream database. 16 For Hypotheses 4–9, additional data 

are obtained from the Compustat, Audit Analytics, and/or Edgar 

databases to measure the following attributes of sample firms: (1) size, 

17



May 23 and 24, 2012 

measured as the natural log of the market value of equity; (2) debt 

constraint—total debt over total assets and long-term debt over total 

assets; (3) cash flow—free cash flow and cash flow from operations; 

(4) return on assets; (5) prior disclosure; (6) remediation; (7) prior 

restatement of earnings; (8) three- and five-year growth rate in sales; 

(9) complexity; and (10) acquisition activity. 17 In addition to these 

variables, we include a corporate governance variable in our test of 

Hypotheses 4–9 to control for the financial reporting environment of 

firms relating to the effectiveness of the board. This variable is 

defined as the fraction of board members that are not officers, and is 

calculated from the list of directors and officers in the Disclosure 

database and checked against proxy statements or 10-K reports in the 

Edgar database for the fiscal year before the internal control reporting 

or delinquency announcement date. All other variables are measured 

as of the end of fiscal year 2004, ending on or after November 15, 

2004, when Section 404 requirements went into effect unless 

otherwise indicated. A list of these variables, their sources and 

definitions, and the expected sign of their relations with abnormal 

security returns, are shown in Table 1. 

Insert Table 1 

As explained in the last section, smaller firms and those with 

inadequate resources are more likely to have internal control 

weaknesses and less likely to be able to remediate such weaknesses 

when discovered. Consistent with prior research, the natural logarithm 

of the market value of common equity (the multiplication of 

Compustat items 24 & 25) is used as a proxy for size (SIZE). 

Adequacy of resources is measured by return on assets and cash flow. 

Cash flow is measured by two variables: the base variable is the free 

cash flow (FCF), which is the cash flow from operations minus cash 

dividends minus capital expenditures (Compustat item 308 minus item 

18


127 minus item 128); the alternative variable, OCF, is simply the cash 

flow from operations (item 308). Both variables take a value of one if 

a firm experienced a negative cash flow in fiscal years 2003 and/or 

2004 and zero otherwise. Return on assets (ROA) is measured as 

income before extraordinary items over total assets in percent 

(Compustat item 18 divided by item 6, multiplied by 100). Based on 

the above definitions, size and return on assets are expected to be 

positively (cash flow negatively) related to security returns around the 

internal control reporting or delinquency announcement date. 

Debt constraint is considered because some argue that Section 404 

reduces or delays access to the capital markets. It is measured by 

leverage, defined as total debt over total assets (TDOA, Compustat 

items 9 plus 34, divided by Compustat item 6). As an alternative 

proxy, we also use long-term debt over total assets, LTDOA 

(Compustat item 9 divided by item 6). Debt over total assets is 

expected to be negatively related to security returns around the 

internal control reporting date. 

We posit that firms that previously disclosed their internal control 

weaknesses, or have plans to remediate their problems, experience a 

less significant decrease in their security prices. This explanation may 

not hold for remediation (REMED) if the market does not believe 

remediation is possible given company resources or management’s 

credibility; thus, the direction of the relation between REMED and the 

market reaction to SOX 404 compliance is uncertain. REMED is an 

indicator variable, taking a value of one if the company identified 

plans to remediate its control problems prior to or at the time of 

internal control reporting or delinquency announcement and zero 

otherwise. Prior disclosure is measured by two alternative indicator 

variables: disclosure pursuant to Section 302 (PDIS, the base 

variable), or through other SEC filings or news releases (WARN, the 

alternate variable). Each indicator variable is set equal to one if 

19



May 23 and 24, 2012 

material weaknesses were disclosed prior to reporting under Section 

404 and zero otherwise. A positive relation is expected between prior 

disclosure and the security returns around the internal control 

weakness date. Neither REMED/WARN nor PDIS is defined for 

Group 3 (firms with effective internal control) as there cannot be any 

prior disclosure or remediation if there are no control problems. 

Restatement of prior years’ financial statements normally reflects 

lack of sufficient controls. Thus, security prices of companies that 

restated their prior years’ earnings are expected to be more negative 

around the internal control reporting or delinquency announcement 

date. We use an indicator variable (RESTATE) that takes a value of 

one if the company has announced a restatement of its past earnings 

between January 1, 2002, and the internal control reporting or 

delinquency announcement date and zero otherwise; and expect a 

negative relation between RESTATE and security prices. 

The last three variables are associated with increased control 

problems, namely that maintaining effective controls would be more 

difficult for firms with a higher sales growth, a larger number of 

segments, and acquisition activities. Sales growth is measured as 

either the three-year least-squares growth rate (GRO3) or alternatively 

as the five-year growth rate (GRO5). The other two variables are 

indicator variables. Complexity (COMP) takes a value of one if the 

number of business segments for a firm is greater than the average 

number of segments for the 150 firms in the sample and zero 

otherwise. Acquisition activity (ACQ) takes a value of one if the firm 

was involved in acquisition activities during 2003 and/or 2004 and 

zero otherwise. A negative relation is expected between each of these 

variables and the security returns around the internal control reporting 

date. 

20


Table 2 presents the Pearson correlation coefficients between the 

15 variables (10 base variables, four alternatives, and the corporate 

governance variable). While the pair-wise correlations are not 

extremely high, many are statistically significant at the .01 or .05 

level, indicating some (although not severe) multicollinearity between 

variables. Similarly, the latent roots of the independent variables 

indicate some multicollinearity among variables. 


Table 3 reports the mean, median, and standard deviation of the 10 

base variables and the corporate governance variable for the full 

sample and the three subsamples to provide a better understanding of 

the characteristics of these firms and why they could be affected by 

SOX 404 compliance. The descriptive statistics in this table show that 

Group 3 has the highest mean and median SIZE, return on assets 

(ROA) and three-year sales growth (GRO3), and the lowest mean and 

median negative free cash flow indicator (FCF). Although the greater 

sales growth should make establishing effective controls more 

difficult, the higher resources (profitability) should facilitate better 

internal control. The portfolio return for this group, therefore, should 

not be negatively affected unless the market believes that Section 404 

imposes a dead weight cost on firms. The other two groups, however, 

should be negatively affected to various degrees by SOX 404 

compliance. 

Table 3 also shows that although Group 1 is larger than Group 2 in 

terms of mean and median SIZE, it has a much lower return on assets 

and a greater incidence of negative free cash flow. Given the 

low/negative profitability, a much greater sales growth for Group 1 

should make it more difficult to maintain an effective internal control 

system, exacerbating the effect of the uncertainty due to delinquency 

in SOX 404 compliance. Thus, Group 1 is expected to experience a 

21



May 23 and 24, 2012 

more significantly negative return as compared to Group 2. The 

degree to which any of the portfolio (Group) returns are actually 

influenced by SOX 404 compliance, however, depends on whether the 

equity prices of firms in the corresponding portfolio systematically 

reacted to the delinquency announcements or the internal control 

reports. 


Methodology 

Hypotheses 1–3 relate to the average impact of SOX 404 

compliance on stock prices of groups of firms. Regardless of whether 

a mean effect is observed for a group, stock returns may vary crosssectionally 

with the firm characteristics. Hypotheses 4–9, therefore, 

examine the effect of firm characteristics and other important factors 

on the stock market reaction to SOX 404 compliance. 

Hypotheses 1–3 are tested by employing a Multivariate Regression 

Model (MVRM) proposed by Schipper and Thompson (1983). 18 The 

standard event study methodology assumes that the residuals are 

identically and independently distributed across firms. Because the 

magnitude of the unsystematic risk differs across firms, however, the 

variance of abnormal returns will vary across firms (Fama, 1976). In 

addition, security returns for firms in the same industry are 

contemporaneously correlated because such firms will react similarly 

to any unanticipated event (Schwert, 1981). The MVRM incorporates 

both the cross sectional heteroscedasticity and the contemporaneous 

correlation of the residuals into the estimation process, allowing joint 

hypotheses to be tested utilizing the F statistic defined by Rao (1973). 

The joint hypothesis tests are of special importance in this study since 

as discussed earlier, firms are expected to be differentially affected by 

SOX 404 compliance. 

22


The MVRM model conditions the return generating process on the 

occurrence or nonoccurrence of an event (SOX 404 compliance) by 

adding a zero one dummy variable to the market model. This dummy 

variable is set equal to one for the three-day event period 

corresponding to trading days t=–1, t=0, and t=+1 relative to the 

internal control reporting date (as the exact timing of the information 

release is unknown), and equal to zero otherwise. The internal control 

reporting date is the filing date with the SEC or the announcement day 

for delinquent firms. The coefficient of this dummy variable measures 

the impact of SOX 404 compliance on stock returns. The model is a 

system of three portfolio return equations—one for each group of 

firms. The equation for each portfolio is: 

where: 

R ~ jt = αj + βj R ~ mt + γj Dt + ˜ejt 

R ~ jt = the return on portfolio j (j=1, 2, 3) on day t (t=1, 2, ..., T). 

T=241, the total number of daily return observations from 120 

days prior to the internal control reporting date to 120 days 

after. Returns for each portfolio are weighted based on the full 

estimated covariance matrix of residuals in order to increase 

the efficiency of parameter estimates. Specifically, 

R ~ jt = P'j Rijt, where 

Rijt = the vector of returns on all i firms in portfolio j on day t, 

P'j= the transpose of portfolio j weights, Pj, 

Pj = (1'Sj –1 1) –1 Sj –1 1, 

1 = a vector of ones, and 

Sj = the full estimated variance-covariance matrix of residuals from 

first-pass OLS regressions similar to Equation (1), but on each 

firm in portfolio j; 

23 

(1)



May 23 and 24, 2012 

R ~ mt = the simple average of returns on the Standard and Poor’s 500 

Index on day t (t=1, 2, ..., T); since the internal control 

reporting dates vary somewhat among firms (although they are 

clustered, the reason for using Schipper and Thompson (1983) 

methodology), day t is relative to the internal control reporting 

date and R ~ mt is the simple average of market returns on day t 

for all firms in portfolio j; 

αj = intercept coefficient for portfolio j; 

βj = risk coefficient for portfolio j; 

γj = the effect of SOX 404 compliance on portfolio j; 

Dt = dummy variable which equals 1 during the three-day period 

(t= –1, t= 0, and t= +1) relative to the internal control 

reporting or delinquency announcement date and zero 

otherwise; and, 

˜ejt = random disturbance which is assumed to be normal and 

independent of R ~ mt and Dt. 

Following Theil (1971, p. 306), the system of regressions in 

Equation (1) can be generalized as: 

� 

� 

� 

R 

�~ 

� 

~R~ 

R 

or 

3 

1 

2 

where: 

� 

~ 

� X 

� � 

� � � 0 

� � 

� � 

0 

0 

~ 

X 

0 

0 � �� 

1 � �e 

~ 

1 � 

� � � 

� �~ 

0 

� 

� * � e 

� 2 2 

~ � � � 

� ~ 

X 

� 

�� 

� �� 

�� 

e3 

� 

3 � 

24 

(2) 

R ~ = X ~ β + ˜e, (3)


R ~ j = T x 1 vector, (the elements of the vector are R ~ j1, R ~ j2, ..., 

R ~ jT); 

X ~ = T x 3 matrix of independent variables which is the same 

for each equation in the system of regressions. The first 

column of this matrix is of ones, the second column is of 

the average daily returns on R ~ m, and the last column is of 

the dummy variable for SOX 404 compliance; 

βj = 3 x 1 vector of coefficients; and, 

˜ej = T x 1 vector of disturbances. 

Estimation of the multivariate regression model in Equation 

(3) assumes that the residuals are independently, identically 

distributed within each equation. As Smith et al. (1986) suggest, 

however, this is not likely to be true. The estimation of the system in 

Equation (3), therefore, must be adjusted for possible time-series 

heteroscedasticity (see Smith et al. 1986, 477, for a detailed discussion 

of this situation). To correct for time-series heteroscedasticity, i.e., to 

obtain efficient estimates of the coefficients in Equation (3), the 

generalized method of moments (GMM) estimation is used. 

To test the effect of firm characteristics and other factors on stock 

market reaction to SOX 404 compliance (i.e., to test Hypotheses 4–9 

for Groups 1 and 2), we regress the abnormal returns percentage on 

firm characteristics (the 10 base variables and the corporate 

governance variable in Table 1). 19 This regression is only based on the 

100 firms in Groups 1 and 2 because two variables (prior disclosure 

and remediation) are not defined for Group 3 (those with effective 

internal control) and because the direction of the market reaction to 

SOX 404 compliance is different for Group 3 versus the other two 

groups. Abnormal return for each firm (the dependent variable in this 

regression) is the dummy variable coefficient in an OLS regression of 

security return for that firm on the market return and a dummy 

variable corresponding to SOX 404 compliance (an OLS regression 

25



May 23 and 24, 2012 

similar to Equation (1) for that firm). The dummy variable equals one 

during the three-day period (t=–1, t=0, and t=1) relative to the internal 

control reporting or delinquency announcement date, and zero 

otherwise. 

V. Results 

Table 4 reports the portfolio abnormal returns for the three-day 

period (t=–1, t=0, and t=1) relative to the internal control reporting or 

delinquency announcement date based on the Multivariate Regression 

Model (MVRM) for the three groups. T-Values and their associated 

significance levels are also reported in that table. The groups are 

subsamples of all the firms in the sample (150 firms), as discussed 

before in Table 3. The estimates in Table 4 are the coefficients of the 

dummy variable in a regression of portfolio returns (weighted based 

on the full estimated covariance matrix of residuals) on the market 

return and a dummy variable corresponding to SOX 404 compliance 

(represented by Equation (3)). Each dummy variable equals one 

during the three-day period (t=–1, t=0, and t=1) relative to the internal 

control reporting date, and zero otherwise. 


The abnormal returns are significant for each of the subsamples, 

positive for Group 3 and negative for Groups 1 and 2. 20 Overall, the 

results support Hypotheses 1 and 2, suggesting that market 

participants penalize delinquent firms and those with ineffective 

internal control. The positive and significant abnormal return for 

Group 3 suggests that market participants value effective internal 

control (reliable financial reporting) despite the high SOX 404 

compliance cost. This result supports the notion that the market 

rewards an effective internal control system rather than expecting or 

demanding it (as predicted a priori). The estimated coefficient of the 

dummy variables for Group 1 is more significantly negative than that 

26


for Group 2. The estimated coefficient (t-statistic) associated with 

Group 1 is –.505 (–8.66); the corresponding value for Group2 is –.171 

(–3.62). The F-statistics (not reported in Table 4) show that 

differences between the coefficients for firms in the three groups are 

significant at the .01 level. These results indicate that, although the 

coefficients for Groups 1 and 2 are both significantly negative, 

delinquent companies (Group 1) experienced a more significant 

decline in their stock prices than those with ineffective internal 

controls. 

Table 5 allows for testing Hypotheses 4–9 by reporting the 

estimated coefficients of independent variables, their T-statistics, and 

significance levels, in an OLS regression of abnormal returns % on 

firm characteristics. As discussed before, this regression is only based 

on the 100 firms in Groups 1 and 2. Abnormal return for each firm 

(the dependent variable in this regression) is the dummy variable 

coefficient in an OLS regression of security return for that firm on the 

market return and a dummy variable corresponding to SOX 404 

compliance (an OLS regression similar to Equation (1) for that firm). 

The dummy variable equals one during the three-day period (t=–1, 

t=0, and t=1) relative to the internal control reporting or delinquency 

announcement date, and zero otherwise. The coefficients in Table 5 

estimate the change in abnormal return % as a result of a unit change 

in a given variable. For example, a one percent increase in sales 

growth reduces estimated abnormal return by .014%; the coefficient of 

2.082 for prior disclosure (PDIS) means that firms that previously 

disclosed their internal control weaknesses (for whom PDIS = 1) are 

estimated to experience 2.082% more positive (or less negative) 

abnormal returns than firms with no prior disclosure of their 

weaknesses (PDIS = 0). 


27



May 23 and 24, 2012 

The estimated coefficients reported in Table 5 are significant for 

the corporate governance variable and four main variables: prior 

disclosure (PDIS), return on assets (ROA), remediation (REMED), 

and growth (GRO3). 21 None of the other coefficients are significant at 

any meaningful level, and all significant coefficients have the 

expected sign. 22 The coefficient for prior disclosure is positive and 

significant at the .003 level, indicating that firms that reported their 

internal control weaknesses pursuant to Section 302 experienced less 

significant decreases in their stock prices. This result may imply that 

the market views prior disclosure as management’s ability or 

willingness to take a corrective course of action on a timely basis to 

remediate internal control weaknesses. It is also plausible that the 

positive coefficient for prior disclosure (i.e., a lower negative market 

reaction to disclosure under Section 404) indicates that the 

information about internal control problems for these firms has 

already been incorporated into stock prices pursuant to disclosure of 

material weaknesses under Section 302 requirements. This alternative 

explanation assumes that the current weaknesses are the same as those 

previously disclosed under Section 302, which is less likely because 

the reported weaknesses under Section 404 were fairly the same for 

only seven firms out of the 23 in Groups 1 and 2 who previously 

reported weaknesses in their internal control. 

The coefficient for the return on assets is also positive and 

significant at the .000 level. This result indicates that firms with more 

resources at their disposal experienced a lower decline in their stock 

prices, as they are expected to be able to remediate their internal 

control weaknesses. The coefficient of the dummy variable for the 

sales growth is negative and significant at the .096 level, indicating 

that firms with higher sales growth experienced a more significant 

decline in their stock prices. This result is expected as growth 

increases the difficulty and cost of developing and maintaining an 

effective internal control system that is associated with an increase in 

28


the complexity of the firm. Finally, the coefficient for the remediation 

is also negative and significant at the .053 level. This result may be 

interpreted in two ways. First, many plans to remediate control 

weaknesses lack specificity. For example, MarkWest Energy Partners 

LP states, “we are in the process of addressing our internal control 

over financial reporting.” The market may perceive lack of specific 

plans to remediate control weaknesses as lack of management 

commitment to do so. Alternatively, the market may perceive the 

firm’s remediation plan as a sign that severe internal control problems 

exist which could not possibly be remediated given the resources 

available to the firm. 

Based on the relative size of the t-statistics reported in Table 5, 

return on assets, prior disclosure, remediation and growth are the firm 

characteristics that have the most significant relation with stock price 

reaction to SOX 404 compliance. In particular, security prices of firms 

with a simple control environment, previously disclosed internal 

control weaknesses, and resources to remediate their problems, are 

less negatively affected by SOX 404 compliance. On the other hand, 

having remediation plans exacerbate the negative effect of ineffective 

internal control on stock prices. 

Size and debt-to-asset ratio are not significantly related to the 

stock price reaction to SOX 404 compliance. There are two 

implications here. (1) To the extent that quality of accounting 

information increases with size and smaller companies have more 

difficulty maintaining an effective internal control system because of 

inadequacy of resources, SOX 404 compliance should be at least as 

useful for smaller companies as it is for larger firms in increasing 

investors’ confidence in financial reports. Thus, the calls for 

eliminating SOX 404 compliance for nonaccelerated filers may be 

premature. Further research is needed to examine the impact of SOX 

404 compliance on stock prices of nonaccelerated filers. (2) The 

results provide indirect evidence of no association between SOX 404 

29



May 23 and 24, 2012 

compliance and the cost of debt capital, which contradicts one of the 

major objections to SOX: reducing or delaying access to the capital 

markets. Future research can examine the direct association between 

SOX 404 compliance and the cost of debt capital by investigating 

changes in debt covenants resulting from SOX 404 compliance. 

VI. Summary And Implications 

This study examines the stock price reaction to SOX 404 

compliance and the relation between abnormal returns and firmspecific 

characteristics. Identifying the nature and extent of this 

relation contributes to the ongoing debate over the value-relevance of 

SOX 404 compliance. Recent research (Ashbaugh-Skaife et al. 2006) 

and anecdotal evidence (e.g., Donaldson 2005; Moody’s 2005) 

suggest that internal control reporting is value-relevant to investors in 

assessing the quality, reliability, and transparency of financial 

reporting. We test the value-relevance of SOX 404 compliance by 

examining the stock price changes around the internal control 

reporting date. Schwert (1981) suggests that the analysis of market 

data is more powerful than other methods because price movements 

incorporate all value-relevant information as it becomes available. 

Most companies also admit in their SEC filings that failure to achieve 

and maintain effective internal control could result in a negative 

market reaction. While De Franco et al. (2005), Cheng et al. (2006), 

and Beneish et al. (2008), also examine the stock price reaction to 

SOX 404 compliance, our methodology provides a more powerful test 

of the value relevance of such compliance and our study is the only 

one considering the stock price reaction to delinquency in SOX 404 

compliance and identifying the firm characteristics that explain the 

cross-sectional variation in abnormal returns. 

Specifically, our results indicate that firms that either reported 

internal control weaknesses or were not able to report on the 

30


effectiveness of their internal control on a timely basis experienced 

significantly negative abnormal returns around the internal control 

reporting date. In contrast, firms with effective internal control and 

timely reports experienced significant and positive abnormal returns. 

These results imply that SOX 404 compliance is value-relevant and 

perceived by investors as improving the quality of financial reporting. 

Our results also show that the negative abnormal returns were more 

pronounced for firms with higher growth in sales (a measure of 

complexity) and less pronounced for firms that previously disclosed 

their weaknesses or had higher resources (return on assets). Size and 

debt-to-asset ratio are not significantly related to the stock price 

reaction to SOX 404 compliance, suggesting: (1) that the market 

values effective internal controls regardless of firm size; and (2) that 

Section 404 may not have affected the cost of debt capital. 

Overall, our results suggest that investors perceive that costeffective, 

efficient, and scalable regulations such as SOX Section 404 

can create a sound and safe environment for public companies to 

improve the effectiveness of internal control over financial reporting 

and thus the accuracy and reliability of financial reports. Our findings 

are also consistent with Lord & Benoit’s (2006a) report, arguing that 

making internal control reporting voluntary or requiring only the 

reporting of disclosure controls and procedures by management would 

not serve the best interests of investors. Finally, our findings 

corroborate the recent decision by the SEC requiring smaller public 

companies to comply with Section 404 as of June,2010 and 

initiatives taken by regulators in other counties to madate internal 

control reporting for public companies . These findings should be 

useful to regulators and policymakers in contemplating further 

revisions, simplifications, and modifications of SOX 404 compliance 

for nonaccelerated filers and foreign issuers, and to countries 

worldwide in considering mandating comparable SOX 404 

compliance. 

31


Endnotes 


May 23 and 24, 2012 

1 On May 23, 2007, the SEC approved its interpretive guidance, which eliminates the 

auditor’s opinion on management’s assessment of internal controls (SEC 2007). The 

PCAOB adopted AS No. 5 on May 24, 2007, which supersedes AS No. 2. Our test period, 

however, only covers SOX 404 compliance requirements under AS No. 2. 

2 Other provisions of the Sarbanes-Oxley Act have also been controversial. For example, 

Cullinan et al. (2006) show that prohibition of loans to executives under Section 402 of 

SOX has reduced financial misstatements and improved the accuracy and reliability of 

financial reporting. On the other hand, Ascioglu et al. (2005) find only weak evidence that 

auditor compensation lowers firm disclosure quality and market liquidity, underscoring the 

need to revisit the rationale and scope of restrictions on non-audit services imposed under 

Section 201 of SOX. 

3 Schneider and Church (2008) suggest that adverse internal control opinions weaken the 

importance assigned to financial reports in assessing companies’ creditworthiness and 

reduce lenders’ confidence that financial statements are presented fairly, thereby increasing 

the cost of debt capital. Contrary to Ashbaugh-Skaife et al. (2006), however, Ogneva et al. 

(2007), find slightly higher cost of equity capital for firms disclosing material weaknesses 

under Section 404, which disappears after controlling for firm characteristics associated 

with disclosure of material weaknesses. Ashbaugh-Skaife et al. (2006) attribute this 

difference in findings to differences in sample, design choices and cost of capital proxy; 

more specifically, suggesting that by controlling for the effects of risk, Ogneva et al. 

remove part of the information quality effect on the cost of capital. 

4 This problem also applies to the studies cited in the previous paragraph. 

5 This bias also exists for the other event studies cited above. The bias in our study is larger 

because 22% of the firms in our sample reporting internal control weaknesses under Section 

404 also reported deficiencies under Section 302. 

6 These statements are supported by an Ernst & Young (2007) survey of executives showing: 

(1) 86 percent said they achieved a better understanding of major risk areas; (2) 89 percent 

reported improvement in their business processes and underlying control structures; and 

(3) more than 50 percent believed there were positive impacts on investor confidence. 

7 Examples of remediation plans are: hiring additional qualified personnel, replacing existing 

personnel, providing additional training, increasing IT security, implementing new software 

system and tax consolidation process, reducing the number of taxable entities and third 

party trust companies, adding additional procedures and reviews, expanding internal audit 

department, and engaging outside experts. 

8 It is possible that certain firm characteristics, including prior restatements of earnings, 

increase the market’s expectation of material weaknesses and thus reduce the market 

reaction at the time of the announcement of such weaknesses. We decided against using a 

market expectation model such as that used by Cheng et al. (2006), however, due to the 

measurement error in such a variable. 

32


9 We also use a continuous measure of cash flow, defined as free cash flow divided by sales 

averaged over the years 2003 and 2004. Results are practically the same as those reported in 

Table 5. 

10 Size is related to many variables/factors. For example, size is related to our measures of 

resource availability/adequacy (profitability), as well as to debt and complexity, as indicated 

in Table 2. 

11 Consistent with Cheng et al. (2006), we also use the natural log of (1 + the number of 

business segments) as a measure of complexity, but the results are practically identical to 

our main model as reported in Table 5, except size becomes important at .102 level. Using 

the effect of current year’s acquisitions on prior year’s sales (Compustat item # A249), 

rather than an acquisition code, slightly decreases the T-Values for GOV and REMED and 

increases that for growth in sales (GRO3). 

12 Not all the firms identified by Glass Lewis & Co. are used in this study, as the search 

through the Edgar database indicated that some of the delinquencies related to Section 302 

of SOX and some of the firms had confounding events during the event window. 

13 We made this exception as delinquent firms did not expect to complete their assessment of 

internal control in time without unreasonable expenses, which led many of them to believe 

and announce that the filing of their 10-K reports would also be delayed. Since the delay in 

assessing their internal control weaknesses was the cause of announcements regarding late 

reporting of 10-K, we considered any price adjustment (market reaction) for Group 1 firms 

to be due to late reporting on internal control and not to the delay in 10-K filing. 

Nevertheless, we found 26 firms that: (1) only announced delays in their 10-K filing 

between January 1, 2005 and May 1, 2005 (the delinquency announcement date range for 

most firms in Group 1); (2) had no confounding event around such announcements; (3) 

were not financial institutions or utility companies; and (4) on which CRSP data was 

available (this was the limiting factor). We compared the average three day abnormal return 

for these 26 firms with that of Group 1. The three day average abnormal return for these 26 

control firms, based on the Schipper and Thompson (1983) methodology, was –.142%, with 

a T-Value of –.34 (significant at .731), compared with the abnormal return of –.505% with a 

T-Value of –8.66 (significant at .000) for Group 1. This result suggests that the significantly 

negative abnormal return for Group 1 is related to delinquency in SOX 404 compliance and 

not to delay in 10-K filings. 

14 Financial institutions with assets greater than $150MM have been subject to internal control 

reporting under the Federal Deposit Insurance Corporation Act of 1991. Utility firms can be 

allowed more revenues by regulators to cover the cost of SOX 404 compliance. 

15 The auditor opinion on internal control could be (but does not have to be) integrated with 

the opinion on the financial statements in the 10-K report. Indeed, out of the 2,965 clean 

opinions on internal control, 2,234 were separate from opinions on financial statements; 

similarly, out of the 492 adverse opinions on internal control, 398 were separate from 

opinions on financial statements. 

16 CRSP data were not available in early 2005, when we started this research. 

17 Many of these variables are found in prior research on internal control reporting to be 

associated with the likelihood of firms reporting an internal control problem (e.g., Ge and 

33



May 23 and 24, 2012 

McVay 2005, Ashbaugh-Skaife et al. 2006 and 2008, Doyle et al. 2007a). The severity of 

the underlying weaknesses is not used as a variable because: (1) it adds subjectivity to the 

process; and (2) the underlying weaknesses are not (at least fully) known for delinquent 

firms, as their assessment of internal control has not been completed as of the delinquency 

announcement date. 

18 The use of the standard event methodology (running an OLS regression for each firm) in 

our study results in a positive, although insignificant, mean abnormal return for Group 2 and 

an insignificantly negative mean abnormal return for Group 3; T-Values are .613 and –.348 

respectively. [The OLS result for Group 1 is consistent with our MVRM, although the T- 

Value is much lower: –2.533 for OLS compared to –8.66 reported in Table 4 for Group 1.] 

The result for Group 2 is consistent with finding of no significant market reaction to 

reporting internal control weaknesses under Section 404 by Beneish et al. (2008) and shows 

that the standard event methodology is a less powerful test of market reaction when event 

dates are clustered. 

19 Ordinarily, we would use the Sefcik and Thompson (1986) methodology to account for the 

cross-correlation and cross-sectional heteroscedasticity as well as potential 

multicollinearity. In our case, however, the highest variance inflation factor was about 1.5, 

so multicollinearity was not severe and the two sets of results were not significantly 

different (please see endnote 21). Thus, we used a cross-sectional OLS regression. 

20 The negative (positive) market reaction for Groups 1 and 2 (Group 3) could not be 

explained by simultaneous release of bad (good) news due to criterion 4 of sample 

selection. However, the significant abnormal returns reported in Table 4 could have been 

driven by a few outliers. To rule out this possibility, the number of significant three-day 

abnormal returns for each of the three subsamples was determined. These numbers were all 

significant at the .01 level based on binomial tests, suggesting that results reported in Table 

4 are not driven by outliers. 

21 The Sefcik and Thompson (1986) methodology results in the same variables being 

significant, although the T-Values increase slightly for GOV and REMED; and decrease 

slightly for return on assets (ROA), prior disclosure (PDIS), and growth in sales (GRO3). 

This result shows that although the multicollinearity problem was not severe in our study, it 

did exist. 

22 Alternative specifications of debt constraints, cash flow, prior disclosure, and growth did 

not generally influence our findings. Debt constraint defined as long-term debt over total 

assets (LTDOA) slightly reduced the significance level for remediation (REMED) to .082 

based on a t-value of –1.758, but did not change the importance of the other variables. With 

cash flow defined as operating cash flow (OCF), the coefficients and t-statistics were almost 

identical. Growth defined as five-year growth in sales (GRO5) produced almost identical 

results except the t-statistics were slightly lower (e.g., –1.809 rather than –1.960 for 

remediation) so the significance levels were slightly higher (.074 rather than .053 for 

remediation). Finally, prior disclosure defined as disclosure of internal control weaknesses 

through other SEC filings or news releases (WARN) was not significant. 

34


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35



May 23 and 24, 2012 

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36


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38


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40


No material 

weaknesses 

Effective assessment of 

int ernal control, separate 

from F. S. reports * 

1. Meet 

expectations 

2. No 

excess returns 

3. No 

market reaction 

GROUP 3 

Compliant 

firms 

FIGURE 1: Section 404 Compliance 

Section 404 

Material 

weaknesses 

Adverse or 

disclaimer of opinion 

on internal cont rol, 

separate from F. S. 

reports * 

1. 

Uncertainty 

2. Risk 

premium 

GROUP 2 

* Delinquency announcement or internal control reporting date is required to be distinct from 

that of other SEC filings or news releases for all groups (except for announcements by 

delinquent firms regarding the late filing of their 10-K reports due to not having (fully) 

assessed the effectiveness of their internal controls) to ensure that the market reaction to 

internal control reporting is not confounded by other events. 

Table 1 

41 

Noncompliant firms 

Delinquency 

announcement, separate 

from F. S. reports * 

1. More 

uncertainty 

2. Higher 

risk premium 

3. More 

negative 

market 

reaction 

GROUP 1


Variable 


May 23 and 24, 2012 

List of Variables 

42 

Variable 

Abbrevia 

tion 

Expected 

Sign a 

Annual 

Compustat Data 

Items/Other 

Sources 

Size—natural log of market value of equity SIZE + 24*25 

Debt Constraint—debt over total asset 

(alternative) 

Total Debt over Total Assets 

Long-term Debt over Total Assets 

Cash Flow—one if cash flow is negative in 

2003 and/or 2004; zero otherwise 

Free Cash Flow—cash flow from 

operations minus cash 

dividends minus capital 

expenditures 

Cash Flow from Operations 

(alternative) 

Return on Assets—income before 

extraordinary items over total assets in 

percent 

Prior disclosure—one if company 

previously disclosed its internal control 

weaknesses; zero otherwise 

Pursuant to Section 302 of the 

Sarbanes-Oxley Act 

Through Other SEC Filings or 

News Releases (alternative) 

Remediation Plan—one if the company 

identified plans to remediate its internal 

control weaknesses prior to or at the time 

of control weakness or delinquency 

announcement; zero otherwise 

Prior Restatement of Earnings—one if the 

company has announced a restatement of 

its past earnings between January 1, 2002, 

and the internal control reporting or 

delinquency announcement date; zero 

otherwise 

TDOA 

LTDOA 

FCF 

OCF 

– 

– 

– 

– 

(9+ 34)/6 

9/6 

308 – 127 – 128 

308 

ROA + 100*(18/6) 

PDIS 

WARN 

+ 

+ 

Audit Analytics 

Edgar database 

REMED ? Edgar database 

RESTAT 

E 

– 

Audit Analytics 

and Edgar 

database


Growth—growth rate in sales 

Three-year least-squares growth 

rate in sales 

Five-year least-squares growth 

rate in sales (alternative) 

Complexity—one if the number of 

business segments for a firm is greater than 

the average for the whole sample; zero 

otherwise 

Acquisition Activity—one if engaged in 

acquisition activities during 2003 and/or 

2004; zero otherwise 

Corporate Governance—fraction of board 

members that are not officers 

43 

GRO3 

GRO5 

– 

– 

GSALE3 

GSALE5 

COMP – SEGNUM 

ACQ – AQSF 

GOV N/A 

Disclosure/Edgar 

database 

a A positive sign implies that greater values of a given variable are associated with less 

negative reaction to SOX 404 compliance. All variables are measured as of the end of 

fiscal year 2004, ending on or after November 15, 2004, when Section 404 

requirements went into effect unless otherwise indicated.

Variable 

SIZ 

E 

TABLE 2 

Pearson Correlations between Firm Characteristics for the Full Sample (N=150) a 

T DO 

A 

FCF 

RO 

A 

PDIS 

REM 

ED 

RESTA 

T E 

44 

GRO 

3 

Comp ACQ GOV 

SIZE 1.00 .228 c 

– 

.318 

.379 c –.058 –.099 –.009 .064 .317 c .077 .125 .206 d –.342 c –.058 .096 

T DOA 1.00 .090 .053 .074 .261 c .106 –.048 .262c –.052 .084 .964c –.043 .108 –.043 

FCF 1.00 –.40 c .069 .015 .025 .094 –.104 –.089 –.048 .094 .608 c .115 .011 

ROA 1.00 –.25 d –.024 –.118 –.078 .162 d .101 .080 .078 –.496 c –.041 –.064 

PDIS 1.00 .235 d –.001 .119 –.010 –.009 –.036 .100 .147 .224 d .118 

REMED 1.00 .092 –.121 .256 d –.014 .148 .297 c .002 .489 c –.102 

RESTAT 

E 

LT DO 

A b 

OCF b 

WAR 

N b 

GRO5 

b 

1.00 –.063 .054 –.069 –.065 .028 .036 –.016 .017 

GRO3 1.00 –.125 .097 .043 –.044 .156 –.193 .539 c 

COMP 1.00 .028 .113 .233 c –.158 .074 –.121 

ACQ 1.00 .106 –.049 –.065 .030 .048 

GOV 1.00 .093 –.018 .052 –.095 

LT DOA b 1.00 –.096 .172 –.040 

OCF b 

1.00 .030 .047 

WARN b 1.00 –.138 

GRO5 b 1.00 

a Correlations for PDIS, WARN, and REMED are based on 100 observations, as these variables are not applicable to Group 3 (firms with no material 

weaknesses in t heir internal control). All variables, including PDIS, WARN, and REMED are defined in T able 1. 

b Alternative measures. 

c Significant at .01 level (2-tailed). 

d Significant at .05 level (2-tailed).

TABLE 3 

Mean, Median, and Standard Deviation (Std. Dev.) of Selected Variables for the Full Sample and Subsamples 

Sample a Variable b SIZE T DOA FCF ROA PDIS REMED REST AT E GRO3 COMP ACQ GOV 

Full Sample 

(N=150; 100 for PDIS & 

REMED) 

Group 1, 

Delinquent Firms 

(N=50) 

Group 2, 

Firms with Ineffective 

Controls (N=50) 

Mean 6.214 .183 .560 –3.473 .230 .570 .453 18.492 .427 .160 .746 

Median 5.925 .130 1.000 1.268 .000 1.000 .000 7.131 .000 .000 .778 

Std. Dev. 1.595 .212 .498 17.828 .423 .498 .499 60.821 .496 .368 .125 

Mean 6.110 .214 .620 –7.180 .240 .660 .720 19.081 .440 .120 .712 

Median 5.821 .190 1.000 –1.155 .000 1.000 1.000 7.047 .000 .000 .721 

Std. Dev. 1.491 .213 .490 18.758 .431 .479 .454 40.964 .501 .328 .138 

Mean 5.777 .126 .560 –4.896 .220 .480 .500 4.489 .360 .240 .751 

Median 5.553 .065 1.000 .921 .000 .000 .500 5.810 .000 .000 .778 

Std. Dev. 1.444 .153 .501 18.445 .418 .505 .505 18.187 .485 .431 .115 

Mean 

Group 3, 

Firms with Effective Controls Median 

6.755 

6.430 

.208 

.157 

.500 

.500 

1.657 

5.923 

N/A 

N/A 

N/A 

N/A 

.140 

.000 

31.906 

9.960 

.480 

.000 

.120 

.000 

.775 

.800 

(N=50) Std. Dev. 1.710 .251 .505 15.213 N/A N/A .351 94.105 .505 .328 .115 

a T he full sample is composed of all the firms in the sample. Other samples are a subset of these 150 firms. Specifically, Group 1 firms are firms whose 

management or auditor did not report on internal control in a timely manner; Group 2 firms are t hose who received adverse or disclaimer opinions on the 

effectiveness of their internal control due to identified material weaknesses or scope limitation; and Group 3 consists of firms with no material weaknesses in 

their internal control, whose auditors’ opinions on internal control effectiveness were unqualified. 

b Variables are defined in Table 1. PDIS and REMED are not applicable to Group 3. 

45



May 23 and 24, 2012 

TABLE 4 

Test of Hypotheses 1–3 

Portfolio abnormal returns (in %), T-Values and their significance level, for the 

three subsamples. a These estimates are the coefficients of the dummy variables in a 

regression of portfolio returns (weighted based on the full estimated covariance 

matrix of residuals) on the market return and a dummy variable corresponding to 

SOX 404 compliance (represented by Equation (3)). The dummy variable equals one 

during the three-day period (t=–1, t=0, and t=1) relative to the internal control 

reporting date or the delinquency announcement date, and zero otherwise. 

Group 1, 

Delinquent 

Firms 

(N = 50) 

Group 2, 

Firms with 

Ineffective Controls 

(N= 50) 

46 

Group 3, 

Firms with 

Effective Controls 

(N= 50) 

Abnormal 

Return –.505 –.171 .284 

T-Value –8.66 

–3.62 2.39 

Significance 

Level .000 .000 .018 

a Groups 1–3 are a subset of the 150 firms in the sample. Specifically, Group 1 

firms are firms whose management or auditor did not report on internal control in a 

timely manner; Group 2 firms are those who received adverse or disclaimer opinions 

on the effectiveness of their internal control due to identified material weaknesses or 

scope limitation; and Group 3 consists of firms with no material weaknesses in their 

internal control, whose auditors’ opinions on internal control effectiveness are 

unqualified.


TABLE 5 

Test of Hypotheses 4–9 

Estimated coefficients of independent variables, their T-Values, and significance levels, in an 

OLS regression a of abnormal returns % on firm characteristics. The coefficients estimate the 

change in abnormal return % as a result of a unit change in a given variable. Abnormal return 

for each firm (the dependent variable in this regression) is the dummy variable coefficient in 

an OLS regression of security return for that firm on the market return and a dummy variable 

corresponding to SOX 404 compliance (an OLS regression similar to Equation (1) for that 

firm). The dummy variable equals one during the three-day period (t=–1, t=0, and t=1) 

relative to the internal control reporting date or the delinquency announcement date, and zero 

otherwise. 

Variable Expected 

Sign 

Coefficient T-Values Significance 

Level 

Constant –1.156 –.553 .581 

Size (SIZE) + –.350 –1.573 .119 

Debt Constraint (TDOA) – –.123 –.078 .938 

Cash Flow (FCF) – –.153 –.258 .797 

Return on Assets (ROA) + .067 4.007 .000 

Prior disclosure (PDIS) + 2.082 3.083 .003 

Remediation (REMED) ? –1.198 –1.960 .053 

Restatement (RESTATE) – .534 .914 .363 

Growth (GRO3) – –.014 –1.680 .096 

Complexity (COM P) – .648 1.072 .287 

Acquisition (ACQ) – –.889 –1.217 .227 

Corporate Governance 

N/A 4.310 2.003 .048 

(GOV) 

a The regression is based on the 100 firms in Groups 1 and 2 only. Group 1 firms are 

delinquent firms (firms whose management or auditor did not report on internal control in a 

timely manner); and Group 2 firms are those who received adverse or disclaimer opinions on 

the effectiveness of their internal control due to identified material weaknesses or scope 

limitation. Variables are defined in Table 1. Significant T-Values are in bold. 

47



May 23 and 24, 2012 

Earnings Management in Delisted Companies: Case 

of Iran 

Abstract 

Jamal Roudaki � 

Accounting PhD 

Faculty of Commerce, Lincoln University, New Zealand 

Over three years of 2007 to 2009 one hundred eight companies 

delisted from the Iranian financial market which forms about one third of 

total companies continuously listed in the same period. This paper 

investigates earnings management (EM) statue in relation to firm 

characteristics such as leverage, size and ROA in a sample of these 

companies. Voluntary disclosure about state of corporate governance is 

also included in the analysis. Results from financial reports of one and two 

years before delisting indicate that managers are extensively managing 

their earnings. Further inquiry reveals that while numbers of delisted 

companies that manage their earnings upward or downward are equal, in 

companies with negative EM profitability exhibit strong statistical relation 

with DACC (proxy for EM). Inquiries about corporate governance 

indicate that five directors (board size) on board of directors is common 

(68 from 73 firms reported 5 members). The mean number of independent 

board members is more than 3 directors, and institutional ownership is 

widespread that 79.5% of firms reported more than 50% of such shares. 

Keyword: Iran earnings management, delisted companies, 

distressed companies, firm characteristics 

*Corresponding Author: Jamal.Roudaki@lincoln.ac.nz


Introduction 

Earnings management in companies at distressed companies are more 

likely. A recent study about the Iranian delisted companies by Etemadi, 

Dastgir, Momeni and Farajzadeh Dekhordi (2012) reveals that discretionary 

accruals manipulation are at the highest a year before filing bankruptcy 

among the last the five years prior to such incident. Such firms negatively 

manage their earnings before delisted as a result of unprofitable period. 

However, as Ahmad (2005) suggested empirical evidence about financial 

information including those related to earnings management of distressed 

companies are limited. Such information either removed from data bases as 

the company no longer listed or active, or the financial information are never 

published to avoid any formal or informal reaction. In the case of Iranian 

delisted companies from 108 companies that delisted during three years of 

2007-9 only 77 financial reports are available from the related website. 

These financial reports are not complete and include minimal financial 

information that satisfies needs of the current research. 

Unlike early studies (i.e. DeFond & Jiambalvo, 1994; Dechow et al., 

1995; Dechow, Sloan, & Sweeney, 1996) that consider only total accruals in 

investigating earnings management, recent studies (i.e. Grove & Basilico, 

2008; Rosner, 2003) consider financial indices and discretionary accruals to 

explain state of earnings management. Therefore this study includes 

discretionary accruals and financial indices to develop earnings management 

literature in this area. This study designed to use limited financial 

information to investigate earnings management in the Iranian delisted 

companies one year before such incident. The motivation of this study is 

based on fact that managers manage earnings before major event such as 

delisting. Beside earnings management information as financial variables, 

corporate governance data brings about non-financial aspect to this study. 

The other objective of this paper is to investigate the status of corporate 

governance in delisted companies over two years end up with delisted. 

Voluntary provided corporate governance data from published financial 

reports of delisted firms are the most reliable source for this study in the 

absence of comprehensive corporate governance guidelines. This paper 

intended to develop the literature of earnings management and corporate 

governance in developing countries. The result will benefit regulators and 

49



May 23 and 24, 2012 

stock market in development of regulation related to listing requirements and 

corporate governance guidelines. 

The paper proceeds as follows. Section two explains background 

of the study that will formed foundation of hypothesis developed in 

section three. Section four then provides research methodology while 

results of empirical data analysis are presented in section five. 

Conclusions and remarks are included in the last section of the paper. 

Background of the Study 

Discretionary accruals of Iranian listed firms investigated by 

Etamadi, et al. (2012). This study, for the first time compares 

distressed and healthy companies and concludes that during the 

distressed period or filling bankruptcy, such companies manage their 

earnings more than their healthy counterparts while the highest 

downward discretionary accruals reported at the year before delisted. 

Mashayekhi (2008) research examines the corporate governance and 

earnings management in the Iranian listed companies from 2003-2005. 

She provides evidence about relationship between corporate 

governance characteristics (board size, independence, number of 

meeting, financial expertise, and ownership) with earning 

management. She also considers two other characteristics of CEO 

duality, independent audit committee in her research. In the same area 

of relationship of corporate governance characteristics and earnings 

management, Hashemi and Rabiee (2011) investigate role of corporate 

governance and real earnings management in Iran. They use data from 

2004 to 2010 of 1398 firm year of listed companies. The same the 

previous research, board size and board independence are independent 

variable with dependent variable of real earnings management in 

Hashemi and Rabiee (2011) study. They found that aforementioned 

board characteristics are contributed to limit real earning management. 

Another research by Rahnamy Roodposhti and Nabavi Chasmi (2010) 

is a survey of effect of corporate governance characteristics on earning 

50


management in 196 Iranian listed firms for the period of 2004-08. 

They reported a negative association between earning management 

with ownership concentration and CEO duality (CEO as chairman of 

the board) while they observed a positive relationship between earning 

management and control variables of firm specification (i.e. leverage 

and size). Board composition, CEO duality, size, and independence in 

relationship with earning management was centre of focus in a 

research study by Badavar Nahandi, Mahmoudizadeh Baghbani and 

Bolour (2011). Their findings are opposite to previous research that 

they found a positive relation between earning management and CEO 

act as board chairman. This research states the relationship between 

earning management and board independence is not significant. 

However, the results of these studies should be interpreted consider 

that in the absence of formal corporate governance rules in the Iranian 

capital market, any information disclosed tend to be voluntary. It is 

worth to mention that corporate governance information are not 

normally reported fully by majority of companies while it is voluntary 

based. Availability of such information is a challenge not only for 

aforementioned research studies but for all research projects 

considering Iranian financial market information including the present 

research. 

Hypothesis Development 

Research studies about earnings management in distressed 

companies are limited since empirical evidences are not widely 

available (Ahmed, 2005). Literature in this area is primarily used data 

from developed countries stock markets mostly from the USA. 

Charitou, lambertides and Trigeogis (2011) study earning quality of 

companies with different financial health and growth characteristics in 

15,049 US companies over ten years end by 2004. They found that 

financially distressed companies exhibited low level of earnings 

timeliness when facing bad news and vice versa for good news while 

51



May 23 and 24, 2012 

healthy companies behave opposite. Lee (2000) investigates distressed 

companies behaviour in three stages of: 1) reducing dividend, 2) debt 

contract violation, and 3) distressed debt rearrangement. They 

conclude that in the first stage (reducing dividend) companies tend to 

decrease reported discretionary accruals either to control further drop 

in financial performance or window dressing by removing negative 

items from financial reports. Successful companies in the debt 

contract violation stage exercise decreasing discretionary accruals 

while unsuccessful companies behave opposite. As companies 

experience trouble debt restructuring (stage 3) they highlight the 

financial hardship by decreasing discretionary actuals to position 

company in upper hand in debt negotiation. Lee’s (2000) study 

suggests that distressed firms’ behaviour towards manipulating 

discretionary accruals is depend on the managers expectation on debt 

contracts. In the same area Ahmed (2005) investigates reported 

discretionary accruals of Malaysian distressed companies in the period 

of debt renegotiation. He concludes that such companies manipulate 

their earnings downwards. Koch (2003) study explains bias and 

credibility when forecasting voluntary earnings and concludes that 

distressed companies have greater intention for such behaviour. His 

empirical results support that distressed firms unlike healthy 

counterparts manipulate earnings upward and consequently financial 

analyst attach less credit to the quality of earnings included in such 

financial reports. Nevertheless, Adu-Boateng (2011) concludes that 

accountants’ choice unethical behaviour related to reporting earnings 

management is attributed to situation factors and moral reasoning. He 

shows accountants in financial distressed firms are more vulnerable to 

unethical behaviour in earning management. A survival analysis 

examination of financial distressed firms performed by Hou and 

Chuang (2007), they consider data from 1996 to 2006 of 4,356 

Taiwanese listed companies. Their empirical results indicate that firms 

that manage their earnings, demonstrate lower profitability are more 

52


subject to became financially distressed. As explained before the only 

research study in the literature about Iranian distressed companies is 

Etemadi et al. (2012) paper. They find that during distressed period (5 

years before filing bankruptcy) firms manage their earnings more than 

non-distressed counterparts. This research reports that adverse 

discretionary accruals observed in the highest point at the year before 

bankruptcy announcement. 

Literature related to Earnings management is well developed 

around the world. Motivations for studies in this area are ranged from 

capital market, and contractual, to regulatory motivations (Healy and 

Wahlen, 1999). In these studies total discretionary accruals is 

considered as proxy for earnings management and consider as a 

dependent variable. This research follows the same approach but 

population of the study is delisted companies of the Tehran Stock 

Exchange. Therefore the first null-hypothesis is: 

H1: There is no earnings management in companies delisted from 

TSE board. 

Relationship between firm characteristics of size, return on assets 

(ROA) and leverage are focus of interest of many research studies (for 

example; Jones and Sharma, 2001; Dechow and Dichev, 2002; Bédard 

et al., 2004; Jaggi and Leung, 2007). From the time that Watts and 

Zimmerman (1986) hypothesized that, managers of large companies 

more likely to manage their earnings to the recent years, there are 

many research studies that report contradictory results. Some scholars 

like Gu, Lee, and Rosett (2005), Benkel et al. (2006), and Dechow and 

Dichev (2002),and Jordon et al (2008) conclude that managers of 

small firms manage their earnings while other such as Warfield et al. 

(1995) provide empirical evidence to agree with Watts and 

Zimmerman (1986). However in the recent studies a statistically 

significant relationship between firm size (total or log of assets as 

proxy) and earning management is reported by Davidson et al. (2005), 

53



May 23 and 24, 2012 

Ebrahim (2007), Jaggi and Leung (2007), Niu (2006), and Park and 

Shin, (2004). Even lack of sufficient evidence to explain the 

relationship between firm size and earnings management is reported 

by some other scholars (for example; Bédard et al.; 2004 & Klien, 

2002). 

It is reported that when a firm exercises low profitability (ROA or 

ROE as proxy), manipulation of earnings tend to occur to cover 

negative results (Burgstahler & Dichev, 1997). Jordon et al. (2008) 

provide a contrary result that firm with negative earnings avoid 

earnings management, earnings manipulation occurs in firms with 

positive earnings. However, a positive relationship is reported by 

Klien (2002) and Davidson et al. (2005). A negative relationship 

between profitability and earnings management is presented by 

comparison of the companies by some scholars (i.e. Ashari, Koh, Tan, 

& Wong, 1994, Bédard et al., 2004, and Sun & Rath, 2009). In this 

study profitability is considered as return on assets (ROA). 

Leverage is another firm characteristic that draw attention of 

scholars studying relation of earnings management. High leverage 

firms are considered to have more intensive to manage their earnings 

(Ahorany et al., 1993; Defond & Jimbalvo, 1994; Glaum, Lichtblau & 

Linderman, 2004; Klein, 2002; Davidson et al., 2005; Jaggi and 

Leung, 2007). One the other hand in the firm with high ratio of debt to 

total assets chance of manipulating earnings is lower than high 

leveraged firm as explained by Park and Shin (2004), Chen, Lin and 

Zhou (2005), and Niu (2006). A contradict results are reported by 

Jordon et al. (2008). Like other firm characteristics scholars such as 

Sun and Rath (2009), Abdul Rahman, Dowds and Cahan (2005), 

Bédard et al. (2004), and Jones and Sharma (2001) said that they did 

not find an statistical significant relationship between manage 

earnings and leverage of the companies under investigation. 

Considering and combining the above contradict results about the 

54


effect of firm size, profitability, and leverage, this study hypothesis 

that: 

H2: There is no relationship between earning management and firm 

characteristics of size, profitability, and leverage of the companies 

delisted from TSE board. 

Methodology 

The objective of this study is to determine the extent of earnings 

management of delisted companies in from Tehran Stock Exchange in 

2007 and 2008. To persuade this objective among four earnings 

management models, Performance Matched Model that developed by 

Kothari Leano and Wasley (2005) considered appropriate for the 

propose of the study. This earnings management model is 

contemporary and comprehensive model that emphasises on firm 

performance. Khatori, et al. (2005) found that discretionary accruals 

estimated in their model is more powerful and inferior to early 

earnings management models (i.e. Jones and Modified Jones Models), 

because discretionary accruals are adjusted for a performance matched 

discretionary accruals ( i.e. return on assets) and possibly avoid a large 

estimated discretionary accruals whenever a firm experiences extreme 

growth. Therefore, non-discretionary accruals is calculated using the 

below model. 

NDACit/TAit-1 = α0 + αi[1/TAit-1] + β1[(∆REVit - ∆RECit)/TAit-1] + 

β2PPEit/TAit-1 + β3ROAit-1 

In the above model, year t is the one year before (2008 or 2007 

where applicable) the firm be delisted and t-1 is two years before of 

the firm be delisted. NDACit is stand for non-discretionary accruals at 

year t while TAit-1 is total assets of two year before of delisting. In the 

same way ∆REV and ∆REC are respectively changes in revenue and 

account receivables from one year to two years before that firm be 

delisted. In the same way PPE and ROA are property, plant and 

55



May 23 and 24, 2012 

equipment and return on assets of the firm. Total Accruals for each 

company calculated as difference of net income before tax and cash 

flow from operating activities. Intercept and coefficient of α and β are 

estimated cross-sectional for one year before delisting using the above 

OLS regression model and plugged into the model to estimate 

unexpected accruals of each delisted firm considering one year and 

two years before delisting. Therefore discretionary accrual (DACC) is 

calculated as differences between total accruals (TACC) divided by 

total assets (TA) and non-discretionary accruals (NADC) in the 

following formula. 

DACCit = (TACCit/TAit-1) – NDACit 

In this study three firm characteristics of size, profitability, and 

leverage are used as independent variables. Following the related 

literature, total assets and more precisely log of total assets is used as 

proxy of size while return on asset (ROA) is used for profitability 

variable. There are many approaches in calculating leverage; the most 

popular is the product of debt to equity which is considered relevant in 

this study. 

Population of the Study and Data Collection 

All delisted companies during 2007 and 2008 (1386 and 1387 

Iranian calendar) are population of this study. Therefore data for the 

study are collected from financial statements of one year and two 

years before (2006 and 2005 where applicable) the year that company 

is delisted. Out of 108 companies that delisted in these two years; 60 

and 13 companies are delisted in 2007 and 2008 respectively while 35 

did not provide sufficient information or no financial report for the 

year under consideration. Table 1 presents detailed information about 

the population of the study. Financial reports of listed companies 

56


downloaded from Research Development Islamic Studies database 

which is affiliated to Iranian Islamic Capital Market. 

Table 1: Delisted Companies from Tehran Stock Exchange, 2007-8 

2008/1387 2007/1386 Total 

Usable 13 60 73 

Per cent 17.8 82.2 100 

Information not provided or not complete 35 

Total 108 

Table 2 shows number of years that companies were listed before 

delisted from the main board of Tehran Stock Exchange. A 

considerable number of reputable companies are delisted in 2007 and 

2008 as 58.9% of such companies have been listed with at least 11 and 

maximum of 20 years listing history while sixteen companies 

(21.92%) even have more than 21 years listing status. According to 

second section of table 2 minimum and maximum years of listed 

status for these companies are 5 and 36 years respectively with mean 

of 17.3 years. Positive while close to one skewness figure indicates 

that numbers of years of listed status for delisted companies are 

skewed to the less than average. As it is appear in table 2 there are 

three patterns in the number of years that these companies are listed 

before delisted. The first pattern is 5 to 18 years listing status that 

includes about 52 companies. The second pattern is 19 to 30 years 

(about 11 years) listing status that there is no company fall into this 

category. The last pattern includes 21 companies with 31 to 35 years 

listing status. Considering that Tehran Stock Exchange have been 

inactive from 1979 to early 1990’s (about 12 years) explaining the 

reason for no observation in the second pattern. In the results 

discussion we will see how these companies manage their earnings to 

cover profitability and inefficiency to avoid being delisted. 

57



May 23 and 24, 2012 

Table 2: Reputation of delisted companies 2007-8 

Years of listing Number Percentage 

Less than 10 years 14 19.18 

11-20 Years 43 58.90 

21-30 years 0 0.00 

More Than 31 16 21.92 

Total 73 100 

Min Max Mean STD Variance Skewness 

58 

Statistic Std. Error 

5.00 36.00 17.3 8.99951 80.991 .964 .281 

Results 

Information about corporate governance is not provided in the 

financial reports of the delisted companies. Since corporate 

governance regulations or rules are not yet developed officially in the 

Iranian financial market thus companies are not required to address 

such firm characteristics in their published financial reports, all they 

report is voluntary in nature. Disclosure about board size of board of 

directors, CEO duality, number of independent directors and 

institutional ownership are extractable from published financial 

statement of delisted companies. 

Table 3 summarises descriptive statistics about board of directors’ 

size. In terms of Board size, five board members model is dominated 

as 68 out of 73 companies formed their boards having this number of 

directors on board. Only two companies have three or four members 

and three companies have three directors on their boards. Common 

number of 5 members on board is not encouraging further 

investigation about relationship of board size and earnings 

management. 

Table 3: Number of directors on board of directors


Board Size 3 and 4 5 6 and 7 Total 

Number of firm 2 68 3 73 

Min Max Mode Mean STD 

Descriptive statistics 3 7 5 5.026 0.486 

Duality is not the case in delisted companies, since there is no 

CEO act as chairman of board of directors. However, in 55 companies 

CEO is member of the Board. Previous research studies (i.e. Badavar 

Nahandi, Mahmoudizadeh Baghbani & Bolour, 2011; Rahnamy 

Roodposhti & Nabavi Chasmi, 2010b; Mashayekhi, 2008) investigate 

the relationship between CEO duality and earning management in the 

Iranian listed companies; however the results as explained in the 

literature review are contradictory. This research found no 

researchable data in the delisted companies since none of CEOs in 

these companies acting as chairman of board. 

Table 4 presents descriptive statistics about independent members 

of board of directors. As it was expected voluntary information about 

board independent is not reported by all companies in published 

financial reports, 59 companies choose to be silent and among other 

14 companies nine companies reported that they have four 

independent board members while others have one or two such 

members. Other research (i.e. Mashayekhi, 2008; Hashemi and 

Rabiee, 2011b; Rahnamy Roodposhti & Nabavi Chasmi, 2010 and 

201; Badavar Nahandi et al., 2011) that use recent data from Iranian 

financial market reported the same results. Mashayekhi (2008) reports 

proportion of board independence as 0.2, 0.5 as minimum and 

maximum respectively. However, lack of sufficient number of 

companies that provide information about independent directors is a 

challenge for earning management research studies. 

59



May 23 and 24, 2012 

Table 4: Number of independent board of directors’ members 

Board independent 1 to 3 4 and 5 Silent Total 

Number of firm 4 10 59 73 


Descriptive statistics 1 5 4 3.533 1.06 

Institutional ownership is important when investigating earnings 

management. Table 5 presents structure of institutional ownership of 

delisted companies. The descriptive statistics in table 5 are for one 

year and two years before the company being delisted. Mean of 

institutional ownership of all delisted companies is 73.43 per cent for 

one year before delisted. More than 51% of shares of 58 companies 

are in the hands of other companies or institutions and only 15 

companies reported less than 50% institutional ownership in one year 

before delisted. Considering the first two columns of Table 5 reveal 

that institutional ownership is about 70% (43.84 + 26.03) of 

companies reported more than 70% institutional ownership. These 

data should be read considering minimum (zero) maximum (100) and 

mode (100) with high standard deviation. Heavy intuitional ownership 

that is appear in table 5 hamper further regression analysis of 

relationship between institutional ownership, DACC, and firm 

characteristics. 

As it was anticipated in the voluntary environment of Iran stock 

market, disclosure about board expertise and remuneration are not 

included in the published financial reports of most of delisted 

companies that the data is not valid to be reported. 

60


Institutional 

Ownership 

Table 5: Institutional Ownership of delisted companies 

Over 

90.1% 

Between 

70.1%- 

90.9% 

61 

Between 

50.1%- 

79.9% 

Sub 

Total 

Below 

50% 

Total 

Number of firm 32 19 07 58 15 73 

Percentage 43.84 26.03 09.59 79.46 20.55 100 

Descriptive 

Statistics 


One year before 0 100 100 73.43 30.94 

delisted 

2 Years before 0 100 100 72.92 31.24 

delisted 

In terms of earning management variables, Table 6 presents results 

of descriptive analysis of DACC and firm characteristics. According 

to this table, while mean of DACC is slightly above one million Rials 

(0.1099 million Iran currency) the minimum and maximum of this 

amount are -2.39 and 4.11 million Rials with positive skweness. 

Further investigation reveals that 36 companies have positive and 37 

reported negative DACC, implies that numbers of companies 

manipulating their earnings upward or downward are almost the same. 

Minimum and maximum of leverage (total debt over equity) are 0.51 

and 5.24 with 1.4909 mean and positive skewness. In addition the 

minimum and maximum of ROAs of them are -2.35 and 0.37 with 

mean of -0.1617 and negative skewness. These results support the 

argument that these companies while manipulating their earning are 

suffering from low profitability. Considering mean and maximum of 

leverage of these companies implies that these companies are highly 

leverage and excessively involved in borrowing that may not able to 

repay while their profit abilities are too low, such condition justify the 

delisting status consequently presence of earning management 

motivation.



May 23 and 24, 2012 

Table 6: Descriptive statistics of DACC and firm characteristics of 

delisted companies 

Firm 

Characteristics 

Min Max Mean STD Skewness 

DACC -2.39 4.11 0.1099 .80585 2.674 

Leverage 0.51 5.24 1.4909 .98212 1.886 

ROA -2.35 0.37 -0.1617 .36813 -3.225 

Log of Total Assets 3.20 6.43 5.0867 .58790 0.034 

Table 7 presents regression analysis between dependent variable of 

DACC of one year before delisting and predictors of as included in the 

study OLS. In this table, R square is 0.332 and Durbin-Watson 1.806. 

Using rule of thumb Durbin-Watson around two is an indication of 

independence observation and absent of autocorrelation in the 

statistical test (Garson, 2007). The immediate conclusion is that 

delisted companies ménage their earning in the year before delisting. 

However the results of statistical analysis related to null hypothesis 

one will be explained with more detail the following paragraphs. 

Table 7: Regression analysis delisted companies one year before 

delisted (Model Summary b ) 

Adjusted 

R 

Square 

Std. Error 

of the 

Estimate 

R 

Square 

Change 

62 

Change Statistics 

R R 

F 

Sig. F Durbin- 

Square 

Change df1 df2 Change Watson 

.576 a .332 .285 .68928 .332 7.058 4 68 .000 1.806 

a. Predictors: (Constant), Dummy IOS One year before, PPE(1St year)/TA(2St year), 

1/TA(2St year), ROA(2St year), [(REV(1St year) – REV(2St year)) – (REC(1St year) – 

REC(2St year))]/ TA(2St year) 

b. Dependent Variable: DACC one year before 

Table 8 presents results of regression analysis in two lines. In the 

first line figures are related to relationship of DACC with firm 

characteristics of one year before delisting while line two is related to 

regression analysis of TACC with the same firm characteristics. The


first line of Table 8 shows a weak R square indicating that 

discretionary accruals of one year before delisting have a weak 

relationship (R 2 0.191) with leverage, profitability and size of the 

companies. The second line of this table shows stronger R square 

(0.457) between Total accruals and firm characteristics. These 

relationships will be investigated with correlation in the following 

paragraphs. TACC shows strong R square with firm characteristics 

which indicates that total accruals. 

Table 8: Regression analysis one year before delisted (Model 

Summary b ) 


R Adjusted Std. Error of 

R 

Square F 

Sig. F Durbin- 

R Square R Square the Estimate Change Change df1 df2 Change Watson 

.437 b .191 .156 .74046 .191 5.426 3 69 .002 1.998 

.676 a .457 .434 90487.12202 .457 19.368 3 69 .000 2.282 

a. Predictors: (Constant), Log TA, Leverage, ROA 

b. Dependent Variable: DACC or TACC 

Statistical relationship of DACC one year before delisting and 

ROA (0.309 at 0.01 level, 2-tailed) of the same year is significant 

while this relation is not existed with firm size (log of total assets as 

proxy). DACC has adverse significant relationship with leverage (- 

0.24 at 0.05 level 2-tailed), implies that highly leverage companies 

have less discretionary accruals consequently low level of managing 

their earnings. In the contrary more profitable companies manage their 

earnings may in the hop not to be delisted or influence stakeholders. 

Table 9 shows the results of correlation analysis of DACC and firm 

characteristics of one year before delisting. Null hypothesis two could 

be divided into three sub hypotheses of relation between DACC in one 

hand and three firm characteristics of 1) leverage, 2) profitability, and 

3) size in the other hand. Based on results in Table 9 first and third sub 

hypotheses cannot be accepted while the second one is verifiable. Not 

63



May 23 and 24, 2012 

surprising, adverse significant relationship between profitability and 

leverage (-0.514 at 0.01 level, 2-tailed) indicate that in these more 

profitable companies are less leverage. As an additional to this 

finding, 0.222 correlation between log of total assets and profitability 

indicates that big companies are relatively more profitable 

consequently more vulnerable to earning management manipulation 

which support above discussion about relationship between manage 

earnings DACC, profitability and size. The results of this study 

confirm Rahnamy Roodposhti & Nabavi Chasmi, 2010 results in 

terms of association between firm size and leverage with earnings 

management. 

Table 9: Correlation analysis of DACC and firm Characteristics one 

year before delisting 

DACC Leverage ROA Log TA 

DACC Pearson Correlation 1 -.240* .399 ** -.086 

Sig. (2-tailed) .041 .000 .470 

Leverage Pearson Correlation 1 .514 ** .216 

Sig. (2-tailed) .000 .066 

ROA Pearson Correlation 1 .222 

Sig. (2-tailed) .059 

Log TA Pearson Correlation 1 

Sig. (2-tailed) 

**. Correlation is significant at the 0.01 level (2-tailed). 

*. Correlation is significant at the 0.05 level (2-tailed). 

Further investigating performed by dividing companies in two 

categories of 36 positive and 37 negative DACCs. Strong R square for 

companies with positive DACCs indicates companies aggressively 

increase their earnings. R square for these companies is higher than all 

companies presented at table 10 that explain high correlation for all 

companies is related to companies with positive DACCS that 

aggressively manage their earnings. In the contrary, companies with 

negative DACCs exhibit low R square (0.035) and low Durbin- 

64


Watson (0.186) that indicates these companies are not manage their 

earnings and their counterparts. Table 10 shows regression analysis 

results for companies with positive and negative DACC in the year 

before being delisted. 

Table 10: Regression analysis delisted companies with positive or 

negative DACCs 

DAC 

C R 

Positi .71 

ve 6 a 

Negati .52 

ve 3 a 

Adjust 

ed R 

Squar 

e 

Std. 

Error 

of the 

Estim 

ate 

R 

Squa 

re 

Chan 

ge 

65 


Durbi 

n- 

Wats 

on 

R 

F Sig. F 

Squa 

Chan df df Chan 

re 

ge 1 2 ge 

.512 .431 .67907 .512 6.305 5 30 .000 1.567 

.274 .157 .41460 .274 2.340 5 31 .065 .887 

a. Predictors: (Constant), Dummy IOS One year before, PPE(1St year)/TA(2St year), 

1/TA(2St year), ROA(2St year), [(REV(1St year) – REV(2St year)) – (REC(1St year) – 

REC(2St year))]/ TA(2St year) 

b. Dependent Variable: DACC(Pos) or Dependent Variable: DACC(Neg) 

Table 11 presents correlation analysis of firm characteristics and 

DACC of delisted companies with positive and negative DACCs 

separately. According to this table DACC in companies with positive 

DACC is not correlated with firm characteristics (leverage, ROA, and 

Size). In the line of all companies in these companies ROA and 

Leverage are highly correlated while relationship of ROA and size is 

weak. In the contrary companies with negative DACCs exhibited a 

strong adverse correlation between DACC and ROA (-0.718 at 0.01 

level, 2-tailed). These results indicate that as profitability decreases 

the possibility of earnings management decreases. Size and leverage 

have not statistical relationship with DACC in companies with 

negative DACCs. Nevertheless, in companies with negative DACCs 

profitability is a good predictor for DACCs but not for their 

counterpart companies.



May 23 and 24, 2012 

The same significant adverse relationship between profitability and 

leverage repeated for companies with negative DACC (-0.377 at 0.05, 

2-tailed). In these companies size and leverage are not correlated. 

Table 11: Correlation analysis of firm characteristics and DACC 

(positive and negative) 

Companies with positive DACC 

DACCs 

(Pos) Leverage ROA Log TA 

DACC Pearson Correlation 1 -.181 -.009 -.258 

(Pos) Sig. (2-tailed) .290 .958 .128 

Leverage Pearson Correlation 1 -.651 ** -.157 

Sig. (2-tailed) .000 .361 

ROA Pearson Correlation 1 .328 


Log TA Pearson Correlation 


1 

Companies with negative DACC 

DACCs 

(Neg) Leverage ROA Log TA 

DACC Pearson Correlation 1 .071 -.718 

(Neg) 

** .005 

Sig. (2-tailed) .678 .000 .978 

Leverage Pearson Correlation 1 -.377 * .224 

Sig. (2-tailed) .021 .183 

ROA Pearson Correlation 1 -.135 


Log TA Pearson Correlation 1 


**. Correlation is significant at the 0.01 level (2-tailed). 

*. Correlation is significant at the 0.05 level (2-tailed). 

While previous literature is dominated by studies consider accruals 

as the dependent variable explaining earnings management, recent 

studies such as Grove and Basilico (2008) and Rosner (2003) focused 

on financial ratios for investigating such statistical relationship. 

Therefore, three major earnings management indices of Days’ sales in 

receivables, sales growth, and leverage are calculated to verify above 

results. These indices basically are showing annual changes in relation 

66


with earnings management. Table 12 presents descriptive analysis of 

findings in relation to these three indices. Days’ sales in receivables 

index (DSRI) expose the changes in the amount of sales over the last 

your years before the company delisted. It is anticipated that delisted 

firms change their credit policy to manipulate reported sales or change 

revenue (sales) to affect earnings (Beneish, 1999). A considerable 

large increase in receivables could be related to earnings overstated. 

The mean of this index is 2.5 with maximum of 34.1 and minimum of 

0.12 while median is 1.2 and skewness is positive. It seems that 

companies are managing their earnings by pushing their sales upward 

in most cases. 

Sales growth index (SGI) is another earnings management index 

that considered relevant in the study. SGI flags companies’ 

performance which is low for delisted companies in this study. Mean 

and median while close are low (0.98 and 0.97 respectively), more 

over standard deviation is low and skewness is positive. These results 

indicate that companies as expected are suffering from poor 

performance that motivates earnings management. 

Leverage index (LVI) is included in this study to investigate 

incentives for using debt agreements to affect earnings management as 

explained by Beneish (1999). As included in Table 12, LVI is between 

maximum 77.2 to minimum 0.21 that the standard deviation is high 

enough to explain the diversity between the companies. Mean is 2.1 

that stand above median indicating that leverage is high in these 

companies. By rule of thumb LVI more than one is clear increase in 

leverage in this case over the last two years ended by delisting. 

67



May 23 and 24, 2012 

Table 12: Descriptive analysis of Earnings management indices 

68 

DSRI SGI LVI 

Mean 2.5 .98 2.1 

Median 1.2 .97 1.0 

Std. Deviation 5.158 .5395 8.977 

Skewness 4.73 1.48 8.53 

Minimum .12 .03 .21 

Maximum 34.1 3.2 77.7 

Correlation analysis of aforementioned indices, DACC and TACC 

of one year before delisted shows that only LVI is significantly related 

(-0.233, at the 0.05 level, 2-tailed) to total accruals (TACC) of one year 

before delisted and not significant relation to DACC. Regression 

analysis indicates also a low R square either when DACC or TACC 

are considered as dependent variables (0.026 and 0.074 respectively). 

Conclusion and remarks 

The objective this paper was to investigate the state of earnings 

management and corporate governance in the Iranian companies 

delisted from the national stock market. Dependent variables of firm 

characteristics are used to structure hypotheses. Results indicate that 

while these companies manage their earnings extensively, profitability 

is statistically significant related to DACC in firms that manage their 

earnings downward. Leverage and total assets (proxy for firm sixe) 

exhibit no statistical relationship with positive or negative DACC. 

While there is no official corporate governance rules are in place by 

the stock market, voluntary disclosure about corporate governance 

indicates that board of directors with five members is common in 

delisted companies. Having more than 3 in average independent


directors on board of directors and highly concentrated institutional 

ownership are reported by these companies. 

This study like other research in the area of distressed companies, 

suffers from lack of reliable financial information related to earnings 

management and corporate governance. Results of this study suggest 

that authorities should take remedial steps in establishment of 

corporate governance guideline, encouraging companies’ disclosure 

about such rules to affect unjustifiable earnings management and 

bring about transparency. Impassioning corporate governance rules 

provide learning opportunity for management, government regulators, 

investors, and a basis for auditors to develop early warning system. 

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May 23 and 24, 2012 

Benchmarking The Research Productivity Of 

Accounting Doctorates 

James R. Hasselbac 

Mary Ball Washington Eminent Scholar, College of Business, University of West Florida 

Alan Reinstein 

George R. Husband Professor of Accounting, Department of Accounting, School of Business 

Administration 

Mohammad Abdolmohammadi * 

John E. Rhodes Professor of Accounting, Bentley University 

Abstract 

Increasing attention to faculty research productivity suggests a need for 

reliable benchmarks, which the literature has provided. We add to this 

literature by providing alternative benchmarks based on records of 5,604 

accounting doctoral graduates from 1971-2005. We measure research 

productivity in four ways: (1) unadjusted number of published articles in the 

Best 3, Best 13, Best 24, and Best 40 journals, (2) published articles adjusted 

for journal quality scores, (3) published articles adjusted for coauthorship, 

and (4) published articles adjusted for both coauthorship and journal quality. 

We find evidence that average publication productivity of accounting faculty 

per year has steadily increased over the 35 years under study. We present 

benchmark measures based on faculty productivity in four sets of journals 

both from 1971-2005 and for each year of 2001-2005. The former shows 

that a significant proportion of faculty have never published in any of the 40 

journals studied. The latter shows nine years of productivity in the most 

* Corresponding Author: mabdolmo@bentley.edu


recent years. This data can be useful as a benchmark for promotion and 

tenure decisions. We also present productivity percentiles as another 

benchmark. Finally, we present data on the research productivity of the top 

10 most productive faculty (based on the most conservative measure of 

published articles adjusted for both coauthorship and journal quality) from 

1971-2005 to identify another comparison benchmark . 

Further correlation analysis indicates that productive researchers rank 

about the same regardless of the productivity measure used to evaluate them. 

Multivariate tests also reveal effects for gender (male faculty generally 

scoring higher than female faculty), school of affiliation (faculty at doctoral 

granting institutions as significantly more productive—especially in the 

higher level journals—than their counterparts at non-PhD schools), 

professorial rank (professors scoring higher than those in administrative and 

other roles), and teaching years since doctorate (those with 10 years or less 

of service since doctoral year being more productive than those with 11 

years or more). 

The benchmarks identified in the study can help with tenure, promotion, 

merit pay, appointment and renewal of chaired professorships, and other 

resource allocation decisions . 

Keywords: Benchmark, Research Productivity, Faculty Recruiting 

1. Introduction. 

The accounting literature provides many studies that use 

accounting faculty research productivity to rank accounting programs 

(e.g., Hasselback and Reinstein 1995; Glover et al. 2006); doctoralgranting 

programs (e.g., Everett et al. 2004, Chan et al. 2007) and 

individuals (e.g., Brown and Gardner 1985A; Hasselback et al. 2000 

and 2003). More recently, in a series of papers, Coyne and his 

coauthors (e.g., Coyne et al. 2010) have introduced multiple 

benchmarks for doctoral programs and individuals by topical area and 

research methodology. The literature suggests that benchmarks are 

often used as critical evidence for (1) promotion and tenure (P&T) and 

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May 23 and 24, 2012 

merit pay purposes; (2) prospective students selecting doctoral 

programs; and (3) accounting programs deciding on their slates of 

doctoral recruiting schools. Focusing on individual research 

benchmarks (percentile analysis and the top-ten researchers overall 

and by the year of graduation) can help to identify (1) the research 

productivity of faculty members’ national peers (e.g., to help them set 

their personal research goals); (2) criteria for awarding new faculty 

members ranks of associate professor or full professor, or tenure; and 

(3) standards to select or retain chaired professors. 

Hasselback’s Accounting Faculty Directory 2011-2012 shows 

that U.S. accounting doctoral programs produced annually about 200 

graduates from 1991-1994; about 110 from 2000 – 2003; and about 

140 from 2007 – 2010. The huge time and cost demands to earn such 

degrees probably have contributed greatly to the lower enrollments in 

recent years, while, as presented later, faculty members publish evergreater 

numbers of research papers in various journals, a phenomenon 

that may be related to increasing requirements to earn P&T. The 

decreasing number of doctoral graduates led the 70 largest accounting 

firms, 47 state societies of CPAs, and others to commit $17 million 

dollars to the Accounting Doctoral Scholars (ADS) program to help 

fund 120 incremental enrollments in accounting doctoral programs in 

areas of particularly high faculty shortage—auditing and tax ( 

American Institute of CPAs Foundation 2011). Research benchmarks 

can help prospective ADS students, and the universities seeking ADS 

students and funding, to develop realistic expectations for their 

research productivity. 

Research benchmarks are also important in light of American 

Assembly of Collegiate Schools of Business (AACSB, 2010) 1 

guidelines asking member schools to adhere to their mission 

statements, including research productivity standards that have led 

many accounting programs to develop journal ranking lists. Lucertini 

76


et al. (1995) urge schools to seek benchmarks to “continuously search, 

measure, and compare” their competitors’ best practices. 

Several benchmarking studies rank research outlets or examine 

research productivity, which generally show three measures of 

research productivity: (1) qualitative rank-ordering of accounting and 

related journals, (2) quantitative measures of total and average faculty 

research productivity and (3) quantitative measures of total and 

average research productivity of faculty according to where they 

earned their doctoral degrees. These studies generally face such 

challenges as: (1) how many journals to count and how many points to 

assign to each one; (2) how many journals to place into each journal 

quality category; (3) how to weigh coauthored articles, (e.g., 1/n credit 

for n-person articles, or full credit for each author); and (4) how to 

measure time since doctorate, thus enabling more valid comparison 

between years of doctoral graduation (e.g., 1990 compared with 2010 

graduates). 

Hasselback and Reinstein [H&R] (1995A; 1995B) and 

Hasselback, Reinstein, and Schwan [HRS] (2000; 2003) examined 

about 40 journals for up to 30 years, giving each coauthor both full 

and partial credit to develop benchmarks for individual, school, and 

doctoral-granting programs. Given the decline in doctoral enrollments 

and that eight years have elapsed since performing the most recent of 

these studies, we updated the datasets analyzed for this study. We 

examine the quality and quantity of research productivity of the 5,604 

accounting faculty graduates of U.S. accounting doctoral programs 

from 1971 and 2005, and who published their research in 40 highly 

rated accounting and business journals through 2009. We first present 

unadjusted raw numbers (i.e., full-credit) of articles published by 

doctoral graduates through 2009. We then assign coauthorship 

weights and journal quality weights using methodologies of prior 

studies to develop four measures of productivity. A Spearman 

correlation analysis of the non-zero publication data indicates that the 

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May 23 and 24, 2012 

four measures are very highly correlated, providing similar rankings 

of productive researchers. Our multivariate linear regression analysis 

investigates the sensitivity of the overall results to demographic 

variables The results indicate significant effects for associations 

between research productivity (as the dependent variable) and gender, 

doctoral vs. non-doctoral institutions’ professorial vs. administrative 

roles, and teaching experience (10 years or less vs. 11 years or more). 

2. Literature Review 

Benchmarking studies first must determine which journals to 

consider and what weights to assign to them. Benjamin and Brenner 

(1974), Brown and Heffner (1994), Hall and Ross (1991), Howard and 

Nikolai (1983), Hull and Wright (1990), Jolly et al. (1995), Reinstein 

and Calderon (2006), Herron and Hall (2004), Barniv and Fetyko 

(2007), among others have surveyed accounting faculty, 

administrators, or practitioners to assess the quality of academic and 

practitioner journals. Other studies (e.g., Herron and Hall, 2004) have 

developed benchmarks by accounting research discipline (e.g., 

auditing and tax). 

Everett et al. (2004) ranked U.S. doctoral programs based on the 

1992-1996 publication productivity of 30 highly rated academic 

accounting journals. They focused on the breadth and depth of faculty 

members’ achievements (e.g., proportion of tenured and tenure-track 

faculty members publishing in these journals) based on their rank on 

the 30 journals investigated. Bean and Bernardi (2005) analyzed the 

journal’s acceptance rates and time in existence, and its audiences to 

assess journal quality, which Matherly and Shortridge (2009) improved 

by including journal Social Science Index (SSCI) scores, submission 

fees, availability on electronic search engines (e.g., ABI-Inform), and 

page length. After synthesizing the Best 25 accounting journals from six 

other studies and conducting their own survey, Barniv and Fetyko 

78


(2007) developed a set of journal quality rankings. Herron and Hall 

(2004) ranked the best accounting journals by surveying 616 accounting 

faculty nationwide and compared their results to prior studies. Coyne et 

al. (2010) examined the research productivity of faculty publishing in 11 

high-quality accounting journals from 1990 through 2009 to help rank 

the top accounting doctoral programs, and Pickerd et al. (2011) used a 

similar methodology to rank individual faculty members by topical area 

and methodology. HRS (2000; 2003) used several of these studies to 

rank 40 journals into four strata. 

Overall, the benchmarking studies of faculty research productivity 

have used three general methods: (1) count the number of articles 

written; (2) perform citation analysis; or (3) survey key constituents 

(e.g., faculty members, deans). These methods have had certain 

limitations. For example, Dwyer (1994), Zivney et al. (1995), Glover 

et al. (2006), Stephens et al. (2011), and Coyne et al. (2010) count the 

number of a faculty member’s or program’s publications to rate 

programs; but they assess the published material’s quantity, not its 

quality, including only articles appearing in the most prestigious 

journals. 

Problems also arise in identifying the journals to “count” and 

whether to (1) consider notes, letters to the editors, and other types of 

published works; (2) give full or partial credit (or disclose both 

results) for coauthored articles; and (3) allow publication credit to the 

faculty member's present institution or to the affiliation when the 

article was written. For example, Englebrecht et al. (2008) analyze 

the 1979-2004 coauthorship patterns for eight premier accounting and 

four premier non-accounting business journals. They find increased 

coauthorship rates over time and that coauthorship within premier 

non-accounting business journals has long exceeded those of 

accounting journals. The authors considered only12 journals and did 

not calculate individual yearly research output data. Danielson and 

Heck (2010) examined the publication patterns of the authors of 15 

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May 23 and 24, 2012 

“high-impact” accounting journals, finding that the same sets of 

authors dominate both the first- and second-tier of such journals, and 

listed the “Best” authors in each of these 15 journals. They ignored 

the authors’ time since earning their doctorates, thereby placing more 

experienced authors generally ahead of less experienced ones. 

Citation analysis measures how often other articles reference 

(“cite”) articles, authors, or journals, presuming that high quality 

articles and journals are cited more often than low quality ones. 

Garfield’s (1955) early study developed this method to track an 

article’s “history.” Recent technological advances have led to 

extensive progress using this method, where SSCI data bases show the 

frequency of citation by a SSCI-listed article. However, this method 

considers, perhaps unequally, only about 10 accounting journals, 

ignoring, for example, the Journal of the American Tax Association 

[JATA] but including Auditing: A Journal of Practice & Theory 

[AJPT]. McRae (1974) first used citation analysis on accounting 

publications to measure the frequency of citations of 17 articles, while 

Brown and Gardner (1985A) used it to assess the research 

contributions of accounting faculty and doctoral programs. Brown 

and Gardner (1985A) and Chan et al. (2009) also used it to measure 

the impact of high level publications. 

Citation frequency is presumed to have the valued attribute of 

objectivity—either an article is cited or it is not, ignoring the article's 

quality or reasons for making the citation. The author's reputation, the 

contentiousness of the subject matter, and the journal's circulation, 

coverage, and timeliness can all influence citation frequency. Further, 

the efficacy of citation analysis depends greatly on the 

representativeness of the publications used to conduct the frequency 

analysis of cited works. Reinstein et al. (2011) point out many 

problems in relying on citation counts to assess the quality of 

scholarly research in accounting. 

80


Surveys of faculty, administrators, or practitioners to assess 

academic and practitioner journal quality also present challenges, such 

as non-response bias; proper ordinal, interval, or ratio scales to use in 

ranking journals relative to an “anchor;” and whether respondents can 

competently assess the journals listed. 

H&R (1995A; 1995B) and HRS (2000; 2003) addressed many 

of these issues in assessing individual and accounting and doctoral 

programs’ quality based upon publication records in 40 accounting 

and business journals. Their comprehensive accounting faculty 

benchmarks relate publication expectations to both the quantity of 

articles and the quality of journals. They based journal quality on a 

composite of five other studies; weighted their results by the number 

of coauthors and journal quality to develop a quality composite index; 

and considered all 2,708 graduates from 73 U.S. doctoral programs in 

1978-1992. 

Stephens et al. (2011) examined the research records of 1990 

to 2000 accounting doctoral graduates publishing their articles in 11 

major academic journals for both their first three and first six years 

after graduation. They used their data to rank doctoral programs, by 

examining the faculty authors’ topical and research methodologies. 

Stephens et al. (2011) weighed the 11 journals equally and ignored 

that larger programs have more graduates available to publish than 

smaller programs. 

The research questions we seek to address in the current study are as 

follows: 

RQ1: What is the faculty research productivity for 1971-2005 

doctoral graduates? 

Hasselback et al. (2011) report that 1999-2003 U.S. accounting 

doctoral graduates had greater research productivity than their 1989- 

1993 counterparts. We investigate this issue for 1971-2005 doctoral 

graduates by using the following research question: 

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May 23 and 24, 2012 

RQ2: What is the trend in faculty research productivity over 

the years 1971-2005? 

Finally, we develop the following research question to help 

identify faculty research productivity benchmarks. 

RQ3: What are benchmarks for accounting faculty research 

productivity? 

3. Research Method 

Hasselback’s Database of Publications served as the source of 

data for faculty publication records in our Best 40 journals. We 

adjusted individual faculty publication records for coauthorship and 

journal quality to derive several sets of benchmarks and to formulate 

trends in coauthorship and publication quality over time (e.g., time in 

grade). We also list the Best 10 researchers based on the number of 

publications adjusted for coauthorship and journal quality for 1971 – 

2005. We compare these researchers based on their Best 40 journal 

ranks, their coauthor adjusted ranks, and their coauthored and journal 

quality ranks to investigate the sensitivity of these ranks to 

coauthorship and journal quality. 

To analyze the research productivity of all 5,604 graduates from 

U.S. accounting doctoral programs from 1971 and 2005, we first 

reviewed HRS’ (2000; 2003) methodologies to select and rank the 

journals to include in our study. We then reassessed this database of 

30 academic, five business, and five practitioner journals by reviewing 

Glover et al. (2006), Barniv and Fetyko (2007), Chan et al. (2008), 

Matherly and Shortridge (2009), and other new ranking studies. We 

examined no post-2005 graduates in order to give them time to amass 

research records through 2009. 

Some changes from the HRS (2003) list include (1) replacing 

Journal of Business (which ceased publication) with Decision 

82


Sciences; (2) adding Review of Accounting Studies, since Advances in 

Accounting and Advances in International Accounting merged into 

one journal; (3) replacing Accounting, Auditing & Accountability and 

Accounting and Finance with the Journal of International Accounting 

Auditing & Tax and the Accounting Historians Journal for the lowest 

category; and (4) moving Auditing: A Journal of Practice & Theory 

[AJPT] from Category 3 to 2 and Accounting Horizons from Category 

4 to 3. 

Exhibit 1 presents the 40 journals under study arranged in a 

descending order of their ratings from previous studies. Similar to 

Morris et al. [1990] and Glover et al. [2006] we assign each journal to 

a category. As the first column in Exhibit 1 shows we use four 

categories to organize the journals. In category I we include the top 

three journals of accounting (JAR, TAR, and JAE), followed by 10 

journals in category II that has Journal of Finance and AJPT in it. 

The third category contains 11 journals (e.g., Decision Sciences, 

Journal of Accounting and Public Policy), followed by category IV 

that contains 16 journals (e.g., Financial Analysts Journal, Issues in 

Accounting Education). We also list each journal’s publication period 

in Column 2. 

[PLEASE PLACE EXHIBIT 1 ABOUT HERE] 

To identify each faculty member’s published journal articles, we 

created a database of journals, authors, and publication dates from 

each selected journal’s tables of contents used in this study. Including 

all articles in the 40 journals through 2009, we resolved problems such 

as author name changes, author misspellings, using initials rather than 

first names, and cases where authors shared the same name by 

checking the actual articles or author vitae. 

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May 23 and 24, 2012 

4. Results 

In this section we present the results organized by the study’s three 

research questions. This is followed by a section on additional 

analyses, to address the sensitivity of the main results to some 

alternative quality measures and several demographic variables. 

4.1 Faculty Research Productivity (RQ1) 

In the first two columns of Exhibit 2, we present the total number 

of 1971-2005 doctoral graduates for each year, followed by the next 

four columns that report the total number of articles published in the 

best 40 journals by year of graduation. The “Full Credit” category 

counts all articles published regardless of journal quality or 

coauthorship. Journal quality (Q1) and authorship (Q2) adjustments, 

are reported in the next three columns as Q1, Q2 and Q1&Q2. Q1 

indicates that the raw number of publications is adjusted for the 

quality of the journals in which they were published per Exhibit 1. Q2 

makes adjustments to full credit articles for coauthorship, where each 

of n coauthors receives 1/n credit for a coauthored article. Nathan et 

al. (1998) found that the vast majority of programs give full credit for 

coauthored publications. Englebrecht et al. (2008) add that such factors 

as each coauthor bringing specialized talents and the time requirements 

for sole-authored articles required to meet increasingly competitive 

publication standards leads to increases of coauthorships. Our “Full 

Credit” measurement gives full credit to each coauthor. Using Q2, we 

also provide data for programs that may use proportional (1/n) credit for 

coauthored articles that have n authors. 

Q1&Q2 is the fourth measure that adjusts full credit articles 

for both journal quality and coauthorship, and is repeated in the next 

four columns in Exhibit 2, which identifies the average numbers of 

articles per faculty. The last four columns report these measures 

scaled by the number of years since doctoral graduation. The 

84


denominators for the number of years since graduation are the 

differences between the year 2009 and the years since graduation. For 

example, we divided the number of articles published by 1971 graduates 

by 39, 1972 by 38, and so on to develop faculty productivity per year. 2 

To illustrate, an author with one sole authored article in Journal of 

Accounting & Economics (JAE) and one single co-authored article in 

Auditing: A Journal of Practice and Theory (AJPT) would accumulate 

to: 1 TIMES 2.00 points (for JAE) + 0.5 TIMES 1.6 points (for 

AJPT), for a total of 2.8 points. 


As shown in the final row of Exhibit 2 the 5,604 graduates of 

1971-2005 have published 22,581 articles in the Best 40 journals 

through 2009. Adjusted for coauthorship and journal quality, the 

publication credit indicates 31,047.45 measure for journal quality 

(Q1), 11,714.94 measure for coauthored articles (Q2), and 16,072.92 

measure for both journal quality and coauthorship (Q1&Q2). Scaling 

this data by the number of faculty (i.e., doctoral graduates) renders 

4.03 papers per faculty over the years (5.54, 2.09, and 2.87, 

respectively when adjusted for Q1, Q2, and Q1&Q2). Further scaling 

of these data renders 0.20 Full Credit, 0.28 Q1, 0.10 Q2, and 0.14 

Q1&Q2 publications per faculty per year. In summary, the last line in 

Exhibit 2 shows that on average each faculty member wrote 0.20 

articles per year, with a 0.28 weight when considering journal quality, 

but only 0.10 when considering coauthorship and 0.14 when 

considering both journal quality and coauthorship. 

4.2 Trends in Faculty Research Productivity (RQ2) 

The scaled data for articles per faculty per year in Exhibit 2 reveal 

great variation from 1971-2005. For example, the mean “Full Credit” 

articles per faculty per year indicates a range from 0.09 for the 1971 

graduates to 0.29 for the 1998 graduates. Using Analysis of Variance 

85



May 23 and 24, 2012 

(ANOVA) to investigate the statistical significance of variation 

observed in Exhibit 2, the results (not tabulated) indicated highly 

significant differences for each of the four productivity measures in 

Exhibit 2 at the p


respected journals, we developed a database of faculty publications for 

the Best 13 journals (top three plus next 10) shown in Exhibit 1—four 

of which are “business” rather than “accounting” journals. To more 

broadly describe journal quality, we developed a database of faculty 

publications for the Best 24 journals (top three plus next 10 plus next 

11) that incorporates about half of the journals shown in Exhibit 1. 

These journals are still selective and well respected overall, as they 

appear in most of the above listings of high quality research journals 

(e.g. Stephens et al. 2011, Coyne et al. 2010). 

We also recognize that our database of publications in 40 journals 

does not contain all accounting faculty publications, only the best 40 

of over 100 considered journals. Thus, the Best 40 benchmark offers 

a level of quality in the top half of all journals to help generalize our 

benchmarks, including five “business” and five “practitioner” 

journals. These benchmarks tend to be most useful for teaching 

institutions and those interested in the quantity of faculty research. In 

particular, they include several journals, such as Journal of 

Accounting Education and Issues in Accounting Education that are 

widely read by those interested in pedagogical issues. 

Per Panel A in Exhibit 4, 75% of all 1971-2005 graduates 

published no articles in the best three journals, 10.3% published only 

one article, 4.8% published two articles, 2.8% published three articles, 

1.7% published five articles, and only 1.4% published three articles. 

The ratio of those publishing six or more articles in the Best 3 journals 

dropped below 1%. As expected, Exhibit 4, Panel A shows that when 

including more journals, the number of faculty not publishing in the 

journals decreases. For example, while 75% of faculty do not publish 

in the Best 3, 61.5% do not publish in the Best 13, 52.6% in the Best 

24, and 32% in the Best 40. Thus, a significant proportion of 

accounting faculty publishes no articles in any Best 40 journal. 


87



May 23 and 24, 2012 

In Panel B of Exhibit 4, we report the results of an analysis that 

parallels Panel A of Exhibit 4, but does so at the year level for each of 

the five years 2001-2005. Since our database traces publications to 

the year 2009, the analysis by the year of PhD graduation provides 

faculty productivity for 5, 6, 7, 8, and 9 years. This analysis can be 

helpful as a benchmark for faculty P & T decisions. Similar to Panel 

A, this data shows that a significant proportion of faculty has never 

published in the Best three, Best 13, Best 24, or Best 40 journals. For 

example, for year 2001, 81.6% never published in the Best three, 

62.4% never published in the Best 13, 54.4% never published in the 

Best 24, and 31.2% never published in the Best 40. This lack of 

publication left only 22.4% of the PhDs that published one article, 

13.6% who published two articles, and 8.8% who published three 

papers in the Best 40 journals. 3 

The four levels of journal quality allow decision-makers to “count” 

articles published in certain journals rather than make quality 

adjustments for each article written, which also considers that many 

doctoral programs and research-focused institutions count only certain 

articles in top tier journals and often ignore those written in lower 

level journals. Other institutions make relative evaluations by looking 

at their faculty productivity relative to their nationwide peers. To help 

with this process, Exhibit 5 shows research productivity benchmarks 

by using percentiles of all faculty publishing in the Best 3, 13, 24, and 

40 journals. As shown in Panel A, the first quartile is zero for these 

journals, indicating that 25% of faculty publish no articles in any 

journal category. Except for the Best 40 journals, the same conclusion 

arises for the second quartile. Only when considering the Best 40 

category does the second quartile show publications, with only two 

publications. The third quartile shows one article in the Best 13, two 

in the Best 24, and six in the Best 40 journals, but no publications in 

the Best 3. Only when considering the 90 th percentile one finds 

publications in all four categories of best journals (2, 5, 7, 11 

88


respectively). The 99 th percentile data found in the final row in Panel 

A of Exhibit 5 shows that only one percent of faculty has achieved 11, 

18, 22, 28 publications respectively in the Best 3, Best 13, Best 24, 

and Best 40 journals. 

Panel B in EXHIBIT 5 presents similar data for the number of 

publications in the Best 40 journals (Full Credit) per faculty per year, as 

well as Q1, Q2, and Q1&Q2 adjusted numbers. As reported in the 

bottom, at the 99 th percentile, faculty members have published 1.23 

papers per year (full credit), 2.02 Q1-adjusted, 0.62 Q2-adjusted, and 

0.98 Q1&Q2 Adjusted. At the 25 th percentile, the number of 

publications is zero and for 50 th percentile, they are 0.09, 010. 0.05, and 

0.05 publications, respectively. 

[PLEASE PLACE EXHIBIT 5 HERE] 

We now identify the top ten productive faculty as a new faculty 

productivity comparison benchmark. We list the top 10 faculty in 

Exhibit 6 (Column 1) from 1971-2005 arranged in a 1-10 rank order 

by the most conservative measure of quality (Q1&Q2) that appears in 

the last column. We also report faculty productivity ranks using the 

remaining three productivity measures (i.e., Full credit, Q1, and Q2) 

that correspond to the 1-10 ranks. Column 2 presents the number of 

articles in each of the four categories of journals per Exhibit 1. 

Column 3 in Exhibit 6 presents the number of publications adjusted 

for each of Q1, Q2, and Q1&Q2, the current institution or affiliation 

(Column 4), and university that granted the doctoral degree (Column 

5). 


The resulting top 10 faculty members in Exhibit 6 provide 

benchmarks for accounting programs aspiring to compete at the 

highest productivity level. For example, in 1971, Professor Ross 

Watts ranked No. 1 under productivity measures of Best 40, Q1, and 

89



May 23 and 24, 2012 

Q1&Q2. Only for Q2 (number of articles adjusted for coauthorship) 

did he rank below number 1. Professor Edward McIntyre ranked 

number 9 under the Q1&Q2 measure, but number 27, 17, and 18 

respectively for the Best 40, Q1, and Q2 measures. 

As expected, Exhibit 6 shows that while many of the top-ten 

ranked faculty regularly publish in the Best 3 premier journals, many 

others do not, which is consistent with the data in Exhibit 4, Panel A 

indicating that over 75% of all faculty have published no articles in 

the Best 3 journals. These results may indicate that limiting 

publication in the Best 3 journals as a research productivity 

benchmark may be too limiting, and thus is likely to be useful 

primarily for the highest rated institutions, which likely will expect 

their faculty to publish in the top journals only. 

Expanding the discussion of those publishing in premier journals 

to include Category II (the next 10) journals—four of which are 

“business” journals—generates a broader coverage to help set 

benchmarks for schools focusing on strong, but not the Best 3 premier 

academic journals. Finally, disclosing the number of faculty 

publishing articles in the Best 24 and the Best 40 journals should help 

establish benchmarks for programs encouraging their faculty to 

publish in a broader set of journals, including practitioner and 

educational publications. These exhibits could provide benchmarks 

for institutions that place equal emphasis on teaching and research. 

For the critical periods of tenure and promotion (P & T), perhaps 6 to 

10 years, an institution desiring to place in the Best third of all 

institutions could, say, set a benchmark of two or three articles in the 

best 40 journals. The productivity measures of the past several years 

(say 2001-2005) may be particularly helpful in assessing faculty 

productivity in their first several years in the professoriate. 

90


4.5 Sensitivity Analysis 

This section further analyzes the robustness of the results 

discussed above. The first analysis appears in Exhibit 7 which 

presents a nonparametric bivariate Spearman correlation matrix of the 

four productivity measures for the entire 35 years under study (1971- 

2005). Only the non-zero publication data are included in this 

analysis (n=3,812). The coefficients are all in the 0.9 plus level, with 

a minimum of 0.928 representing the relationship between Q1 and Q2, 

and all coefficients are highly significant at



May 23 and 24, 2012 

contains 4,093 male and 1,503 female faculty members. In the 

absence of theory predicting difference between genders, we do not 

make a signed predication. 

We expected to find significant differences in research 

productivity between faculty serving at doctoral-granting (n=1,134) 

compared to non-doctoral (n=4,470) programs, expecting those 

working at the former to publish more, and in higher level journals 

than those at the later ones. We also expect that faculty in professorial 

ranks (i.e., assistant, associate, or full professor, n=2,745) will have 

significantly higher research productivity than those in administrative 

or other roles (n=2,859). We next included the “Teaching Years since 

Doctorate” variable. Assuming that many faculty members work to 

achieve tenure and promotion to the ranks of associate and full 

professor in their first 10 years after doctoral year, we argue that 

faculty members in their first 10 years (n = 731) are more motivated to 

be productive than those with 11 or more years (n = 4,873). 

The results from our regression analysis are presented in Exhibit 8. 

For this analysis we report the results of the regression analysis with 

Q1&Q2 as its dependent variable because this measure is the most 

conservative measure of productivity. We also used Full Credit, Q1, 

and Q2 measures as dependent variables and estimated the regression 

model, but we do not tabulate these results because they are generally 

consistent with the Q1&Q2 regression results. 


As Exhibit 8 shows, the regression model is highly significant (Fstatistic 

= 464.27, p


significant (p



May 23 and 24, 2012 

We introduce multiple benchmark measures for faculty use. One 

measure is nine years of productivity in the most recent years, which 

can serve as a useful benchmark for promotion and tenure decisions. 

Productivity percentiles add another benchmark for faculty to identify 

their corresponding productivity percentile. For example, schools 

who are interested in the 75 th percentile productivity can compare their 

faculty with the top 75% by consulting our benchmark in this paper. 

Panel A of Exhibit 5 shows that at this level of productivity faculty 

has published one article in the Best 13, two in the Best 24, and six in 

the Best 40 journals, but none in the Best 3. At the 90 th percentile, we 

find faculty publishing 2, 5, 7, 11 articles respectively) in the best 

journals. Thus only 10 percent of faculty publish 11 articles or more 

in the Best 40 journals since their doctoral years, which improves to 

22 articles at the 99 th percentile. Panel B in Exhibit 5 shows percentile 

data for Full Credit, Q1, Q2, and Q1&Q2 publications in the Best 40 

journals. 

As another benchmark we present the top-10 productive faculty 

based on their publications in the Best 40 journals, adjusted for journal 

quality and coauthorship (see Exhibit 6). We list these faculty 

members by their rank of productivity according to the most 

conservative Q1&Q2 measure, and identify their ranks according to 

the other three measures (Full Credit, Q1, and Q2). Using the 

nonparametric Spearman correlation of non-zero productivity 

measures, we find that productive researchers rank about the same 

regardless of the productivity measure used to evaluate them 

Finally, we use multivariate linear regression to investigate the 

sensitivity of our productivity measures to the effects of several 

demographic variables (Exhibit 8). We find that gender has a 

significant effect in the regression with Q1&Q2 as dependent variable, 

indicating that male faculty were a bit more productive than female 

faculty. For example, under the Q1&Q2 measure, the mean 

productivity per year for men is 0.1428 and for women is 0.1299, 

94


which, given the large sample sizes, indicates statistically significant 

differences. However, while the same result was observed for “Full 

Credit” as the dependent variable, gender became less significant for 

Q1and Q2 as the dependent variables. These results suggest a need for 

further research on gender differences in research productivity in 

accounting. Kirchmeyer et al. (2000) suggest that future research in 

this area should investigate such variables as gender similarity among 

department colleagues and graduation from a highly ranked doctoral 

program. 

Other results from our regression analysis were generally 

expected, suggesting that (1) faculty serving at doctoral-granting 

programs significantly outperform those serving at non-PhD-granting 

institutions; (2) faculty holding professorial ranks outperform their 

colleagues in administrative positions; and (3) more recent graduates 

(over the past 10 years or less) on average have higher levels of 

productivity than those with 11 or more years since their doctoral 

graduation. 

5.2. Limitations and Extensions 

As with prior studies measuring faculty research productivity, 

this study has limitations. First, we compiled data from only 40 

journals, omitting data from many other journals and publication 

outlets (e.g., monographs) where accounting faculty might publish. 

We also omitted notes and commentaries appearing in the 40 journals 

of the study and ignored the productivity of accounting faculty who 

earned doctoral degrees outside accounting. 

We developed four measures of the quantity and quality of 

research productivity to evaluate faculty research productivity. These 

measures may be sensitive to the accurate perceptions of those who 

rated the quality of journals. While perceived journal quality is a 

surrogate for the quality of specific articles, we note that journals of 

95



May 23 and 24, 2012 

lower perceived quality often publish seminal articles, and not all 

articles in premier journals are of high quality. 

Our multiple benchmark measures (proportion publishing 1, 2, 

3, or more articles in Best 3, 13, 24, and 40 journals in the five most 

recent years of PhD graduation, percentiles associated with 

productivity, and top 10 faculty) should help accounting programs 

evaluate their faculty for merit and P&T decision purposes in 

comparison with their peers nationwide. However, some schools 

should recognize that a large proportion of faculty that do not publish, 

or publish very few articles in our journal lists. Other schools with 

distinct research missions and resources may make their own 

customized adjustments to the benchmarks in our study. Our data 

may also be limited for comparisons among non-doctoral and 

doctoral-granting programs, and among research institutions and 

teaching institutions. Also, while productive researchers in our study 

rank about the same regardless of the productivity measure used to 

evaluate them, other productivity methodologies may produce 

different results. 

5.3 Conclusions 

The limitations aside, our results can help (1) faculty compare 

themselves to their national colleagues; (2) administrators assess the 

required number and quality of articles for P&T and merit pay 

purposes; and (3) justify granting associate/full professor or tenure to 

outside candidates, or chaired professorships to current or external 

faculty members. We also note some significant differences by year 

for publication productivity (Exhibit 2), with a general increasing 

trend over time (Exhibit 3) that could reflect more programs 

demanding increased levels of faculty research. Extrapolating this 

trend can help indicate future levels of expected productivity may be a 

fruitful avenue for future research. 

96


Future research can also extend our list of 40 journals and examine 

the research records of accounting faculty who have earned doctoral 

degrees in fields outside of accounting, or from non-U.S. programs. 

Analyzing these data by the schools that authors earned their doctoral 

degrees can provide productivity rankings of doctoral and nondoctoral 

granting accounting programs. 

Endnotes: 

1 Lewis (2008) notes that the AACSB International found that about 40% of its 

members created internal journal lists to assess their faculty’s research quality. 

2 We recognize the limitation of not adjusting our results for faculty who left 

academe, e.g., by retirement or death. 

3 We thank the Associate Editor for a suggestion leading to this analysis. 

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EXHIBIT 1 

BEST 40 JOURNALS QUALITY WEIGHTS & YEARS EXAMINED 

Category Journal Name Rating 

I. Top three 

Journal of Accounting Research [1971-2009] 

2.25 

The Accounting Review [1971-2009] 

2.25 

Journal of Accounting and Economics [1979-2009] 

2.00 

II. Next 10 

III. Next 11 

IV. Next 16 

Jurnal of Finance [1971-2009] 

Accounting, Organizations and Society [1976-2009] 

Auditing: A Journal of Practice and Theory [1981-2009] 

Contemporary Accounting Research [1984-2009] 

Journal of Accounting, Auditing and Finance [1977-2009] 

Journal of the American Taxation Association [1979-2009] 

Journal of Finance and Quantitative Analysis [1971-2009] 

Journal of Financial Economics [1974-2009] 

Management Science [1971-2009] 

Review of Accounting Studies [1996-2009] 

Decision Sciences [1971-2009] 

Journal of Accounting and Public Policy [1982-2009] 

Journal of Business, Finance and Accounting [1974-2009] 

Journal of Taxation [1971-2009] 

National Tax Journal [1971-2009] 

Abacus [1971-2009] 

Accounting and Business Research [1971-2009] 

Accounting Horizons [1987-2009] 

Behavioral Research in Accounting [1989-2009] 

Journal of Accounting Literature [1982-2009] 

Journal of Management Accounting Research [1989-2009] 

Financial Analysts Journal [1971-2009] 

Issues in Accounting Education [1983-2009] 

Journal of Accountancy [1971-2009] 

Advances in Accounting/Advances in International Accounting [1984/1987-2009] 

Advances in Taxation [1987-2009] 

101 

2.00 1 

1.60 

1.60 

1.60 

1.60 

1.60 

1.60 1 

1.60 1 

1.60 1 

1.60 

1.35 1 

1.35 

1.35 

1.35 2 

1.35 

1.15 

1.15 

1.15 

1.15 

1.15 

1.15 

1.00 2 

1.00 

1.00 2 

.95 

.95



May 23 and 24, 2012 

The International Journal of Accounting [1971-2009] 

Journal of Accounting Education [1983-2009] 

Journal of International Accounting, Auditing and Taxation [1992- 

2009] 

The Journal of Information Systems [1986-2009] 

Research in Accounting Regulation [1987-2009] 

Research in Governmental and Nonprofit Accounting [1985-2009] 

Accounting Educators' Journal [1988-2009] 

Accounting Historians Journal [1974-2009] 

Critical Perspectives on Accounting [1990-2009] 

Strategic Finance/Management Accounting [1971-2009] 

The CPA Journal [1971-2009] 

1 Top five rated business journals 

2 Top five recognized practitioner journals 

102 

.95 

.95 

.90 

.90 

.90 

.90 

.85 

.85 

.85 

.85 2 

.85 2 

.85 2 

.85 2

EXHIBIT 2 

DOCTORAL RESEARCH PRODUCTIVIT Y BY YEAR OF DOCTORAL DEGREE 

PhD Graduates Number of Articles in Best 40 Articles per Faculty in Best 40 Articles per Faculty per Year in Best 40 

Degree No. of Full Q1 Q2 Q1&Q2 Full Q1 Q2 Q1&Q2 Full Q1 Q2 Q1&Q2 

Year Grads Credit Adjusted Adjusted Adjusted Credit Adjusted Adjusted Adjusted Credit Adjusted Adjusted Adjusted 

1971 140 518 684.95 319.71 423.80 3.70 4.89 2.28 3.03 0.09 0.13 0.06 0.08 

1972 142 621 868.95 401.73 564.68 4.37 6.12 2.83 3.98 0.12 0.16 0.07 0.10 

1973 152 648 924.90 384.21 551.54 4.26 6.08 2.53 3.63 0.12 0.16 0.07 0.10 

1974 167 723 1011.00 413.68 575.58 4.33 6.05 2.48 3.45 0.12 0.17 0.07 0.10 

1975 152 749 1010.25 419.29 563.86 4.93 6.65 2.76 3.71 0.14 0.19 0.08 0.11 

1976 135 649 804.60 378.26 471.81 4.81 5.96 2.80 3.49 0.14 0.18 0.08 0.10 

1977 133 803 1156.05 437.81 638.91 6.04 8.69 3.29 4.80 0.18 0.26 0.10 0.15 

1978 179 1162 1552.65 635.77 844.12 6.49 8.67 3.55 4.72 0.20 0.27 0.11 0.15 

1979 131 621 838.60 330.37 451.48 4.74 6.40 2.52 3.45 0.15 0.21 0.08 0.11 

1980 137 760 1052.50 388.25 542.56 5.55 7.68 2.83 3.96 0.18 0.26 0.09 0.13 

1981 174 890 1167.80 461.68 608.88 5.11 6.71 2.65 3.50 0.18 0.23 0.09 0.12 

1982 176 964 1280.75 501.37 670.99 5.48 7.29 2.85 3.81 0.20 0.26 0.10 0.14 

1983 163 821 1136.60 420.96 582.26 5.04 6.97 2.58 3.57 0.19 0.26 0.10 0.13 

1984 162 740 1040.50 375.79 532.46 4.57 6.42 2.32 3.29 0.18 0.25 0.09 0.13 

1985 173 734 991.80 384.98 514.62 4.24 5.73 2.23 2.97 0.17 0.23 0.09 0.12 

1986 187 865 1165.20 438.48 581.80 4.63 6.23 2.34 3.11 0.19 0.26 0.10 0.13 

1987 199 925 1187.20 477.21 608.38 4.65 5.97 2.40 3.06 0.20 0.26 0.10 0.13 

1988 207 844 1173.15 425.89 594.56 4.08 5.67 2.06 2.87 0.19 0.26 0.09 0.13 

1989 216 871 1161.05 450.77 595.39 4.03 5.38 2.09 2.76 0.19 0.26 0.10 0.13 

1990 175 863 1289.65 409.82 612.28 4.93 7.37 2.34 3.50 0.25 0.37 0.12 0.17 

1991 198 753 962.55 384.45 485.59 3.80 4.86 1.94 2.45 0.20 0.26 0.10 0.13 

1992 201 762 1044.65 357.17 484.83 3.79 5.20 1.78 2.41 0.21 0.29 0.10 0.13 

1993 205 577 746.00 272.45 349.31 2.81 3.64 1.33 1.70 0.17 0.21 0.08 0.10 

103

1994 196 767 1044.00 366.86 499.46 3.91 5.33 1.87 2.55 0.24 0.33 0.12 0.16 

1995 170 649 921.00 302.12 431.84 3.82 5.42 1.78 2.54 0.25 0.36 0.12 0.17 

1996 163 506 717.75 246.41 345.42 3.10 4.40 1.51 2.12 0.22 0.31 0.11 0.15 

1997 159 552 751.20 259.31 351.88 3.47 4.72 1.63 2.21 0.27 0.36 0.13 0.17 

1998 152 536 813.15 249.15 376.03 3.53 5.35 1.64 2.47 0.29 0.45 0.14 0.21 

1999 129 324 490.90 153.75 231.46 2.51 3.81 1.19 1.79 0.23 0.35 0.11 0.16 

2000 107 300 449.50 145.83 216.16 2.80 4.20 1.36 2.02 0.28 0.42 0.14 0.20 

2001 125 285 395.15 140.62 193.97 2.28 3.16 1.12 1.55 0.25 0.35 0.12 0.17 

2002 114 225 319.45 108.85 155.97 1.97 2.80 0.95 1.37 0.25 0.35 0.12 0.17 

2003 105 208 319.90 93.72 140.35 1.98 3.05 0.89 1.34 0.28 0.44 0.13 0.19 

2004 142 210 321.75 103.02 159.67 1.48 2.27 0.73 1.12 0.25 0.38 0.12 0.19 

2005 138 156 249.35 75.20 121.02 1.13 1.81 0.54 0.88 0.23 0.36 0.11 0.18 

Tot al 5604 22581 31047.45 11714.94 16072.92 4.03 5.54 2.09 2.87 0.20 0.28 0.10 0.14 

1. Full credit refers to giving equal credit for each publication in the best 40 journals 

2. Q1 denotes the number of art icles adjusted for journal quality per Exhibit 1 

3. Q2 denotes the number of art icles adjusted for co-authorship (if n authors, then each author receives 1/n crdit) 

4. Q1&Q2 denotes the product of Q1 and Q2, i.e., considering both artilces adjusted for journal quality and adjusted for co-authorship. 

104

EXHIBIT 3 

RESEARCH PRODUCTIVIT Y OF DOCTORAL GRADUATES OF 1971-2005 

MEAN NUMBER OF ARTICLES IN BEST 40 JOURNALS PER FACULT Y PER YEAR 

105 

1. Full credit are articles per faculty per year (APFPY) in the best 40 

journals. 

2. Q1 is APFPY adjusted for journal quality. 

3. Q2 is APFPY adjusted for coauthorship, where an article with n authors 

brings 1/n credit for publication per faculty. 

4. Q1&Q2 is APFPY adjusted for journal quality and coauthorship.

EXHIBIT 4 

ARTICLES PUBLISHED IN BEST JOURNALS 

Panel A: Number and Proportion of Faculty Publishing in 

Best n Journals for the Entire 35 Years under Study (1971-2005) 

Number of Faculty (Percent of Tot al) 

No. of Articles Best T hree Best 13 Best 24 Best 40 

0 4205 (75.0%) 3444 (61.5%) 2947 (52.6%) 1792 (32.0%) 

1 578 (10.3%) 763 (13.6%) 830 (14.8%) 861 (15.4%) 

2 266 (4.8%) 365 (6.5%) 443 (7.9%) 557 (9.9%) 

3 156 (2.8%) 225 (4.0%) 288 (5.1%) 426 (7.6%) 

4 93 (1.7%) 154 (2.7%) 191 (3.4%) 328 (5.8%) 

5 77 (1.4%) 118 (2.1%) 167 (3.0%) 234 (4.2%) 

6 48 (0.9%) 95 (1.7%) 113 (2.0%) 238 (4.2%) 

7 41 (0.7%) 88 (1.6%) 113 (2.0%) 188 (3.2%) 

8 28 (0.5%) 60 (1.1%) 81 (1. 5%) 155 (2.8%) 

9 21 (0.4%) 48 (0.9%) 57 (1.0%) 121 (2.2%) 

10 20 (0.4%) 40 (0.70%) 62 (1.1%) 127 (2.3%) 

11-15 44 (0.8%) 110 (2.00%) 158 (2.8%) 291 (5.2%) 

16-20 20 (0.4%) 54 (1.0%) 87 (1.5%) 147 (2.6%) 

21-30 7 (0.1%) 31 (0.5%) 47 (0.8%) 91 (1.6%) 

Over 30 0 (0.0%) 9 (0.2%) 20 (0.4%) 48 (0.9%) 

Total 5604 (100.0%) 5604 (100.0%) 5604 (100.0%) 5604 (100.0%) 

106

Panel B: Number and Proportion of Faculty Publishing in 

Best n Journals for the Most Recent Five PhD Years (i.e., 5-9 years of productivity) 

2005 Productivity to 2009 (Five Years, N=138) 


0 103 (74.6%) 84 (60.9%) 78 (56.5%) 62 (44.9%) 

1 20 (14.5%) 29 (21.0%) 30 (21.7%) 34 (24.6%) 

2 11 (8.0%) 14 (10.1%) 16 (11.6%) 20 (14.5%) 

3 2 (1.4%) 6 (4.3%) 9 (6.5%) 13 (9.4%) 

4 1 (0.7%) 4 (2.9%) 4 (2.9%) 6(4.3%) 

5 2(1.4%) 

8 1 (0.7%) 1 (0.7%) 

9 1(0.7) 1(0.7%) 

Total 138 (100.0%) 138 (100.0%) 138 (100.0%) 138 (100.0%) 

2004 Productivity to 2009 (Six Years, N=142) 


0 101 (71.1%) 85 (59.9%) 81 (57.0%) 54 (38.0%) 

1 24 (16.9%) 26 (18.3%) 25 (17.6%) 36 (25.4%) 

2 9 (6.3%) 15 (10.6%) 16 (11.3%) 23 (16.2%) 

3 4 (2.8%) 10 (7.0%) 12 (8.5%) 12 (8.5%) 

4 3 (2.1%) 3 (2.1%) 3 (2.1%) 5 (3.5%) 

5 1 (0.7%) 1(0.7%) 5 (3.5%) 

6 3 (2.1%) 2 (1.4%) 3 (2.1%) 

7 2 (1.4%) 3 (2.1%) 

8 1 (0.7%) 

Total 142(100.0%) 142(100.0%) 142(100.0%) 142(100.0%) 

107

2003 Productivity to 2009 (Seven Years, N=105) 


0 81 (77.1%) 70 (66.7%) 60 (57.1%) 43 (41.0%) 

1 8 (7.6%) 11 (10.5%) 14 (13.3%) 23 (21.9%) 

2 7 (6.7%) 8 (7.6%) 9 (8.6%) 9 (8.6%) 

3 2 (1.9%) 6 (5.7%) 11 (10.5%) 9 (8.6%) 

4 2 (1.9%) 3 (2.9%) 3 (2.9%) 7 (6.7%) 

5 3 (2.9%) 1 (1.0%) 1 (1.0%) 4 (3.8%) 

6 1 (1.0%) 

7 1 (1.0%) 3 (2.9%) 2 (1.9%) 5 (4.8%) 

8 1 (1.0%) 1 (1.0%) 1 (1.0%) 1 (1.0%) 

10 1(1.0%) 2 (1.9%) 

12 1 (1.0%) 1(1.0%) 1 (1.0%) 

17 1(1.0%) 1 (1.0%) 

Total 105(100.0%) 105(100.0%) 105(100.0%) 105(100.0%) 

108

2002 Productivity to 2009 (Eight Years, N=114) 

No. of Articles Best Three Best 13 Best 24 Best 40 

0 94 (82.5%) 76 (66.7%) 61 (53.5%) 38 (33.3%) 

1 7 (6.1%) 16 (14.0%) 19 (16.7%) 24 (21.1%) 

2 2 (1.8%) 8 (7.0%) 13 (11.4%) 19 (16.7%) 

3 5 (4.4%) 4 (3.5%) 7 (6.1%) 10 (8.8%) 

4 3 (2.6%) 3 (2.6%) 6 (5.3%) 9 (7.9%) 

5 3 (2.6%) 4 (3.5%) 5 (4.4%) 6 (5.3%) 

6 1 (0.9%) 1 (0.9%) 2 (1.8%) 

7 2 (1.8%) 

8 2 (1.8%) 1 (0.9%) 1 (0.9%) 

9 1 (0.9%) 

10 1(1.0%) 

11 1 (0.9%) 

12 1(1.0%) 

13 1 (0.9%) 

17 

Total 114(100%) 114(100%) 114(100%) 114(100%) 

109

2001 Productivity to 2009 (Nine Years) 


0 102 (81.6%) 78 (62.4%) 68 (54.4%) 39 (31.2%) 

1 8 (6.4%) 19 (15.2%) 19 (15.2%) 28 (22.4%) 

2 3 (2.4%) 9 (7.2%) 13 (10.4%) 17 (13.6%) 

3 7 (5.6%) 6 (4.8%) 7 (5.6%) 11 (8.8%) 

4 3 (2.4%) 6 (4.8%) 5 (4.0%) 9 (7.2%) 

5 2 (1.6%) 1 (0.8%) 4 (3.2%) 4 (3.2%) 

6 2 (1.6%) 4 (3.2%) 7 (5.6%) 

7 4 (3.2%) 3 (2.4%) 2 (1.6%) 

8 1 (0.8%) 3 (2.4%) 

9 2 (1.6%) 

10 1 (0.8%) 

11 1 (0.8%) 

12 

13 1 (0.8%) 

1 (0.8%) 

Total 125(100%) 125(100%) 125(100%) 125(100%) 

EXHIBIT 5 

Panel A: Percentile Measures of Faculty Productivity: 

Full Credit in Best N Journals 1971-2005 

Percentile Best T hree Best 13 Best 24 Best 40 

25 th 0 0 0 0 

50 th 0 0 0 2 

75 th 0 1 2 6 

90 th 2 5 7 11 

95 th 5 9 11 16 

99 th 11 18 22 28 

110

Panel B: Percentile Measures of Faculty Productivity 

Per Faculty per year 1971-2005 

Percentile Full 

Q1 

Q2 Q1&Q2 

Credit Adjusted Adjusted Adjusted 

25 th 0.00 0.00 0.00 0.00 

50 th 75 

0.09 0.10 0.05 0.05 

th 0.28 0.35 0.14 0.18 

90 th 0.57 0.82 0.28 0.41 

95 th 0.75 1.16 0.38 0.58 

99 th 1.23 2.02 0.62 0.98 

1. Full credit refers to giving equal credit for each publication in the best 40 journals. 

2. Q1 denotes the number of articles adjusted for journal quality per Exhibit 1. 

3. Q2 denotes the number of articles adjusted for co-authorship (if n authors, then each author receives 1/n credit). 

4. Q1&Q2 denotes the product of Q1 and Q2, i.e., considering both artilces adjusted for journal quality and adjusted for coauthorship. 

111

1971 

1 

EXHIBIT 6 

MOST PRODUCTIVE ACCOUNT ING DOCTORATES 

BY PUBLICATION IN THE BEST 40 JOURNALSFOR THE YEARS 1971-2005 

2. Research Productivity 

(Full Credit) 3. Quality Adjustment s 4 5 

Best 

40 I II III IV 

Q1 

112 

Q2 

Q1Q 

2 

Affiliation 

Doctoral 

Program 

6. Top Ranks by Four 

Measures 

Watts, Ross L. 28 19 6 3 0 54.60 14.65 27.13 MIT Chicago 1 1 2 1 

Miller, P aul B. W. 28 0 0 4 24 27.10 21.00 20.20 Colorado Spr. Tx-Austin 2 4 1 2 

Graham, John R. 22 2 15 3 2 37.25 11.83 20.06 Arkansas 4 2 4 3 

Largay, James A. III 19 3 3 5 8 25.65 10.67 15.29 Lehigh Cornell 5 5 5 4 

Bailey, Andrew D. Jr. 19 7 7 0 5 31.50 7.92 13.45 Illinois Ohio St 6 3 10 5 

Salamon, Gerald L. 12 7 1 4 0 22.30 7.00 13.18 Indiana Ohio St 10 6 13 6 

Greer, Willis R. Jr. 12 2 2 4 4 16.55 9.00 12.47 No Iowa Michigan 9 10 7 7 

Sundem, Gary L. 10 5 2 2 1 18.60 6.17 12.07 U Washington Stanford 15 8 15 8 

McIntyre, Edward V. 6 5 1 0 0 12.85 5.50 11.72 Florida St N Carol 27 14 18 9 

Reichardt, Karl E. 23 0 0 0 23 19.55 12.33 10.48 Valparaiso Missouri 3 7 3 10 

Bes 

t 40 

Q1 

Q 

2 

Q1Q 

2

1972 

Ohlson, James A. 51 21 28 2 0 95.60 36.50 67.46 New York U Berkeley 1 1 2 1 

Riahi-Belkaoui, Ahmed 41 3 5 15 18 51.50 37.33 47.32 Ill-Chicago Syracuse 3 4 1 2 

Abdel-khalik, A. Rashad 30 19 7 2 2 58.60 23.83 46.35 Illinois Illinois 5 3 4 3 

Ball, Raymond J. 32 20 8 3 1 61.65 18.50 35.30 Chicago Chicago 4 2 5 4 

Previts, Gary John 51 1 2 9 39 51.05 29.50 28.54 Case Western Florida 2 5 3 5 

Deakin, Edward B. 16 10 0 2 4 28.50 11.67 21.47 Texas Illinois 10 6 7 6 

Choi, Frederick D. S. 19 1 1 2 15 19.85 17.00 17.80 New York U U Wash 6 11 6 7 

Lusk, Edward J. 12 6 2 4 0 21.50 9.50 16.52 SUNY-Plattsb Nrthwstrn 14 9 12 8 

Hagerman, Robert L. 17 6 5 1 5 27.75 10.17 16.31 SUNY-Buffalo Rochester 8 7 9 9 

Granof, Michael H. 14 3 1 4 6 18.60 9.83 12.65 Texas-Austin Michigan 12 12 11 10 

1973 

Sunder, Shyam 37 19 7 9 2 66.90 27.67 48.29 Yale 

113 

Car 

Mellon 2 1 1 1 

Ashton, Robert H. 31 17 8 3 3 57.40 21.33 39.82 Duke Minnesota 3 2 2 2 

Gordon, Lawrence A. 38 3 8 23 4 53.60 20.42 27.97 Maryland Rensselaer 1 4 3 3 

Imhoff, Eugene A. Jr. 25 9 5 5 6 39.20 17.67 26.18 Michigan Mich St 5 5 4 4 

Collins, Daniel W. 28 20 6 0 2 54.10 12.08 23.22 Iowa Iowa 4 3 7 5

Uecker, Wilfred C. 14 11 3 0 0 29.55 8.33 17.61 Rice Tx-Austin 13 7 12 6 

Schnee, Edward J. 24 0 4 10 10 28.70 12.83 15.48 Alabama Mich St 7 8 6 7 

Boatsman, James R. 19 11 2 4 2 34.40 8.92 15.27 Arizona St Tx-Austin 10 6 11 8 

Scott, William R. 10 4 4 2 0 17.90 8.00 14.83 U Waterloo Chicago 18 16 14 9 

Warren, Carl S. 11 6 2 0 3 19.55 7.50 14.07 Georgia Mich St 15 14 15 10 

1974 

Libby, Robert 40 28 10 1 1 81.00 20.50 41.52 Cornell Illinois 1 1 1 1 

Hughes, John S. 35 19 15 1 0 66.85 16.07 31.37 UCLA Purdue 2 2 3 2 

Zimmerman, Jerold L. 25 22 1 0 2 50.45 15.40 30.74 Rochester Berkeley 4 3 4 3 

Ferris, Kenneth R. 34 3 13 7 11 46.85 19.92 27.93 Arizona St Ohio St 3 5 2 4 

Baiman, Stanley 24 17 6 1 0 48.75 12.42 24.65 Pennsylvania Sanford 5 4 7 5 

Magee, Robert P. 18 13 4 1 0 36.50 11.58 24.43 Northwestern Cornell 10 6 9 6 

Griffin, Paul A. 17 8 4 4 1 29.55 11.17 18.99 Cal-Davis Ohio St 13 8 10 7 

Liao, Woody M. 20 4 1 9 6 27.05 12.67 17.59 Cal-Riverside Florida 7 9 5 8 

Holder, William W. 21 1 3 1 16 23.05 12.17 14.19 So Calif Oklahoma 6 12 8 9 

Blocher, Edward J. 18 3 4 1 10 23.55 11.00 14.18 No Carolina Tx-Austin 11 11 11 10 

114

1975 

Foster, George 28 12 9 6 1 49.20 19.33 35.00 Sanford Sanford 4 3 1 1 

Dirsmith, Mark W. 42 0 25 7 10 57.85 19.17 26.16 Penn State Nrthwstrn 1 1 2 2 

Vickrey, Don W. 21 5 1 12 3 30.95 14.33 20.75 Arizona St Tx-Austin 7 8 5 3 

Fellingham, John C. 33 7 10 10 6 50.30 12.95 20.11 Ohio State UCLA 2 2 6 4 

Wright, William F. 16 3 8 3 2 25.45 12.17 19.05 Berkeley 11 10 8 5 

Harrell, Adrian M. 24 2 11 6 5 33.95 12.58 17.54 So Carolina Tx-Austin 5 6 7 6 

Givoly, Dan 20 10 5 2 3 35.15 9.67 17.08 Penn State NYU 8 5 11 7 

Baker, C. Richard 20 2 1 2 15 21.90 14.83 17.02 Adelphi UCLA 9 13 4 8 

Lorek, Kenneth S. 23 11 2 3 7 38.65 9.58 16.68 No Arizona Illinois 6 4 12 9 

Flesher, Dale L. 31 1 3 3 24 31.85 16.33 16.43 Mississippi Cincinnati 3 7 3 10 

1976 

Maples, Larry 32 0 0 10 22 33.95 23.33 25.48 Tenn State Miss St 3 5 1 1 

Dillard, Jesse F. 28 1 6 8 13 33.40 17.25 20.98 Portland St S Carol 4 6 4 2 

Englebrecht, Ted D. 42 1 4 4 33 42.40 20.33 20.41 Louisiana Te S Carol 2 1 3 3 

Bloom, Robert 43 0 1 3 39 41.15 20.58 19.75 John Carroll NYU 1 2 2 4 

Gibbins, Michael 21 7 11 3 0 36.80 10.25 18.27 Univ Alberta Cornell 7 3 8 5 

115

Porcano, T homas M. 22 1 4 1 16 24.75 14.58 17.52 Miami U-Ohio Indiana 6 9 5 6 

Ro, Byung T . 17 5 5 6 1 27.60 10.33 17.48 Purdue Mich St 9 7 7 7 

Pastena, Victor S. 18 11 6 0 1 34.60 7.92 15.39 SUNY-Buffalo NYU 8 4 10 8 

Graham, Lynford E. 23 0 8 0 15 26.35 13.50 14.44 Penn 5 8 6 9 

Patton, James M. 15 6 1 2 6 22.85 8.08 12.94 Pittsburgh Wash U 10 10 9 10 

1977 

Ingram, Robert W. 44 13 4 9 18 64.45 22.75 34.31 Alabama Tx Tech 1 2 1 1 

Dhaliwal, Dan S. 42 17 15 9 1 73.35 19.33 33.10 Arizona Arizona 2 1 2 2 

Schipper, Katherine 26 14 6 6 0 47.20 12.53 22.35 Duke Chicago 3 3 8 3 

Prat t, Jamie H. 23 10 8 1 4 40.30 12.00 20.08 Indiana Indiana 8 5 9 4 

Ketz, J. Edward 26 3 4 6 13 32.35 14.83 19.32 Penn State Va T ech 4 7 4 5 

Jiambalvo, James J. 21 8 11 1 1 37.10 10.08 18.36 U Washington Ohio St 11 6 11 6 

Grimlund, Richard A. 17 6 7 3 1 29.35 10.42 18.24 U Washington 16 10 10 7 

Williams, Paul F. 20 2 6 1 11 24.80 13.83 18.12 N Carol St N Carol 12 17 5 8 

McEnroe, John E. 24 0 2 10 12 26.25 16.17 17.88 DePaul Kentucky 6 13 3 9 

Wolfson, Mark A. 23 12 6 3 2 42.40 9.75 17.84 Sanford Tx-Austin 9 4 14 10 

116

1978 

Wallace, Wanda A. 53 3 8 4 38 58.60 42.92 47.40 Wm & Mary Florida 2 5 1 1 

Penman, Stephen H. 34 16 15 3 0 63.35 24.17 43.62 Columbia Chicago 8 4 3 2 

Reckers, Philip M. J. 79 5 20 20 34 99.80 34.67 42.90 Arizona St Illinois 1 1 2 3 

Larcker, David F. 49 29 13 7 0 93.10 22.08 41.89 Sanford Kansas 3 2 5 4 

Shields, Michael D. 43 7 23 11 2 67.10 20.25 31.21 Michigan St Pittsburgh 4 3 7 5 

Pany, Kurt J. 38 7 8 7 16 51.60 15.67 21.58 Arizona St Illinois 6 6 8 6 

Merchant, Kenneth A. 24 4 11 4 5 35.95 14.75 21.49 So Calif Berkeley 10 11 9 7 

Hopwood, William S. 23 15 5 1 2 44.70 10.33 20.45 Fla Atlantic Florida 11 7 13 8 

Ratcliffe, Thomas A. 37 0 1 1 35 33.40 22.42 20.19 Troy Alabama 7 12 4 9 

Munt er, Paul 42 1 1 2 38 38.95 21.33 19.44 KPMG Colorado 5 8 6 10 

1979 

Wright, Arnold M. 57 6 23 13 15 79.35 30.08 42.34 Northeastern S Calif 1 1 1 1 

Raman, K. K. 38 5 12 13 8 55.30 20.17 29.99 North T exas Indiana 2 2 2 2 

Messier, William F. Jr. 29 6 17 4 2 47.45 14.25 22.93 Nev-L Vegas Indiana 3 3 3 3 

Mensah, Yaw M. 21 5 3 12 1 32.20 13.00 20.85 Rutgers-N Br Illinois 6 6 4 4 

Brownell, Peter 14 8 5 1 0 27.15 10.50 20.58 Berkeley 11 8 7 5 

117

Solomon, Ira 25 8 10 6 1 42.05 11.00 18.68 Illinois Tx-Austin 5 4 6 6 

Covaleski, Mark A. 28 0 18 6 4 39.95 11.92 17.16 Wisconsin Penn St 4 5 5 7 

Baldwin, Bruce A. 16 4 0 0 12 20.55 10.08 13.58 Ariz St-West Ariz St 9 11 8 8 

Ashton, Alison Hubbard 8 6 1 0 1 16.10 6.33 13.50 Duke Tx-Austin 24 16 15 9 

Smith, David B. 19 8 4 2 5 30.55 7.48 12.70 Nebraska Illinois 8 7 12 10 

1980 

Banker, Rajiv D. 54 17 24 6 7 88.80 22.03 35.66 Temple Harvard 1 1 1 1 

DeAngelo, Linda E. 24 8 15 0 1 44.21 13.50 24.87 S Calif U Wash 4 2 3 2 

Baber, William R. 20 11 2 6 1 35.20 10.67 18.78 George Wash N Carol 7 5 6 3 

Smieliauskas, Wally 16 5 9 2 0 28.89 10.33 18.65 Univ Toronto Wisconsin 12 10 7 4 

Holthausen, Robert W. 19 13 6 0 0 37.56 9.42 18.62 Pennsylvania Rochester 

Car 

8 4 10 5 

Evans, John H. III 24 13 3 6 2 42.65 10.00 17.85 Pittsburgh Mellon 5 2 8 6 

Bamber, E. Michael 23 3 10 3 7 32.70 11.25 16.13 Georgia Ohio St 6 6 4 7 

Left wich, Richard W. 16 10 6 0 0 31.25 7.83 15.47 Chicago Rochester 13 8 14 8 

Silhan, P eter A. 12 5 2 3 2 20.30 8.83 14.93 Illinois Tennessee 18 16 12 9 

Un Hong 

Biddle, Gary C. 16 9 5 2 0 30.05 7.67 14.68 Kong Chicago 14 9 15 10 

118

1981 

Chow, Chee W. 68 8 13 17 30 87.35 30.42 40.37 San Diego St Oregon 1 1 1 1 

Knechel, W. Robert 31 6 12 8 5 47.65 19.58 29.33 Florida N Carol 3 2 3 2 

Knight, Lee G. 47 0 0 5 42 44.90 22.33 21.38 Wake Forest Alabama 2 3 2 3 

Waller, William S. 21 10 9 1 1 38.90 11.17 21.06 Arizona U Wash 8 5 8 4 

Smith, Abbie J. 17 12 5 0 0 33.90 8.75 17.32 Chicago Cornell 12 6 12 5 

Murray, Dennis F. 21 4 5 6 6 29.50 12.00 17.27 Colo-Denver Mass 9 10 6 6 

Antle, Rick 16 9 5 2 0 30.95 8.50 16.97 Yale Stanford 14 9 13 7 

Stone, Mary S. 24 5 3 9 7 33.40 12.40 16.66 Alabama Illinois 5 7 5 8 

Robinson, John R. 26 4 10 9 3 40.05 10.25 15.96 Texas-Austin Michigan 4 4 9 9 

Burgstahler, David C. 18 11 2 3 2 32.95 8.17 14.96 U Washington Iowa 11 8 15 10 

1982 

Kaplan, Steven E. 55 2 19 21 13 72.15 27.17 36.02 Arizona St Illinois 1 1 1 1 

Palmrose, Zoe-Vonna 18 11 4 1 2 34.25 13.58 25.74 So Calif U Wash 13 7 5 2 

Lys, Thomas Z. 27 18 8 1 0 50.90 12.40 23.31 Northwestern Rochester 4 2 6 3 

Lambert, Richard A. 19 16 1 2 0 38.80 9.83 20.13 Pennsylvania Sanford 10 5 12 4 

119

Bernard, Victor L. 18 10 6 1 1 34.35 10.50 20.01 Michigan Illinois 14 6 9 5 

Stout, David E. 46 0 0 3 43 43.55 19.40 18.32 Youngstown Pittsburgh 2 3 2 6 

Pincus, Morton 18 10 4 2 2 32.80 10.50 18.16 Calif-Irvine Wash U 15 8 10 7 

Abdolmohammadi, 

Mohammad 24 1 5 7 11 28.70 14.08 17.30 Bentley Indiana 6 10 4 8 

Schneider, Arnold 20 3 2 8 7 26.40 11.58 16.44 Georgia T ech Ohio St 8 15 7 9 

Borthick, A. Faye 29 1 1 1 26 28.70 15.67 15.28 Georgia St Tennessee 3 11 3 10 

1983 

Penno, Mark C. 16 9 3 4 0 29.85 11.58 22.22 Iowa Nrthwstrn 16 10 3 1 

T se, Senyo Y. 17 7 9 1 0 31.45 8.75 16.56 Texas A&M Berkeley 12 7 8 2 

Bamber, Linda Smith 21 6 7 2 6 33.15 9.17 15.64 Georgia Ohio St 5 3 6 3 

Healy, Paul M. 18 10 5 2 1 32.50 8.58 15.57 Harvard Rochester 9 4 10 4 

Wild, John J. 16 8 5 3 0 29.85 8.33 15.54 Wisconsin Wisconsin 17 11 12 5 

Hassell, John M. 31 7 2 2 20 40.05 11.50 15.23 Indiana-Indy Indiana 2 1 4 6 

Palepu, Krishna G. 17 9 6 1 1 31.25 8.25 14.87 Harvard MIT 13 8.5 13 7 

Smith, L. Murphy 32 0 2 1 29 30.25 15.50 14.69 Texas A&M La T ech 1 8.5 1 8 

Knapp, Michael C. 15 2 3 1 9 19.00 11.00 14.40 Oklahoma Oklahoma 18 25 5 9 

Sullivan, Julie H. 19 7 7 3 2 32.45 8.50 14.37 Cal-San Diego Florida 7 5 11 10 

120

1984 

Landsman, Wayne R. 40 19 10 8 3 70.65 16.58 28.79 No Carolina Sanford 1 1 1 1 

Waymire, Gregory B. 26 17 6 3 0 51.05 13.67 27.64 Emory Chicago 3 3 2 2 

McNichols, Maureen F. 27 17 7 2 1 52.20 12.75 24.68 Sanford UCLA 2 2 3 3 

Easton, P eter 23 14 3 6 0 43.45 12.50 23.37 Notre Dame Berkeley 4 4 4 4 

Jain, Prem C. 17 6 9 0 2 30.35 10.87 20.04 Georgetown Florida 9 6 5 5 

T homas, Jacob K. 20 11 7 1 1 37.70 10.33 19.88 Yale Michigan 7 5 7 6 

Srinidhi, Bin N. 19 3 8 7 1 29.75 10.83 16.74 SUNY-Albany Columbia 8 7 6 7 

Swenson, Charles W. 16 4 5 5 2 25.65 9.50 15.11 So Calif So Calif 11 9 9 8 

Williams, David D. 17 3 7 5 2 25.95 8.50 12.99 Ohio State Penn St 10 8 11 9 

Read, William J. 23 0 2 6 15 24.55 10.12 10.72 Bentley Va T ech 5 10 8 10 

1985 

Shaw, Wayne H. 18 7 10 1 0 34.70 10.33 19.82 So Methodist Tx-Austin 6 5 4 1 

Bedard, Jean C. 31 7 13 4 7 47.60 12.17 19.38 Bentley Wisconsin 1 1 3 2 

Zarowin, Paul A. 18 10 8 0 0 34.95 10.00 19.04 New York U Chicago 7 4 5 3 

Rezaee, Zabihollah 30 2 4 2 22 32.90 18.58 18.99 Memphis Miss 2 6 1 4 

121

Strawser, Jerry R. 29 1 5 12 11 34.95 14.42 17.66 Texas A&M Tx A&M 3 4 2 5 

Datar, Srikant M. 24 11 11 0 2 43.60 8.70 15.92 Harvard Sanford 4 2 10 6 

Lipe, Marlys Gascho 13 5 4 0 4 21.65 8.33 13.58 Oklahoma Chicago 12 11 11 7 

Melumad, Nahum D. 15 7 7 0 1 27.95 7.00 13.17 Columbia Berkeley 9 7 14 8 

Pownall, Grace 13 7 4 2 0 24.45 6.17 11.88 Emory Chicago 13 8 16 9 

Chen, Kevin C. W. 13 5 4 3 1 22.45 6.67 11.44 Hong Kong Sc Illinois 14 9 15 10 

1986 

Kothari, S. P . 40 24 15 0 1 75.95 16.73 31.60 MIT Iowa 1 1 1 1 

Shevlin, Terry 30 17 10 3 0 55.20 13.33 24.70 U Washington Sanford 2 2 4 2 

King, Ronald R. 28 10 14 4 0 49.20 13.50 24.28 Wash Univ Arizona 3 3 3 3 

Krishnan, Gopal V. 22 1 9 7 5 29.85 13.58 18.70 Lehigh North T x 4 5 2 4 

Balakrishnan, Ramji 22 4 10 5 3 33.75 12.00 18.33 Iowa Columbia 5 4 6 5 

Hite, Peggy A. 22 1 6 3 12 26.65 12.58 14.79 Indiana Colorado 6 7 5 6 

O'Leary, Daniel E. 12 0 2 4 6 14.05 11.50 13.57 So Calif Case Wes 22 31 7 7 

Gaver, Jennifer J. 15 8 4 3 0 27.25 7.00 12.58 Georgia Arizona 13 6 14 8 

Viator, Ralph E. 17 0 5 3 9 19.70 10.33 12.32 Texas Tech Tx A&M 10 15 8 9 

Church, Bryan K. 20 2 5 7 6 26.40 9.00 12.12 Georgia T ech Florida 7 9 9 10 

122

1987 

Lundholm, Russell J. 20 10 8 2 0 38.00 12.25 23.01 Michigan Iowa 7 4 4 1 

Francis, Jennifer 25 22 2 1 0 52.05 10.70 22.62 Duke Cornell 4 1 6 2 

Hand, John R. M. 17 8 8 1 0 32.05 9.58 19.25 No Carolina Chicago 11 5 12 3 

DeFond, Mark L. 22 14 6 1 1 41.35 9.92 18.53 So Calif U Wash 5 3 10 4 

Cohen, Jeffrey R. 36 1 10 9 16 44.40 15.50 18.45 Boston Coll Mass 1 2 2 5 

Beneish, Messod D. 18 6 5 5 2 30.40 10.50 16.96 Indiana Chicago 10 6 7 6 

T yson, Thomas N. 26 0 0 4 22 23.85 17.33 15.86 St John Fshr Geo St 3 10 1 7 

Patten, Dennis M. 13 1 5 5 2 18.30 10.00 14.28 Illinois St Nebraska 15 17 9 8 

Bricker, Robert J. 20 2 7 5 6 27.90 10.42 14.23 Case Western Case Wes 8 7 8 9 

Roberts, Robin W. 19 1 5 2 11 22.55 10.83 12.47 Cen Florida Arkansas 9 13 5 10 

1988 

Geiger, Marshall A. 36 1 9 5 21 42.00 18.62 21.27 Richmond Penn St 1 2 1 1 

Ryan, Stephen G. 23 12 7 1 3 42.10 11.58 21.05 New York U Sanford 2 1 2 2 

Bonner, Sarah E. 19 11 5 2 1 36.05 9.33 16.92 So Calif Michigan 4 4 5 3 

Sivaramakrishnan, K. 21 9 8 4 0 37.60 9.42 16.52 Houston Nrthwstrn 3 3 4 4 

123

Kaplan, Steven N. 14 0 14 0 0 25.20 9.33 16.47 Chicago Harvard 12 7 6 5 

Ali, Ashiq 18 10 6 2 0 33.80 7.83 14.85 Texas-Dallas Columbia 5 5 9 6 

Kachelmeier, Steven J. 18 7 6 3 2 30.85 8.92 14.71 Texas-Austin Florida 6 6 7 7 

Clinch, Gregory J. 13 9 2 2 0 24.95 7.00 13.20 U Melbourne Stanford 16 8 12 8 

Ely, Kirsten M. 11 5 4 2 0 20.15 6.67 12.67 Sonoma State Chicago 24 17 15 9 

Roberts, Michael L. 15 0 5 4 6 18.40 8.83 11.34 Colo-Denver Geo St 8 22 8 10 

1989 

Skinner, Douglas J. 30 19 9 1 1 56.45 18.32 33.60 Chicago Rochester 3 2 2 1 

Barth, Mary E. 40 23 9 6 2 73.75 18.25 33.35 Sanford Sanford 1 1 3 2 

Fogart y, T imothy J. 37 0 7 6 24 40.20 19.50 21.53 Case Western Penn St 2 3 1 3 

Bartov, Eli 19 11 8 0 0 36.95 10.00 19.67 New York U Berkeley 5 6 5 4 

Khurana, Inder K. 24 6 8 5 5 37.50 9.89 15.77 Missouri Ariz St 4 4 6 5 

Bushman, Robert M. 18 14 4 0 0 37.05 7.50 15.57 No Carolina Minnesota 6 5 11 6 

Ponemon, Lawrence A. 16 1 7 1 7 20.95 11.33 14.73 Union-NY 8 11 4 7 

Indjejikian, Raffi J. 15 12 3 0 0 30.80 6.92 14.44 Michigan Penn 10 7 13 8 

Frost, Carol A. 12 6 3 2 1 21.35 7.83 14.03 North T exas Michigan 15 10 9 9 

Warfield, Terry D. 18 5 1 5 7 25.00 8.08 11.17 Wisconsin Iowa 7 8 7 10 

124

1990 

Sansing, Richard C. 28 10 14 4 0 49.55 18.14 32.17 Dartmouth Tx-Austin 3 4 2 1 

Raghunandan, K. 53 3 21 15 14 70.95 21.17 28.53 Fla Internat Iowa 1 1 1 2 

Rajan, Madhav V. 26 17 8 1 0 52.40 12.33 25.25 Sanford Car Mellon 4 2 3 3 

Nelson, Mark W. 26 14 10 2 0 49.80 11.58 21.94 Cornell Ohio St 5 3 4 4 

Shackelford, Douglas A. 23 14 6 3 0 43.95 10.17 19.70 No Carolina Michigan 6 5 6 5 

Lee, Charles M. C. 20 7 11 1 1 37.95 9.83 18.69 Sanford Cornell 9 9 7 6 

Lang, Mark H. 21 15 5 1 0 42.55 9.08 18.44 No Carolina Chicago 8 7 10 7 

Guenther, David A. 16 11 3 1 1 30.55 9.67 18.38 Oregon U Wash 12 12 8 8 

Koonce, Lisa 22 8 10 3 1 38.15 9.50 16.54 Texas-Austin Illinois 7 8 9 9 

Sridhar, Sri S. 16 9 6 1 0 30.70 8.33 16.21 Northwestern Pittsburgh 13 11 12 10 

1991 

Amir, Eli 16 7 6 3 0 29.15 8.33 15.69 London Bus Berkeley 3 2 5 1 

Young, Joni J. 16 0 8 0 8 19.70 12.33 15.31 New Mexico Illinois 4 10 1 2 

Balsam, Steven 20 4 6 6 4 29.30 10.67 14.83 Temple Baruch 1 1 3 3 

Ghosh, Dipankar 16 0 3 11 2 20.25 11.50 14.33 Oklahoma Penn St 5 9 2 4 

125

Huddart , Steven J. 13 7 4 2 0 22.70 7.83 13.33 Penn State Yale 9 5 7 5 

Krishnan, Jagan 14 2 7 5 0 21.65 6.67 10.84 Temple Ohio St 7 6 10 6 

T utt le, Brad M. 17 0 8 6 3 22.80 8.00 10.72 So Carolina Ariz St 2 4 6 7 

Wahlen, James M. 13 7 4 1 1 24.30 5.33 10.17 Indiana Michigan 10 3 

16. 

17 8 

Cullinan, Charles P. 14 0 1 5 8 15.10 9.08 9.76 Bryant Kentucky 8 5 4 9 

Ramsay, Robert J. 15 1 5 6 3 21.35 6.58 9.52 Kentucky Indiana 6 8 13 10 

1992 

Sloan, Richard G. 28 20 8 0 0 55.45 12.83 25.55 Cal-Berkeley Rochester 1 1 1 1 

Ittner, Christopher D. 21 10 7 4 0 37.75 9.25 16.22 Pennsylvania Harvard 3 2 4 2 

Beatty, Anne 18 12 4 1 1 34.00 8.83 16.16 Ohio State MIT 4 3 6 3 

Cloyd, C. Bryan 17 5 7 4 1 28.80 8.83 15.53 Virg Tech Indiana 6 6 7 4 

Luft, Joan L. 16 5 5 6 0 25.90 8.92 14.10 Michigan St Cornell 7 8 5 5 

Gigler, Frank B. 13 12 1 0 0 28.35 6.25 13.72 Minnesota Minnesota 10 7 11 6 

Lowe, D. Jordan 27 1 6 9 11 32.70 10.31 12.30 Arizona St Ariz St 2 4 2 7 

Hirst, D. Eric 13 8 3 1 1 24.85 6.17 11.87 T exas-Austin Minnesota 11 9 12 8 

Kennedy, S. Jane 12 9 2 1 0 24.60 5.42 11.39 U Washington Duke 14 10 14 9 

Glover, Jonathan C. 18 5 10 2 1 30.30 6.62 11.34 Carnegie Mel Ohio St 5 5 9 10 

126

1993 

Hermanson, Dana R. 46 0 10 13 23 52.65 16.92 18.92 Kennesaw St Wisconsin 1 1 1 1 

Subramanyam, K. R. 18 12 4 2 0 34.85 8.42 16.53 So Calif Wisconsin 2 2 3 2 

Dechow, Patricia M. 18 10 8 0 0 34.30 7.58 14.52 Cal-Berkeley Rochester 3 3 4 3 

Rees, Lynn L. 17 4 6 3 4 26.55 9.17 13.32 Texas A&M Ariz St 4 4 2 4 

Salterio, Steven E. 15 3 12 0 0 25.95 7.20 12.28 Queen's Univ Michigan 5 5 5 5 

Anderson, Shannon W. 11 2 5 4 0 17.10 6.17 9.72 Rice Harvard 9 9 8 6 

Barron, Orie E. 12 8 4 0 0 24.40 4.67 9.53 Penn State Oregon 6 6 13 7 

Prawitt, Douglas F. 12 3 4 2 3 18.45 4.83 8.00 Brigham Yg Arizona 7 8 12 8 

Frankel, Richard M. 10 9 1 0 0 20.60 3.83 7.87 Wash Univ Sanford 13 7 21 9 

Spilker, Brian C. 10 3 3 1 3 15.65 4.33 7.38 Brigham Yg Tx-Austin 14 13 17 10 

1994 

Hunton, James E. 60 7 8 12 33 74.25 30.08 35.49 Bentley Tx-Arlin 1 1 1 1 

Vafeas, Nikos 15 1 6 7 1 21.85 11.42 16.98 Cyprus Kansas 5 7 2 2 

Maydew, Edward L. 18 12 3 3 0 35.00 7.83 15.40 No Carolina Iowa 4 2 5 3 

Dutta, Sunil 13 8 5 0 0 26.00 6.58 13.35 Cal-Berkeley Minnesota 6 5 7 4 

127

Beasley, Mark S. 21 2 6 2 11 26.85 8.33 11.55 N Carol St Mich St 2 4 3 5 

Wilkins, Michael S. 19 2 9 5 3 28.25 7.83 11.53 Texas A&M Arizona 3 3 4 6 

Schrand, Catherine M. 11 4 6 1 0 20.70 5.75 11.38 Pennsylvania Chicago 15 8 13 7 

Hogan, Chris E. 11 4 7 0 0 19.95 5.92 11.12 Michigan St Ohio St 16 9 11 8 

Boone, Jeff P. 13 1 3 4 5 17.20 6.58 9.32 Tx-S Antonio North T x 7 14 8 9 

Un Hong 

Park, Chul W. 10 6 3 1 0 18.65 4.83 8.99 Kong Wash U 20 12 20 10 

1995 

T homas, Wayne B. 23 7 3 9 4 35.35 9.67 15.10 Oklahoma Okla St 1 1 1 1 

Aboody, David 15 11 4 0 0 30.55 7.08 14.57 UCLA Berkeley 4 2 3 2 

Kasznik, Ron 15 11 4 0 0 30.15 6.42 13.10 Sanford Berkeley 5 4 6 3 

Core, John E. 16 9 7 0 0 30.35 6.67 12.76 Pennsylvania Penn 3 3 5 4 

Walther, Beverly R. 15 8 6 1 0 28.50 6.00 11.57 Northwestern Chicago 6 5 10 5 

Dichev, Ilia D. 10 6 2 2 0 19.50 6.00 11.33 Emory U Wash 17 10 11 6 

DeZoort, F. T odd 17 0 8 7 2 23.25 7.50 10.35 Alabama Alabama 2 6 2 7 

Hopkins, Patrick E. 13 7 2 1 3 23.20 5.50 10.07 Indiana Tx-Austin 10 7 12 8 

Payne, Jeff L. 11 2 7 1 1 17.95 6.08 9.45 Kentucky Florida 15 13 9 9 

Botosan, Christine A. 8 5 1 2 0 15.15 4.83 9.01 Utah Michigan 27 18 19 10 

128

1996 

Mills, Lillian F. 20 5 8 7 0 33.00 10.39 17.27 Texas-Austin Michigan 1 2 1 1 

Erickson, Merle M. 19 11 6 2 0 36.05 8.08 15.57 Chicago Arizona 2 1 4 2 

Venkatachalam, Mohan 15 10 5 0 0 30.00 6.75 13.39 Duke Iowa 6 4 8 3 

Ayers, Benjamin C. 18 5 11 2 0 31.50 6.98 12.56 Georgia Tx-Austin 3 3 7 4 

Narayanan, V. G. 8 4 4 0 0 15.15 5.83 11.48 Harvard Sanford 19 12 10 5 

Phillips, Fred 18 1 0 0 17 19.15 10.00 11.21 Saskat chewan Tx-Austin 4 8 2 6 

Sprinkle, Geoffrey B. 13 4 4 3 2 20.85 6.25 10.29 Indiana Iowa 8 6 9 7 

Kadous, Kathryn 11 4 7 0 0 20.20 5.33 9.83 Emory Illinois 9 7 12 8 

Willenborg, Michael 10 9 1 0 0 21.10 4.50 9.51 Connecticut Penn St 11 5 15 9 

Hayes, Rachel M. 9 6 3 0 0 17.55 4.50 8.86 Utah Sanford 15 9 17 10 

1997 

Johnstone, Karla M. 19 4 6 4 5 28.10 8.33 12.62 Wisconsin Conn 1 1 1 1 

Bushee, Brian J. 

10 9 1 0 0 21.35 5.50 11.52 Pennsylvania Michigan 13 5 9 2 

Marquardt, Carol A. 11 6 3 2 0 20.80 5.67 10.78 

129 

CUNY-Baruch Cornell 9 6 7 3 

Mayhew, Brian W. 13 5 8 0 0 24.05 5.33 10.16 Wisconsin Arizona 5 2 10 4

Lehavy, Reuven 11 4 6 0 1 19.90 5.58 9.93 Michigan Nrthwstrn 10 8 8 5 

Nelson, Karen K. 10 8 2 0 0 20.70 4.67 9.90 Rice Michigan 14 7 14 6 

Skaife, Hollis 13 7 1 2 3 22.30 6.17 9.78 Wisconsin Iowa 4 4 5 7 

Leone, Andrew J. 12 9 2 1 0 23.35 4.75 8.75 U Miami Pittsburgh 7 3 13 8 

Mauldin, Elaine G. 11 2 1 3 5 14.55 6.92 8.19 Missouri Nebraska 8 14 2 9 

Wong, M. H. Franco 9 6 3 0 0 17.80 4.08 8.18 Univ Toronto Penn 18 9 23 10 

1998 

Guay, Wayne R. 19 10 8 1 0 35.35 9.17 16.95 Pennsylvania Rochester 2 2 1 1 

Rajgopal, Shivaram 20 12 7 0 1 37.95 7.75 15.06 U Washington Iowa 1 1 2 2 

Zhang, Xiao-Jun 12 9 2 1 0 24.30 6.00 12.18 Cal-Berkeley Columbia 6 3 5 3 

Davila, Antonio 14 3 9 1 1 22.90 7.25 11.85 Univ Navarra Harvard 4 4 3 4 

Smith, Michael J. 8 2 5 1 0 13.65 6.00 10.12 Boston Univ Stanford 18 16 6 5 

Miller, Gregory S. 8 7 1 0 0 17.10 4.33 9.34 Michigan Michigan 19 10 17 6 

Krishnan, Ranjani 10 7 2 1 0 19.85 4.42 8.72 Michigan St Pittsburgh 9 5 15 7 

Hatfield, Richard C. 13 3 4 2 4 19.15 6.17 8.70 Alabama Florida 5 7 4 8 

Liang, Pierre Jinghong 7 4 2 1 0 13.10 4.83 8.35 Carnegie Mel Florida 26 17 10 9 

Nissim, Doron 10 2 7 0 1 17.50 4.50 7.94 Columbia Berkeley 10 9.5 14 10 

130

1999 

Nagar, Venky 12 8 2 2 0 23.20 6.83 13.36 Michigan Penn 3 2 1 1 

Ke, Bin 14 11 3 0 0 28.30 6.33 12.93 Penn State Mich St 1 1 3 2 

Piot roski, Joseph D. 9 6 2 1 0 18.35 4.67 9.59 Sanford Michigan 6 4 4 3 

Liu, Jing 12 6 5 0 1 22.50 4.42 8.28 UCLA Columbia 4 3 7 4 

Kohlbeck, Mark J. 13 0 4 2 7 15.55 6.58 7.93 Fla Atlantic Tx-Austin 2 8 2 5 

Lo, Kin 7 6 1 0 0 13.85 4.00 7.93 British Colu Nrthwstrn 12 9 11 6 

Gu, Zhaoyang 7 4 2 1 0 12.80 4.33 7.61 Minnesota Tulane 13 13 9 7 

Hung, Mingyi Y. 7 5 2 0 0 13.70 3.67 7.14 So Calif MIT 14 11 12 8 

Wu, Joanna Shuang 8 7 0 0 1 15.70 3.50 6.81 Rochester Tulane 10 7 14 9 

Widener, Sally K. 9 1 4 3 1 12.85 4.67 6.80 Rice Colorado 7 12 5 10 

2000 

Bradshaw, Mark T . 8 6 1 1 0 16.00 5.00 9.74 Boston Coll Michigan 7 4 3 1 

Roulstone, Darren T . 6 4 1 1 0 11.50 5.00 9.38 Ohio State Michigan 13 12 4 2 

Yetman, Robert J. 10 3 3 4 0 16.70 5.48 9.20 Cal-Davis N Carol 2 3 2 3 

Weber, Joseph P. 9 9 0 0 0 19.25 4.00 8.50 MIT Penn St 5 1 10 4 

131

Hodge, Frank D. 10 4 5 1 0 18.15 4.75 8.40 U Washington Indiana 3 2 5 5 

Sedatole, Karen L. 9 3 2 2 2 14.10 4.08 6.68 Michigan St Michigan 6 5 9 6 

Wilks, T. Jeffrey 8 2 3 3 0 12.75 3.75 6.49 Brigham Yg Cornell 8 7 12 7 

Mueller, Jennifer M. 12 0 0 3 9 11.80 6.42 6.33 Auburn Va T ech 1 11 1 8 

Gore, Angela K. 4 2 0 2 0 7.20 3.50 6.07 George Wash SUNY-Buf 29 25 14 9 

Robinson, Dahlia 8 1 3 1 3 10.80 4.50 5.98 South Fla Georgia 9 13 7 10 

2001 

Hope, Ole-Kristian 15 3 4 6 2 22.85 7.67 11.55 Univ Toronto Nrthwstrn 1 1 1 1 

Yee, Kenton K. 8 0 6 1 1 11.95 7.33 10.88 Sanford 6 7 2 2 

Louis, Henock 7 4 3 0 0 13.05 4.50 8.58 Penn State Ohio St 9 4 6 3 

Mercer, Molly 6 3 1 2 0 10.40 3.67 6.26 Arizona St Tx-Austin 11 9.5 8 4 

Krull, Linda K. 6 5 1 0 0 12.60 2.92 6.26 Oregon Arizona 12 5 13 5 

Parsons, Linda M. 8 1 1 3 3 10.25 4.67 5.63 Alabama Houston 7 12 5 6 

Hodder, Leslie D. 9 5 2 1 1 16.60 3.08 5.57 Indiana Tx-Austin 4 2 12 7 

Myring, Mark J. 11 1 1 1 8 12.35 5.08 5.35 Ball State Kent St 2 6 3 8 

Janvrin, Diane J. 10 0 1 2 7 10.40 4.83 4.92 Iowa State Iowa 3 9 4 9 

Johnston, Derek 5 0 2 3 0 7.25 3.25 4.76 Colorado St Colorado 18 20 11 10 

132

2002 

Cheng, Qiang 9 5 3 0 1 16.95 5.00 9.61 Wisconsin Wisconsin 3 2 3 1 

Mittendorf, Brian 11 5 3 2 1 18.55 5.58 9.40 Ohio State Ohio St 2 1 2 2 

Hilary, Gilles 6 4 1 0 1 11.05 3.67 6.09 HEC Paris Chicago 7 5 6 3 

Kimbrough, Michael D. 4 4 0 0 0 9.00 2.67 6.00 Indiana Harvard 15 10 12 4 

T owry, Kristy L. 7 3 3 0 1 12.40 2.95 5.72 Emory Tx-Austin 5 4 10 5 

Hayes, David C. 13 0 0 0 13 12.45 5.67 5.47 Jms Madison S Fla 1 3 1 6 

Krische, Susan D. 5 4 1 0 0 11.00 2.25 5.00 Illinois Cornell 9 6 13 7 

Hofmann, Mary Ann 5 0 2 2 1 6.65 3.75 4.84 Appalach St Ariz St 10 16 4 8 

Markov, Stanimir 5 5 0 0 0 10.50 2.17 4.54 Texas-Dallas Rochester 11 7 14 9 

Farber, David B. 4 3 0 1 0 7.90 2.17 4.51 Missouri Cornell 16 13 15 10 

2003 

Richardson, Scott A. 17 8 7 2 0 31.40 7.58 12.92 Barclays Michigan 1 1 1 1 

Hanlon, Michelle 10 7 0 3 0 18.85 5.00 9.10 MIT U Wash 3 3 2 2 

Li, Oliver Zhen 12 5 6 1 0 21.85 4.92 8.72 Arizona Arizona 2 2 3 3 

Soliman, Mark T . 8 5 3 0 0 15.55 3.17 6.33 U Washington Michigan 5 4 8 4 

133

Chen, Shuping 7 4 3 0 0 13.55 2.50 4.89 Texas-Austin S Calif 6 6 10 5 

Zhang, Yuan 3 3 0 0 0 6.25 2.33 4.75 Columbia S Calif 22 16 13 6 

Hales, Jeffrey W. 4 2 2 0 0 7.70 2.33 4.71 Georgia T ech Cornell 15 12 11 7 

T una, Irem 7 5 2 0 0 14.20 2.17 4.38 London Bus Michigan 7 5 15 8 

Kang, Tony 7 1 3 2 1 10.65 3.08 4.33 Oklahoma St Illinois 8 8 9 9 

Brandon, Duane M. 10 0 2 1 7 10.75 3.92 4.16 Auburn Va T ech 4 7 4 10 

2004 

McVay, Sarah E. 6 5 1 0 0 12.35 2.67 5.62 Utah Michigan 5 2 5 1 

Roychowdhury, Sugata 4 4 0 0 0 8.50 2.50 5.25 MIT Rochester 13 7 9 2 

Brazel, Joseph F. 8 2 2 0 4 11.15 3.83 4.84 N Carol St Drexel 1 4 2 3 

Sandino, T atiana 5 3 0 0 2 8.60 2.53 4.79 So Calif Harvard 8 6 7 4 

Blouin, Jennifer L. 7 4 2 1 0 13.55 2.42 4.78 Pennsylvania N Carol 2 1 10 5 

Wang, Dechun 4 2 2 0 0 7.70 2.33 4.60 Texas A&M Missouri 14 8 13 6 

Cohen, Daniel A. 6 4 2 0 0 11.45 2.42 4.58 New York U Nrthwstrn 6 3 11 7 

Lin, Haijin H. 7 1 2 4 0 10.45 2.73 4.19 Houston Car Mellon 3 5 

14. 

4 8 

T ucker, Jennifer W. 3 3 0 0 0 6.75 1.83 4.12 Florida NYU 18 5 17 9 

Srinivasan, Suraj 3 3 0 0 0 6.75 1.83 4.12 Harvard Harvard 19 14. 18 10 

134

2005 

LaFond, Ryan 9 8 0 1 0 18.85 2.92 6.19 Industry Wisconsin 1 1 1 1 

Rogers, Jonathan L. 4 4 0 0 0 8.50 2.33 5.04 Chicago Penn 4 2 3 2 

Sadka, Gil 4 2 2 0 0 7.70 2.17 4.33 Columbia Chicago 5 3 5 3 

Jiang, John (Xuefeng) 3 2 1 0 0 6.10 1.67 3.53 Michigan St Georgia 10 9 14 4 

Dey, Aiyesha 3 3 0 0 0 6.50 1.58 3.50 Chicago Nrthwstrn 11 6 15 5 

Williamson, Michael G. 4 1 2 0 1 6.45 2.17 3.38 Texas-Austin Indiana 6 7 

18. 

6 6 

Wynn, Jinyoung P. 2 2 0 0 0 4.25 1.50 3.25 Houston Purdue 23 5 17 7 

T ang, Vicki Wei 4 2 2 0 0 7.45 1.67 3.19 Georgetown Michigan 7 5 

16. 

12 8 

Li, Feng 2 2 0 0 0 4.25 1.50 3.12 Michigan Chicago 24 5 

23. 

18 9 

Cassar, Gavin J. 2 1 1 0 0 3.85 1.50 3.05 Pennsylvania Berkeley 25 5 19 10 

5. Full credit refers to giving equal credit for each publication in the best 40 journals. 

6. Q1 denotes the number of articles adjusted for journal quality per Exhibit 1. 

7. Q2 denotes the number of articles adjusted for co-authorship (if n authors, then each author receives 1/n credit). 

8. Q1&Q2 denotes the product of Q1 and Q2, i.e., considering both artilces adjusted for journal quality and adjusted for co-authorship. 

135 

5



May 23 and 24, 2012 

EXHIBIT 7 

CORRELATION MATRIX OF PRODUCTIVITY MEASURES 

(Spearman Rank Correlations of Non-zero Values, N=3,812) 

Full credit 

Q1 Adjusted 

Full credit 1.000 

Q1 Adjusted 0.971 1.000 

136 

Q2 Adjusted 

Q2 Adjusted 0.956 0.928 1.000 

Q1&Q2 

Adjusted 

Q1&Q2 Adjusted 0.935 0.963 0.971 1.000 

All Spearman correlation coefficients are significant at


Teaching Years since Doctorate: 10 or less years since doctorate = 1, 11 years or 

more = 0 

School* Teaching Years since Doctorate: Interaction term between School and 

Years since Doctorate 

137



May 23 and 24, 2012 

The Usefulness of Activity Based Costing and 

Traditional Costing in Banking Industry: Evidence 

from a Bank in Iran 

Nazemi A � . 

Assistant Professor of Shiraz University 

Eram Sq., Eram Paradise, College of Economics, Management and Social 

Sciences, Department of Accounting 

Seyedi A. 

Abstract 

Shandiz Higher Education Institute, Mashad 

In this study the results of implementation of two costing systems are 

illustrated. These systems are Activity Based Costing (ABC) and 

Traditional Costing. In order to analyze the cost structure for the bank 

services, an Iranian bank (Mehr bank) is chosen as a sample. After 

explaining the calculation methods of unit costs of the systems, a 

statistical pair-wise comparison is done to find the probably usefulness of 

the information. The results show that there is no difference between the 

unit costs of two systems. Therefore it suggests doing more research to 

explain the reasons of insignificant differences between ABC and 

traditional systems. 

Keywords: Activity Based Costing, Traditional Costing, Mehr Bank 


*Corresponding Author: anazemi@rose.shirazu.ac.ir


1. Introduction 

After reducing direct material and labor costs in the mid-1970s, 

organizations recognized that antiquated information systems were not 

meeting their ever-changing management information needs. Many 

companies embarked on costing initiatives to focus on indirect 

product and service costs. By assigning overhead costs, companies 

were able to clearly identify, improve, or divest unprofitable products, 

inefficient processes, and poorly performing regions. 

There are two approaches in cost determination in accounting 

systems: Transitional and Activity Based Costing (ABC). The 

traditional method allocates some costs incurred in departments and 

uses a few cost drivers to allocate overhead costs. This cause 

sometimes miss-allocation and error in decisions. On the other hand 

ABC improves the precision of cost allocation by increasing the 

number of cost drivers and concentrate to all stages in a life cycle of 

products or services. 

There are some difficulties in determining service centers such as 

banks and financial institutions, because the specification of units 

object is more discretionary than in manufacturing units. In this article 

the calculation of cost of services is considered in banking industry. 

The article is organized as follows: Section 2 describes theoretical 

background of the ABC philosophy. Section 3 contains the literature 

review in Iranian banks. Section 4 describes the methodology 

overview and hypothesis. Section five presents the unit cost 

components for the banks products and compare the result in two 

approaches. Finally, section 6 draws conclusions of the results. 

The value added concept drives costs in banking industry. It is 

expected that each activity (for example open accounts or money 

orders) drives value. Some costs are an integral part of value added 

process when providing services to costumers. Some supportive 

activities are needed for service providing. IT related costs for internet 

139



May 23 and 24, 2012 

banking is an example of supportive activity. The determination of 

cost of services is one of the most strategies for managers in banking 

industry. The information influences the net income numbers. Sapp et 

al. [1] argued that three factors influences service fees in banks: costs, 

customers and competition. The costs of services include various 

items that are fixed or variable in nature. Some items also have fix or 

variable nature. The determination of costs of services provides a 

basis for determining profitable services and bank fees that can be 

received from customers. This subject has also various implications in 

privatization and improvement of competitive advantages [2]. 

Activity-Based Costing (ABC) is an accurate method of assigning 

costs to work activities, processes, products/services, customers, and 

lines of business. It is based on the notion that efforts required to 

produce products and services can be quantified and, therefore, 

assigned to the product or service. 

Kaplan and Cooper [3] and Johnson [4] introduced ABC as a new 

dimension of cost analysis. It has been developed in many 

organizations such as service industries. ABC addressed deficiencies 

in the traditional cost accounting. ABC is a cost measurement system 

that provides the cost of each product, service or customer by 

analyzing each activity needed to produce a product or service for the 

costumer. 

ABC allocates the joint costs in two stages. First, the cost of 

activities is determined in this stage. Include: [5] 

1. The investigation of service and non service costs. 

2. The determination of activities related to provide services. 

3. The determination of number of activity centers to collect data. 

4. The calculation of costs related to each activity center. 

Second, the allocation of costs related to each activity center is 

done. To separate services this stage includes: 

1. The determination of various service and non service basis to 

allocate costs. 

140


2. The assignment of costs of activity centers to various objects 

and the determination of overhead rate, and 

3. The allocation of applied over head rate to the services. 

The two-dimensional ABC model structure is presented in figure 

1. The cost assignment view is constructed from 3 main building 

blocks: [6] 

1. Resources are economic elements that are applied or directed to 

the performance of activities. They are the sources of costs. 

2. Resources flow to activities, which are processes or procedures 

that cause work to be performed in an organization. 

a. Various factors, which are referred to as resource drivers, are 

used to assign the costs of resources to activities. 

3. Cost object is any activity, organizational units or other work 

unit for which a separate measurement of cost is desired. 

Figure 1. The two-dimensional ABC model structure [6] 

3. Literature review 

3.1. Domestic Evidences 

There are a few evidences about ABC application in Iranian banks. 

Obviously, no banks have reported the establishment of cost systems 

141



May 23 and 24, 2012 

that show the sufficient information about the structure of costs and 

banks services. Therefore it is necessary to suggest an efficient cost 

system that is based on the last new advances in cost reporting 

methods and new technologies. 

Arabmazar and Naseri [7] reported some evidence of 

implementing ABC in Refah bank. They calculate the costs of short- 

term and long- term deposits and showed that the short-term and zerointerest 

deposits (also named Quard- Hassan in Islamic based Iranian 

banks) have more actual costs than expected based on ABC approach. 

Anvari Rostami and Rezayat [8] compared the cost of three 

Islamic loans in Iranian Export Development Bank(IEDB) and 

showed that traditional based system allocate the costs of products 

based on improper cost drivers, and the products will appear less or 

more expensive than actually they are. 

Namazi and Nazemi[9] computed and compared the cost per 

transaction unit in Electronic banking at Iranian Agricultural Bank 

using two costing systems(traditional and ABC). They found that the 

there is a significant difference between the results of two cost units 

based on the two systems. They assessed the results by distributing the 

results through the financial managers of the bank and found the 

similar results. 

3.2. Foriegn Evidences 

There are some evidences related to implementation of the ABC in 

banking industry. 

Kaplan and Anderson [10] introduced the second version of ABC 

that they called Time-Driven Activity Based Costing (TDABC). In 

contrast with traditional ABC systems, TDABC has at least five 

significant differences: [11] 

1. Time is the primary cost driver for cost object. 

2. TDABC eliminates the first step of the traditional ABC 

implementation process: the determination of different activities. 

142


3. TDABC simplifies the costing process by eliminating the need 

to interview and survey employees for allocating resource costs to 

activities before deriving them down to costs objects. 

4. Unlike conventional ABC, TDABC also determines unused 

capacity by basing the predetermined overhead costs rate upon the 

practical capacity 

5. TDABC can accommodate complexities of actual production or 

services. 

Kaplan and Anderson [10] showed two cases (ATB and Comption) 

in financial services that use TDABC and improve their profitability. 

However there are some evidences that the rate of implementation is 

not high in some countries, because of unclear objectives, inadequate 

training ABC, and weakness in designing the system, weak 

participation and personnel resistance. [11] 

In this study the aim is to report the evidence of ABC versus 

Traditional costing applications in banking and the usefulness of the 

information for making decisions. 

4. Methodology and hypothesis 

In order to focus on the costing implementation issues, a case 

study of Iranian Banks is chosen. There are 23 active banks in Iran. 

Most of these banks are governmental but the number of private banks 

has been increased in recent years. We chose the Mehr bank, because 

the availability of the information. It is a bank established sine 2008 

particularly for Islamic banking. Islamic banking refers to a system of 

banking or banking activity that is consistent with Islamic law 

(Shariah) principles. Islamic banks use the same tools and procedures 

as traditional (i.e. non-Islamic modern banks) in those areas where 

there is no conflict between banking operation and Islamic principles 

[12]. The major part of the bank is to provide services as Good Loan 

(Qard Hassan). This is a loan by which a borrower is obliged to repay 

143



May 23 and 24, 2012 

the lender only the principal sum borrowed on the loan. The bank is 

chosen because it is the firs bank that establish only for providing pure 

Islamic banking. It has 300 branches in Iran, 50 branches is at 

Khorasan razavi province. We chose 14 branches of the province 

based on the availability of information. 

We argue that the results of the costing systems may differ because 

of different drivers used to allocate the indirect costs between cost 

objects. Therefore the hypothesis is mentioned as follows: 

Hypothesis: There is a significant difference between the results of 

Traditional versus ABC for the products. 

To calculate unit costs, the information is collected from financial 

general ledger and the IT department of the bank. There are five major 

categories to provide the services based on the information. These are 

personnel, administrative, deposit interests, bad debts expenses and 

overhead costs. Some of the costs are traceable to the products. In this 

study the products are categorized into two major parts: loans and 

deposits. The deposits also divided into four groups (long term, short 

term, Qard Hassan and other current deposits). These are deposit 

interests and bad debt expenses. Other expenses including personal 

and administrative expenses should be allocated to the products. To 

allocate the indirect costs we chose two bases: one is the traditional 

costing method and the other is Activity based Costing methods. We 

calculate the cost of the products using traditional costing methods 

using the average balances of the deposits and loans (as suggested 

before in the Iranian studies). But when we tried to allocate the 

indirect costs based on the ABC method, we consider the percentage 

of the costs attributed to the cost objects. Since the major part of the 

indirect costs is related to personnel, we try to differentiate the share 

of the products from the costs based on the observation and 

approximation of the provided services. Next, we allocated the 

reminding costs based the percentages calculated from the other costs. 

Because of the competitive environment we just reported the 

144


comparative results and avoided to show the cost per unit information. 

Table 1 show the results of the information relating to the relative 

importance of the costs per each service provided. 

Table 1-the costs descriptive based on the behavior (direct/indirect) in 

percent of total costs 

Direct 

Costs 

Indire 

ct Costs 

Loan 

s 

12 

Curre 

nt 

Deposits 

- 

145 

Quard 

Hassan 

Deposits 

- 

Short 

term 

Deposits 

55 

Long 

term 

Deposits 

As the table shows the major part of the costs are traceable to the 

products. However 16 percent of the costs is not traceable (including 

personnel and administrative). We try to examine the results of the 

two traditional and ABC methods to allocate these costs. 

The result of the allocation is reported in table 2. In the first part 

the results of the traditional method is reported. In the second part the 

ABC method implemented and the results is reported. To compare the 

results the pair wise t statistics is used and the results is reported in the 

third section of the table 3. There are not significant differences 

between the two mean samples. Pearson correlation coefficient shows 

that the null hypothesis is not rejected at the %95 significant level. 

Table 2-the allocated costs in traditional and ABC methods, in percent of 

5. Conclusion 

The results show that there is no significant difference between the 

traditional and ABC results. Therefore the null hypothesis is not 

rejected. There are some possible reasons for these results. Most of the 

expenses in banking industry are related to interest paid to costumers. 

May be it reduces the importance of other bank costs that are 

7 

9 

17



May 23 and 24, 2012 

examined in this article. In this article 14 branches are selected as 

samples. Although the samples are good cased to calculate the unit 

costs, but increasing the number of samples can provide more useful 

results and extend to other branches of the bank. total costs 

First part: Traditional Method 

Direct 

Costs 

Indirect 

Costs 

Loans 

12 

3.45 

Second part: ABC Method 

Direct 

Costs 

Indirect 

Costs 

Loans 

12 

2.8 

Third Part: Comparison 

ABC VS 

Traditional 

Pair 

Comparisons 

Current 

Deposits 

- 

0.087 

Current 

Deposits 

- 

0.003 

146 

Quard 

Hassan 

Deposits 

- 

0.75 

Quard 

Hassan 

Deposits 

- 

0.8 

Correlation Coefficient 

Mean 

0.28 

0.7 

STED 

0.31 

Short 

term 

Deposits 

55 

9.37 

Short 

term 

Deposits 

t 

2 

55 

8.3 

Sig. 

0.1 

Long 

term 

Deposits 

17 

1.903 

Long 

term 

Deposits 

Sig. 

17 

1.65


References: 

Sapp, R., Crawford, D. and Rebischke S. Activity Based 

Information for Financial Institutions, Journal of Performance 

Management, Vol. 18, No. 2, (2005) 22-34. 

Mays, J. and Sweeney, R. Activity Based Costing in Banking: A 

Case Study, CMA Magazine, May, Vol. 68, No. 4, (1994)19-25. 

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Right Decision, Harvard Business Review, September-October, 

(1988) 96-103. 

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(1998) 23-30. 

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(1999) 71-106. 

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Tartu University Press, 2004. 

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a Deposit Cost Model Using ABC: Evidence from Refah Bank, 

Accounting and Auditing Review, 34 (2004) 3-26. 

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Islamic Bank Loans Using ABC and Traditional Methods: Evidence 

from Export Development Bank in Iran, Accounting and Auditing 

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Harvard Business School Press, (2007) First Ed. 

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Namazi, M. Designing the Third Version of the Activity Based 

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Directory of Islamic Financial Institutions, (1988) London, Croom 

Helm. 

148



May 23 and 24, 2012 

Empirical Examination of the Relationship Between 

"Intellectual Capital" with " the Operating Cash Flow 

and Weighted Average Cost of Capital" 

Ali Asgar Anvary Rostamy � 

Associate Professor, Tarbiat Modares University (TMU) 

Chair/Head Department of Accounting, Tarbiat Modares University (TMU) 

Ramin Zamani Amoghin 

Abstract 

Master's Degree of Accounting 

Mostafa Deljou 

Master's Degree of Accounting 

Nowadays, in order to maximize achieving goals and strategies, 

organizations have to identify, measure and manage their Intangible 

Assets (IA). Intellectual Capital (IC) is considered as one of the most 

important parts of organizations' IA. On the other hand, considering the 

fact that corporations' cost of capital is one of the key factors affecting 

capital budgeting, financial decision makings and macro assessment of 

corporations' functions, and also due to the fact that "cash flow" is the 

ultimate and tangible outcome of a profit unit, this paper is to present the 

results of an experimental study on the relation between IC with two 

important factors affecting on performance (i.e. operating cash flow and 

weighted average cost of capital) in Tehran Stock Exchange (TSE) 

between 2005 to 2009. Two fundamental hypotheses of this research 

*Corresponding Author: zrezaee@memphis.edu


1. Introduction 


May 23 and 24, 2012 

suppose a negative significant relationship between IC and WACC but a 

positive significant relationship between IC and corporations' operating 

cash flow. The findings confirm the above hypotheses. The results imply 

that by enhancing and applying efficient and fruitful management of IC, 

corporations' WACC can be expected to be cut back due to the 

enhancement of knowledge level, intellectual ability and their becoming 

more intelligent on one hand, and their increasing of the operating cash 

flow on other. Therefore, utilization of this effective IC for obtaining a 

higher financial yields and a better future performance could be suggested. 

Keywords: knowledge management, intellectual capital, weighted 

average cost of capital, operating cash flow, Q Tobin. 


By passing the industrial economy and entering a knowledge – 

based economy refered to by different names and titles, management 

systems, too, are experiencing remarkable metamorphosis and a new 

literature on knowledge management is emerging. Draker, the famous 

management scholar states, "We are entering a knowledge society in 

which important economical resources are on longer financial capital, 

natural recourses, work force, etc, but rather it would be the 

knowledge. Twenty first century would be the century of knowledge 

economy in which knowledge or intellectual capital would receive 

more preference as a wealth producing factor in comparison to other 

tangible and physical assets. » [4, 15] 

In 1980s, market value in proportion to the book value of a 

corporation (that is the proportion of corporations' capital value in the 

market to the net value of the assets which are announced in their 

balance sheets) was one of the motives which attracted the attention of 

analysts and researchers. Meanwhile, the concept of intellectual 

capital or intangible assets emerged in stock markets in order to 

150


explain the existence of the difference between corporations' book 

value and their true value. Between 1980 to 2000 the average 

proportion of market value to the book value of the 500 largest 

American corporations increased continually and is now 6 times 

greater. In the other words, out of every exchanged 6 dollars of a 

corporation in the stock mark, only 1 dollar has been announced in the 

balance sheet of that corporation and the other 5 is considered as the 

intangible assets of that corporation which are not supposed to be 

announced in financial reports of that corporation.[13] 

Today everybody agrees that organization performance is not 

evaluated or interpreted only based on financial aspects, rather, there 

are other factors which guarantee not only the improvement of 

financial issues but also the non-financial existence of organizations. 

[22] The non-financial issues which have become so remarkably 

notable nowadays, are entitled as intangible assets, intellectual capitals 

and knowledge-based assets. Today, the intangible aspect of economy 

has been based upon the intellectual capital and its initial component 

is knowledge and information. To be active in today's' markets, 

organization are in need of any type of information and knowledge in 

order to improve their own performance.[12] 

In the end, it could be said that financial management is in fact 

decision making and adopting optimized strategies regarding investing 

funds on assets and obtaining optimum financial combination in order 

to enhance the overall value of the institute covering all of the issues 

put forward on intellectual capital or in the other words, blending 

financial management with knowledge management would lead to 

increasing the efficiency and finally to optimization of management of 

the united business affairs. 

151


2. Research Theoretical Literature 

2.1. Knowledge Management 


May 23 and 24, 2012 

Francis Beiken refers to knowledge as power. The power of 

knowledge is an important source for maintaining the precious 

heritage, learning new things, solving problems, creating competition 

nucleus and providing new opportunities for the individual and 

organization in present and future. 

In the past decades and with the advent of industrial revolution, all 

the trends have been directed toward industry, mass production and 

suggesting strategies for its development. But now advancing toward 

knowledge-based societies is an issue discussed everywhere. With the 

emergence of information technology and its influence on all 

dimensions of human life, new paradigms are being presented, new 

viewpoints are being made in which knowledge-as a valuable source 

along with the new sources which were previously focused in 

economy (job, land, capital)- is put forward as a valuable asset. 

In the new viewpoint, focus on industry has given its place to 

focus on knowledge. Organizations not being an exception to this 

have accordingly undergone many changes and alterations. As a 

result, in the era of growing changes and to attain a competitive 

privilege, in order to survive and challenge the fluctuating conditions 

of the competition setting, organizations have resulted to applying 

modern management tools and strategies. The two components of the 

phrase "knowledge management", i.e. "knowledge" and 

"management" have combined together aided by the two elements of 

technology competence as well as organizational culture to control 

organization's collective memory. Knowledge management would 

help organizations and corporations boost decision making, have more 

flexibility, enhancy optimization, creating new job and business 

152


opportunities, reduce the expenditures and improve employee 

motivation. 

Knowledge management could be applied to diverse ranges of 

activities including normal, logical, complicated, and unexpected. It 

involves less emphasis on whatever we possess, rather more emphasis 

on whatever we know. Knowledge management is not about 

employees hands, rather it's about their minds. Thus, managers 

challenge would be establishing suitable environment for human 

growth and development in knowledge-based organizations. 

2.2. Intellectual Capital 

The scientific and practical foundation of intellectual capital can 

be traced back in accounting activates of human resources in 1970s 

and 1980s and with the advent of a new type of economy in early 

1990s-known as "knowledge-based economy". The concept of 

intellectual capital was developed by some pioneer writers in 1990s 

and many things were written about it. 

In early 1990s the concept of intellectual capital or intangible 

assets emerged in stock markets to explain the existence of the 

difference between the book value of offices and their true value. As 

an example, in his best-seller Intellectual capital (Sveiby, Stewart, 

1997; Edvinson & Malone, 1997), Thomas Stewart (the former editor 

of Fortune Daily), has referred to knowledge as the heating nucleus of 

competitive weapons and the new wealth of today's organizations, 

considering it to be more precious and powerful than organizations 

physical resources. It could be said that the advent of knowledge 

economy has led to the enhancement of the popularity of intellectual 

capitals as a research topic. 

Micheal Haw, in an article published in 2001 entitled "the 

emergence of modern technologies and their application in 

organization's intellectual capitals" stated; "Cost accounting measures 

a creative economy with the tool of a production ". Based economy 

153



May 23 and 24, 2012 

for example, the items on traditional balance sheets are not to be 

trusted, for knowledge-based assets would rapidly become outdated. 

A large number of management economics and accounting 

scholars and scientists have studied intellectual capital and its 

different aspects, presenting different definitions about intellectual 

asset with some of which we will become acquainted. [8] 

Intellectual capital has a transitory and flowing nature and will 

assist the organization on entering the competitive environment once 

it is discovered and used by the organization. [5] 

Intellectual capital consists of the sum of an organization's 

members and the conversion of the practical function of the 

knowledge of organization's members. 

Intellectual capital is obtained by the difference sum of a 

commercial unit's market value and its assets substitution costs.[18] 

Intellectual capital is the searching and keeping on efficient 

application of knowledge in comparison to information. [19] 

Intellectual capital consists of a wide organizational knowledge 

being exclusive to every single corporation allowing the corporation 

to adjust continuously to the fluctuating conditions. [14, 16] 

2.3. Components of intellectual capital 

Brouking's pattern, is a distinguishing one in the field of 

intellectual capital developed in 1996. According to her idea, 

intellectual capital consists of: [7] 

Market Assets (customer assets): All market's intangible assets 

such as business brands, customers, customer's loyalty, distribution 

channels, orders, etc. 

Human-based Assets: Skills and experiences, problem-solving 

abilities, approaches and leadership capabilities, plus every other 

property institutionalized in employees. 

154


Intellectual Ownership assets: Tricks and secrets, business brands, 

patents, and every other intangible asset which is capable of being 

supported by the law of spiritual ownership rights. 

Infrastructural Assets: Includes all technologies, procedures, and 

methodologies enabling an organization on proper performance. 

Infrastructural 

Assets 

Organization/Corporation Objevtives 

Intellectual Assets 

Intellectual 

ownership 

Assets 

155 

Humanbased 

Assets 

Market/customers 

Assets 

Figure 1: Brouking's classification of intellectual assets 

A featured in Figure 1, Brouking has classified intellectual assets 

into 4 categories. A list of items classified in each of these categories 

will be presented. These components of intellectual assets components 

are items frequently referred to as intellectual assets. Unfortunately 

the present accounting system recognizes only intellectual ownership 

as the component to be mentioned in balance sheet, other components 

being simply ignored. However, it should be noted that these ignored 

components of the intellectual asset are in fact the same motives 

creating value and competition privilege for the corporation or 

organization.



May 23 and 24, 2012 

Table1: Four components of Intellectual Assets 

Human 

Capital 

Communication 

Capital 

(customers) 

Organizational (Infrastructure) 

Capitals 

Intellectua Substructure 

l Asset Capitals 

- Tricks and - business brands - Patents - Management 

secrets - Customers (Names, - Royalty philosophy 

- Educations Purchase Records) - Designing - Common culture 

- Job 

- Customer's loyalty Rights - common values 

preparations and Trust 

- Business - Management 

- Job related - Customer's influence Secrets processes 

knowledge and Vastness - Business - Information 

-Psychological - Corporations names brand 

systems 

assessments - In Advance Sales -Product - Network systems 

- Job related - Distribution channels appearance - Financial 

qualification - Business 

relations 

- Patterns and Cooperation's 

- Corporation's 

frameworks (Common 

strategies 

- Cultural Investments) 

- Corporation's 

diversity - License grant 

methods 

Agreements 

- Sale tools 

- Favorable contracts 

- Knowledge Bases 

- Agreements of 

- Specialized teams 

establishment 

privileges 

and networks 

2.4. The concept of weighted Average cost of capital 

Conceptually, a corporation's capital cost consists of the cost of 

investor's chance for investment on that corporation. The estimate of 

weighted average cost of capital in the corporation, is an attempt to 

quantify the average preference as expected by all the corporation's 

investors, including: the indebtedness related to short term and long 

term creditors on which interest is accrued, preferred and common 

stockholders. Corporation's cost of capital calculated based on 

weighted average whose weights will be determined based on the 

values of investment's different resources. 

156


The concept of investment cost is based on the assumption that the 

goal of a corporation equals maximizing stock holder's wealth. Each 

corporation has its own risk and preference (of course the kind of 

corporation whose aim is obtaining some profit). Each of the investor 

groups, for example holders of savings bond, and, preferred and 

common stock, are after the amount of productivity rate which is 

worth their risk. Cost of capital is the minimum preference rate 

obtained by the corporation to supply the corporation investors with 

the expected preference. The most important application of cost of 

capital could be probably defined as follows: 

If a corporation's investment efficiency rate is more than its cost of 

capital and if this amount of efficiency increases, investor's wealth 

will increase. Its reason is said to be that if the corporation's efficiency 

rate becomes more than its cost of capital, the owners of bond and 

proffered stock will archive the expected fixed rate of efficiency, then 

the remaining rate of efficiency will accrue on common stock holders 

which would be more than their expected rate of efficiency. 

According to the second definition of cost of capital, it would be 

associated to the capital budgeting process. The cost of capital would 

be defined as the discount rate which is applied to evaluating and 

judging different investment projects. The corporation's value will 

increase if those projects with higher efficiency rate than the 

corporation's cost of capital are accepted by them. In the same way, 

the corporation's value will decrease if project's efficiency rate are less 

than the corporation's cost of capital. So, the other application of cost 

of capital would be the evaluation of capital projects. 

2.5. Cash Flow Statement 

Balance sheet shows the financial structure of a business unit 

within a specific time frame. Meanwhile the need for a separate 

financial statement exhibiting cash management is completely felt. 

157



May 23 and 24, 2012 

Cash flow statement-as one of the main financial statements-plays 

this role. Cash flow is the final and tangible result of a profit unit's 

performance. On the other hand, according to Disclosure Principle, 

providing all the potentially useful data and its advantages is more 

necessary than the cost of providing the data. So, providing cash flow 

statement as of the beneficial information bases in predicting cash 

flows with a low cost of preparation and presentation along with other 

basic financial statements have been taken into account. This financial 

statement provides useful information in the format of operating cash 

flows, investment cash flows, and financing cash flows. 

These information would help evaluate future cash flows, 

liquidity, solvency, and the financial flexibility of economical units. 

The Financial Accounting Standards Committee rendered necessary 

preparing and reporting the cash flow statement in 1987. Statement of 

changes in Financial Position in which changes in account balance, in 

debt ness, indebtedness, and the rights of capital owners were 

reported, was replaced by cash flow statement. Cash flow statement 

depicts the profit unit's cash circulation and is in fact an important step 

toward cash flows accountings. Historical information related to cash 

flow could have a crucial role in judging about the price, timing and 

ambiguities related to future cash flows. 

Information mentioned in this basic financial statement indicates 

the nature of the relation between economical unit's profit-making and 

its capability in making cash. Based on the fact that the items included 

on cash flows statement are exposed to changes of accounting's 

optional methods, this financial statement enjoys a high unvarying 

level among economical units, having a high ability of data 

comparison accordingly. 

The results of some researches indicates the fact that the 

desirability of the information based on cash flows is much higher 

than that of the promissory information in predicting and decisionmaking. 

The result of more comprehensive researches shows that if 

158


the data included on cash flow statement are focused and analyzed 

simultaneously along with other financial statements, much more 

beneficial data will be provided. 

2.6. Research Literature 

The first experimental study of intellectual capital measurement 

was carried out by a Sweden association in mid 1980s, after which 

many researches were done within [17, 6] or among [21] countries to 

determine corporation's intellectual capital conditions. Joya's 

documentary researches showed the attempt to include intellectual 

capital on the balance sheet is a logical concept and Joya's researches 

proved this to be totally scientific.[11] 

Garcia and Martinez (2007) in an experimental study proceeded to 

examine the relation between intellectual capital information used in 

investment decision-makings on Spanish corporations.[9] 

Shen Tai and Chen-Tung (2008) presented a new model for 

evaluating intellectual capital performance using a combination of 2tuple 

phase approach with Multi-Conversional Decision Making 

technique (MCDM) and tested it on specialized advanced corporations 

in Taiwan.[20] 

Young Chu, et. al (2006) studied the relation between intellectual 

capital components with value/performance, specialized advanced 

industries of Industrial Technology Research Institute (ITRI) and 

concluded that, first there is a significant positive relationship between 

intellectual capital components and corporation's performance, and 

second, increasing the intellectual capital involves the process of 

creating value and their strategic saving on corporations. [24] 

Hong Pew Tan et. al (2007) in their research have examined the 

relation between intellectual capital with corporations' financial 

efficiency. The results indicate that, first, there is a significant positive 

relationship between intellectual capital and corporations' present and 

future financial efficiency, second, the effect of intellectual capital on 

159



May 23 and 24, 2012 

corporations' financial efficiency in different industries are different. 

[10] 

There are a lot of research in Iran on IC. One of them that has been 

is implemented by Anvary Rostamy and Rostamy in 2002. They 

studied several measurement models of measuring IC. [2] 

Based on the above-mentioned issues pointing at examining the 

relation of intellectual capital with the activities of commercial units, 

we realize that intellectual capital has influence on corporation's 

performance evaluation indexes. So by identification of the type and 

amount of the effects, we could proceed to have a better management 

of the units. 

3. Research Methodology 

The present study is an applied correlation type research. To 

achieve the desired information and to make the research executive, 

all the related websites such as those of the stock exchange and bonds 

and investment companies were reviewed, also related articles, books 

and documents were used. 

The time period of the research starts from early 2005 until the end 

of the financial year 2009. 

3.1. Subjects and Research Theories 

The subjects of this research include all Tehran's stock exchange 

companies possessing the following qualifications: 

The final point of financial year must be in March (Esfand-the last 

month of Iranian year) to cause increasing or maintaining the ability 

of comparison of the achieved financial data. 

On the whole, bank industries, credit agencies and other financial 

institutes must not be financial mediation or investments because the 

kind of their activity is different from that of the other industries. 

160


No trading pause of more than 3 months must have occurred 

during research period so that it wouldn't have been considered as the 

cost quotation of the common stock. 

The required financial data related to the period between 2005 to 

2009 must have been presented and available to stock market. 

The hypotheses of this research are as follows: 

Hypothesis 1: There is a negative and significant relationship 

between intellectual capital (IC) and weighted average cost of capital 

(WACC). 

Hypothesis 2: There is a positive and significant relationship 

between intellectual capital (IC) and cash flow operations (CFO) of 

corporations. 

3.2. Variable Measurement Methodology 

Measuring intellectual capital, "Tobin's q" 

"Tobin's q" method was developed by James Tobin (1978)-the 

economy Nobel winner. This proportion measures the relation 

between the market value of a corporation and its replacement value 

(the cost of that corporation's replacement). Theoretically, this 

proportion tends toward the unit in the long run, but experimental 

evidences show that at the same time, this proportion could be 

significantly different with the number one. For example software 

companies which highly enjoy intellectual capital, have a proportion 

of about 7 or more whereas the companies with abundant physical 

capital have a proportion of about one. Q-Tobin proportion is 

basically similar to the proportion of market to value, but with this 

difference that Tobin while calculating, applies the replacement cost 

of the physical assets instead of their book values. The obtained 

proportion is used in such a way that if the "q" proportion of a 

company is more than the "unit" sum and if it is more than the 

competitive "q", then that company has the ability of gaining more 

interest than similar companies.[23] 

161


naturally are not directly communicable with other classes of cash 

flows on statement of cash flows. 

Since according to Iran's accounting standards, statements of cash 

flows are reported in 5 separate topics, the amount of cash gained 

from the operating activities which was applied as the dependent 

variable in this research has been extracted from the topic of the 

operating activities included on statement of cash flows of the 

statistical sample companies. 

3.3 Data Analysis and Results of Hypotheses Testing 

To analyze the data gained from this study, statistical methods of 

"Correlation analysis" and "Regression analysis" were applied; the 

former to determine the type and the amount of association among the 

variables, and the latter to determine the effect of independent 

variables on dependent variables. Also, due to the fact that a large 

number of independent variables affect the two assessment indexes of 

performance and also because only one of these factors, ie, intellectual 

capital has been examined using Dourbin-Watson Test (D.W), the 

amount of the correlation of independent factors which affects 

variables (weighted average cost of capital and operating cash flow) 

were measured. [1,3] 

Based on these analyses, the results of hypothesis testing are as 

follows: 

Hypothesis 1: There is a negative and significant relationship 

between intellectual capital (IC) and weighted average cost of capital 

(WACC). 

sample. 

Hypothe 

sis 

Table 2: Statistical Results of Hypothesis Testing 1 for the whole 

Independ 

ent 

Dependa 

nt 

Correlati 

on Rate 

R 2 

163 

Ttest 

B 

standa 

Sig. 

* 

D.W Result


1 IC 

Variable Variable rd 

WAC 

C 

-0.335 


May 23 and 24, 2012 

164 

.09 

5 

- 

2.56 

2 

- 

0.33 

5 

.01 

3 

2.20 

4 

confirm 

ed 

* 

Regression Correlation is significant at 0.05 level. 

Based on the results of statistical testing of this hypothesis, it could 

be said that «IC» has a negative and significant effect on the 

investigated corporation's WACC at the significance level of %5. So, 

the research hypothesis is confirmed at the confidence level of 0.95 in 

the investigated corporations. Of course, this effect is calculated 

weakly, which means %95 of corporations' WACC changes is due to 

their IC. On the other hand, DW testing, has been calculated for the 

regression model showing number 2.204. since this amount stands 

within the numerical interval (1.5-2.5), the lack of correlation in error 

components of the regression model confirms the above model. 

Hypothesis 2: There is a positive and significant relationship 

between intellectual capital (IC) and cash flow operations (CFO) of 

corporations. 

Table 3: Statistical Results of Hypothesis Testing 2 for the Whole 

Sample. 

Hypoth 

esis 

Indepen 

dent 

Variable 

Depen 

dant 

Variabl 

e 

Correla 

tion 

Rate 

2 IC OCF 0.583 

R 2 

0.3 

40 

Ttest 

0.5 

83 

B 

stand 

ard 

13.35 

9 

Sig 

. * 

0.0 

00 

D. 

W 

1.7 

04 

Result 

confir 

med 

* Regression Correlation is significant at 0.05 level. 

Based on the results of statistical testing of this hypothesis, it 

could be said that IC has a positive and significant effect on the 

investigate corporations in Tehran's stock market at %5 level of 

significance. Therefore, hypothesis 2 is confirmed at the confidence 

level of 0.95 in investigated corporations. The correlation amount 

0.583 indicates the existence of a relatively strong and positive 

relationship between these two variables, however the determining co


efficiency shows a weak effect. This means that %34 of corporations' 

OCF changes are due to their IC. The rest would be explained through 

variables other than IC. On the other hand, DW test has been 

calculated for the regression model of the first hypothesis, showing 

number 1.7. since this amount stands within the numerical interval 

(1.5-2.5), the lack of correlation in error components of the regression 

model confirms the above model. 

4. Conclusions and Final Remarks 

This research was carried out in order to investigate 

experimentally the relationship between (IC) and the two items (OCF) 

and (WACC) of performance assessment indexes of admitted 

corporations at Tehran's stock market. The results showed that there is 

a significant relationship between IC and WACC and also OCF of the 

investigate corporations. The results obtained from hypotheses testing 

of the study presented briefly on figures 2 and 3, indicates the 

significant and reverse relationship between IC and WACC, also the 

significant and positive relationship between IC and OCF of the 

investigate corporations. This means that changes in the IC of 

corporations would result in their WACC changes reversely, and on 

the other hand OCFs will increase by IC's increasing, that is, if the 

business unit utilizes its available ICs efficiently, it can increase stock 

holder's wealth in such a way that the unit's risk would not increase. In 

other words, if the preference rate of the investment of a corporation is 

more than its cost of capital without the risk level increasing, stock 

holders' wealth will increase and this will be due to the increasing of 

the cash obtained from corporations' operations. This issue is justified 

by the fact that by efficient utilizing of corporations IC, their 

preference rate will be more than their cost of capital. (By properly 

using business unit's assets, also by decreasing the usage of financial 

resources with high interests and finally by proper management, it is 

possible to provide low interest financial resources.); thus, bond 

165



May 23 and 24, 2012 

holders and preferred stock holders would gain the desired fixed 

preference rate and the remaining of the preference rate will accrue on 

common stock holders which will be more than their expected 

preference rate. Therefore by using intellectual capitals and applying 

knowledge management, the cash gained through unit's operation will 

increase, so will stock holders wealth. In the end it can be said that 

when corporations' IC increases, OCF increases too, on the contrary, 

their WACC decreases. 

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May 23 and 24, 2012 

The competitive pricing of audit services in a non- 

Big 4 market 

Abstract 

Azizkhani masoud 

Assistant Professor OF Ilam University 

This study examines audit pricing in a market characterized by increasing 

competition for the supply of audit services but without the presence of large 

international audit firms. Using traditional audit fee modelling, we find that, in 

the absence of international “brand names”, audit firm size or market power is 

still associated with audit fee premia. We also find that increased competition in 

the supply side of audit services following regulatory changes resulted in lower 

audit prices. These results have implications for the consequences of increasing 

the number of audit firms. This study extends the audit fee literature by 

examining how increased competition in the audit market affects the pricing of 

audit services in emerging markets. 

Keywords: Audit fees, Audit fee premium, Auditor competition 

JEL Classification: G14; G18; M42.


1- Introduction 

Studies of the pricing of audit services have documented 

significant variations in audit fees in relation to auditor characteristics 

and clients’ characteristics in many markets. In the large majority of 

countries previously examined, irrespective of their development 

status, the market for audit services are lead by, if not dominated by, 

international audit firms or their affiliates. We extend this literature by 

examining the pricing of audit fees in an audit market from which 

international audit firms are excluded by regulation. 

The extant audit fee literature is focused on markets that include, 

and usually dominated by, Big N audit firms. A persistent feature of 

this literature is that Big N auditors earn higher audit fees than non- 

Big N auditors, which has long been interpreted as a support for the 

“auditor size” hypothesis, whereby actual or perceived audit quality is 

differentiated by auditor size (e.g., Francis 1984; Francis and Stokes 

1986; Palmrose 1986; Francis and Wilson 1988; Ahmed and 

Houghton 1996). Large international accounting firms (Big N) are 

excluded from the Iranian audit market, providing a particularly rich 

and interesting natural experiment in which we can examine the 

emergence of larger audit firms in an increasingly competitive market, 

and how size and reputation affects audit fees. We are able to examine 

the Iranian audit market dynamics in relation to reputation effects and 

market power because Iranian auditors do not have to price any civil 

litigation risk. Prior research argues that when the litigation risk is 

high, auditors will continuously assess their exposure to lawsuits and 

incorporate that assessment into the audit plan or pricing of audit 

services (Seetharaman et al. 2002; Chaney et al 2004). As we explain 

in section 2, the legal liability of auditors in Iran is very limited. Their 

main legal exposure is prosecution under criminal provisions in the 

Iranian Trade Law, but to date, there are no reported prosecutions. 

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May 23 and 24, 2012 

Third, the traditional auditing literature exhibits a major interest in 

competition effects. This ranges from the effects of competition on 

audit fees and fee consequences of overcapacity in audit firms 

(increased competition) following corporate failures during the late 

1980s (Beattie and Fearnley 1998b), to the consequences of reduced 

competition associated with emergent market domination of Big N 

audit firms (e.g., Gilling and Stanton 1978; Wolk et al. 2001; 

Kohlbeck et al. 2008; Asthana et al. 2009). This focus on supply-side 

phenomenon is complemented by a substantial part of the competition 

literature being concerned with declining competition resulted from 

increased concentration in supply as the number of Big N firms 

declined (e.g., Johnson and Lys 1990; GAO 2003; Wolosky 2003; 

Bloom and Schirm 2005). Recently, regulators are concerned about 

the consequences of increased concentration in audit markets on the 

pricing of audit services and called for increasing the competition in 

the audit market (House of Lords Report 2011). Prior research on 

audit fees is focused largely on mature audit and capital markets such 

as the US, UK and Australia, where competition in these markets is 

characterized by relatively stable level, but with increases in 

concentration and implied reduction in competition in the large client 

sector that is dominated by big international accounting firms (e.g., 

Pong 1999; Wolk et al. 2001). The Iranian audit market experienced a 

regulatory licensing change in early 2002 which lead to an increase in 

the number of audit firms engaged by firms listed on the Tehran Stock 

Exchange (hereafter TSE), and in turn, rapid growth in audit market 

competition. This provides a novel opportunity to examine how this 

increased competition affects the pricing of audit fees. 

The main objectives of this study are as follows: (1) examining the 

pricing of audit fees in the Iranian audit market to investigate whether 

there is a fee premium for the “size” of the auditor; and (2) examining 

the effect of increased level of competition in the audit market on the 

pricing of audit services. 

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The reminder of this is organized as follows. We next describe the 

Iranian audit market environment. We then review the prior literature 

on audit fees. The fourth section describes the research methodology 

and the sample. The findings are summarized in the fifth section. 

Finally, the research conclusions and its limitations are presented. 

2- The Iranian audit market 

In general, the Iranian audit market is different from markets of 

emerging economies in the world or even in the region. First, from the 

demand side, article 144 of the Iranian Trade law requires public 

companies to appoint a certified auditor which must be selected from 

those accredited auditors authorized by the Economic Ministry 1 . The 

TSE was established in 1967 and grew to 105 listed companies at the 

time of the Islamic Revolution in February 1979. Following the 

revolution, all banks and insurance companies and many heavy 

industry companies were fully nationalized. Other not fully 

nationalized companies were transferred to, and controlled by the 

government when private sector owners abandoned or forfeited their 

interests in these companies and through the government-owned banks 

acting on debt defaults. All audit services for these government 

controlled companies were assigned to government auditors, 

culminating with the establishment of the Iranian Auditing 

Organization (IAO) in 1987. Beside the IAO, there are small private 

audit firms which are certified by the Economic Ministry and are 

allowed to provide audit services to non-government controlled firms. 

Also after the revolution, large International accounting firms are 

banned from operating and providing audit services in Iran. 

To stimulate economic recovery, the Iranian government 

implemented a privatization policy to transfer ownership of public 

sector companies to the private sector through a series of five-year 

Plans (Davani, 2003, TSE, 2003). The first five-year Plan (1989-1993) 

required the government to transfer ownership of nationalized and 

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May 23 and 24, 2012 

State industrial units (excluding strategic industries) to private sector 

shareholders (Roudaki, 1996, Ebadi, 1995). This resulted in an 

increase in the number of companies listed on the TSE grew from less 

than 60 firms prior to 1990 to 201 firms by 1995. The second fiveyear 

Plan (1995-1999) was a continuation of the first Plan with the 

number of TSE listed firms increased to 296 in 1999. By that time, 

and due to the large increase in the demand side, the IAO was unable 

to provide audit services to the variety of government controlled 

entities and was not suited to audit the increasing number of profitseeking 

companies post-1989 (Moulkaraei, 2005). This resulted in a 

high audit report delays, especially for government-controlled firms. 

In 1993, the Act “Using services of Certified Public Accountants” 

which allows certified public accountants to practice and provide audit 

services to the public sector was passed by the Parliament but due to 

unknown reasons was not effective till late 2001. With the 

establishment of the Iranian Certified Public Accountants (IACPA) in 

late 2001, the audit market experienced a large increase in the number 

of audit firms which are allowed to provide audit services to the 

market 2 . This Act also allows TSE listed firms to choose their auditor 

from the members of the ICPA (either the IAO 3 or private audit 

firms), regardless of their ownership structure (Davani 2003). 

The establishment of the IACPA in 2001 leads to a change in the 

supply side of the audit market by increasing the number of audit 

firms providing audit services in the market. In late 2001, from the 

402 issued licensed, 309 auditors were sole practitioners, mostly 

providing services to small non-listed clients, and 93 were in 

partnership, which are more likely potential competitors for the audits 

of listed companies (Bagherpour et al. 2011). From 2001-2003, there 

was a 38% increase in the number of licensed private sector auditors 

who were principals in private sector audit firms (Bagherpour et al. 

2011). This trend suggests a substantial increase in competition for 

audit clients in the audit market. This provided greater opportunity for 

174


companies to select an auditor that was a better match to their needs. 

As a consequence, the audit market moved from a market dominated 

by the IAO to a market with many more auditors, potentially offering 

a wider range of quality and services. The market share of the IAO 

decreased from 73% in 1998 to 24% in 2004, two years after the 

establishment of the IACPA (Azizkhani 2010). 

Also, the Iranian audit market is different from other markets in a 

way that it is an emerging market with limited shareholder protection. 

Big International accounting firms are not operating in the audit 

market and local audit firms are not affiliated with international audit 

firms. There is a rapid growth in audit market competition as 

evidenced by a 100 percent growth in the number of auditors engaged 

by companies listed on the TSE from 2000-2003 (Azizkhani 2010). In 

addition, the Iranian Code of Law does not expose auditors to a 

litigation risk other than prosecution by the State under criminal 

provisions, which eliminates the insurance hypothesis. 

Third, the litigation risk against auditors in Iran is low. This may 

have an impact on the pricing of audit services. Prior research on audit 

fees shows that as the expected losses from imposition of legal 

liabilities increases, the audit fees will increase (Beaty 1993). In Iran, 

there are only two potential risks for auditors, who do not follow the 

professional rules set by the IACPA in their audits: 1) a penalty 

imposed by the quality control committee of the IACPA that reviews 

audit works; and 2) revoking audit firm’s license as the SEO’s 

“trusted auditor” by the peer review Audit committee in the SEO who 

reviews audit reports of TSE listed firms 4 . Under the IACPA rules, the 

quality control committee monitors and reviews the quality of audits 

performed by IACPA members on a sample basis and at least one 

audit report of any member should be reviewed within a 3 years 

period. The penalties applied to auditors with imperfect audits are not 

severe and are limited to banning the auditor from audit works for a 

number of years 5 . Although the statutory requirements for an audit 

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May 23 and 24, 2012 

identify shareholders as the intended recipient of reports, they do not 

lead to a recognizable duty of care or direct liability from the implied 

relationship between auditors and shareholders and Iranian law does 

not provide for civil action against auditors to recover damages. The 

primary legal exposure for auditors in Iran is prosecution under 

criminal provisions in the Iranian Trade Law; however, we are not 

aware of any such prosecutions to date. The absence of civil litigation 

risk for Iranian auditors may have an impact on the pricing of audit 

services. Seetharaman et al. (2002) show that auditors adjust their 

audit fees upward to reflect increases in their litigation liability. 

3- Literature review 

Starting with Simunic innovative paper (1980), there is extant 

body of literature examining factors associated with pricing audit 

services. The main objective of these studies was to identify factors 

that have an impact on the pricing of audit services. The results of 

studies in the US (e.g., Simunic 1984; Francis and Simon 1987; 

Palmrose 1996; Gist 1992), the UK (Taylor and Baker 1981; Taffler 

and Ramalinggam 1982; Chan et al. 1993; Chaney et al. 2004), 

Australia (Francis 1984; Francis and Stokes 1986; Craswell and 

Francis 1999; Boon et al. 2005), New Zealand (Firth 1985; Johnston 

et al. 1995), Canada (Chung and Lindsay 1988; Anderson and Zeghal 

1994), Japan (Taylor 1997), Singapore (Low et al. 1990), Hong Kong 

(Defond et al. 2000), India (Simon et al. 1986), and Pakistan ( Simon 

and Taylor 1997), tried to establish the association between audit fees 

and variables related to client’s; size, complexity, and risk. 

Findings in prior research indicate that the most important factor in 

explaining the level of audit fees is the size of the audittee. The idea is 

that auditors of larger companies should have to spend a lot of time 

and efforts on testing and analyzing data, information and 

management assertions on financial reports (Simunic 1980; Firth 

176


1985; Chan et al. 1993; Pong and Whittington 1994; Simon 1995). 

Complexity of audittee is also found to be related to the audit fees 

(Chan et al. 1993). Prior research shows that as audit clients become 

more complex, more time and effort is needed to perform the external 

audit work, and hence, audit fees are significantly influenced by the 

auditee’s complexity level (e.g., Simunic 1980; Francis 1984; Francis 

and Stokes 1986; Maher et al. 1992; Carcello et al. 2002). 

Findings in prior research show a relation between auditee’s risk 

and audit fees. Simunic (1980) argues that audit fee is a function of 

cost of performing the audit (including a normal profit) and the 

expected costs of the audit risk. Audit risk is the risk that financial 

statements may be materially misstated after the audit is completed 

and an unqualified opinion is issued (Arens and Loebbecke 1994). 

Audit firms adjust for an increase in the audit risk level by either 

increasing their audit effort (to reduce the chance of undetected 

misstatements 6 ), or charging a fee premium as a compensation for the 

greater risk of audit failure (Brinn et al. 1994; Pratt and Stice 1994; 

Seetharaman et al., 2002). Prior studies have provide empirical 

evidence that audit firms charge higher audit fees when the client’s 

audit risk increases (e.g., Simon and Francis 1988; Palmrose 1996; 

Cameran 2005). 

Also, prior research has tried to relate audit fee to auditor quality 

with the expectation that audit fees might be higher when an auditor is 

recognised to be of superior quality (Hey et al. 2006). Prior studies 

attempted to use a large number of different proxy variables to 

represent auditor quality, with the majority using an indicator variable 

for large international accounting firms [Big N] (Simunic 1980; 

Francis and Stokes 1986; Palmrose 1996; Chan et al. 1993). These 

studies show that Big N audits charge higher audit fees and, on 

average, their premium has been around 20% (Francis 2004). The 

mostly used explanation for charging this premium is either because 

of Big auditors’ “brand name reputation” or due to their “market 

177


Aturn = auditee’s asset turnover, measured by the ratio 

of total sales divided by total assets at the end of fiscal 

year; 

Lev = financial leverage measured by the ratio of total debts 

to total assets at the end of fiscal year; 

Switches = 1 if it is the auditor’s first year with the client, 

and 0 otherwise; 

Peak = 1 if the client’s balance sheet date is at 29/12, 

and 0 otherwise; 

Loss = 1, if the firm reported a loss for year t-1, and 0 

otherwise; 

InvRec = firm’s complexity measured as the ratio of sum 

of inventories and accounts receivables to total assets t; 

Peak*size = the interaction term between peak and client’s 

size; 

Auditor = 1 if the auditor is the Audit Organisation, and 0 

otherwise; 

Year = dichotomous indicator variables to control for 

fiscal years; and 

Ind = dichotomous indicator variables based on twodigit 

TSE codes (XX industries) 

4-1-1- Test variable 

This study use variable Auditor to examine the relation between 

auditor type and audit fees. Variable auditor represents the auditor 

size. As explained earlier, most prior studies used the indicator 

variable of “Big N” to proxy audits performed by large international 

accounting firms and provided evidence that audit fees are higher for 

these auditors (Simunic 1980; Francis and Stokes 1986; Palmrose 

1996; Chan et al. 1993). However, these studies attributed this 

premium fee to either the “brand name reputation” or “market power 

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May 23 and 24, 2012 

(size)” of Big N auditors (see Camerman (2005) for a review). Thus, it 

is not clear whether it is the “brand name reputation” or the “market 

power (size)” of large international firms that drives their higher audit 

fees. As explained, in the Iranian audit market, the large International 

accounting firms or their affiliated firms are not allowed by law to 

operate (the brand name reputation is absent), and the IAO is the 

largest audit firm operating in the market with a dominant share 

comparable to the Big 4 market share (size or market power). Thus, 

we use variable Auditor for audits by the IAO, as a proxy for the 

“market power (size)” of the auditor, to examine its relation with audit 

fees. If it is the auditor’s size (market power) that derives auditor’s 

higher fees, then we expect a positive association between auditor 

type and audit fees. 

4-1-2- Control variables 

Following prior research, we control for individual client’s 

characteristics that have been shown to affect audit fees. For client’s 

size, this study uses ln(Assets) and assets turnover (ATurn). Prior 

research (e.g., Simunic 1980; 1984; DeFond et al. 2000; Hay et al. 

2006) argues that larger firms typically have more transactions and 

larger balances, which requires more audit work (giving rise to higher 

number of hours billed). Also, it has been argued that larger firms are 

more likely to be subject to public scrutiny, higher agency and 

political costs and therefore a higher risk for the auditor (Naser and 

Nuseibeh 2007; Caneghem 2009). Thus, as suggested in Hey et al. 

(2006), this study uses total assets to proxy for client’s size. It has 

been argued that for firms with high sales or transaction volume 

relative to assets (e.g. service firms), total assets may not adequately 

capture the effort needed to conduct the audits (Caneghem 2009; 

Chaney et al. 2004). Therefore, this study also controls for asset 

turnover (Aturn) to capture both effects. We expect positive 

associations between these variable and audit fees. 

180


Hey et al. (2006) argue that as client’s complexity increases, its 

audit is harder and more time-consuming. Also, the content and/ or 

nature of some financial statement items may add to the complexity of 

an audit engagement (Caneghem 2009). The nature of transactions, 

the accounting criteria for recognition and measurement, and the 

degree of necessary professional judgment regarding the potential 

importance of the outcome of future events are all factors that may 

add to the complexity of the audit (Thinggaard and Kiertzner 2008). 

To measure client’s complexity, and consistent with prior research 

(Fargher et al. 2001), this study uses variable (InvRec), the ratio of the 

sum of inventories and receivables over total assets, and expects a 

positive association with audit fees. 

Prior research suggests controlling for audit risk in examining 

audit fees (Pratt and Stice 1994; Jubb et al. 1996; Seetharam et al. 

2002). Consistent with prior studies, two measures are used in this 

study to control for audit risk. First, client’s profitability reflects the 

extents to which an auditor may be exposed to a loss in the event of a 

client is not financially viable and eventually fails (Simunic 1980). 

Thus, loss reporting firms expose higher audit risk and are expected to 

have a higher audit fees. To capture this, variable loss (loss) is used in 

this study. Although a positive relation with audit fee is expected for 

this variable, however, some studies report a negative relation 

between this variable and audit fees (Neimi 2002) with the 

explanation that poor financial condition might increase fee pressure 

and prevent auditors to raise audit fees. Second, this study controls for 

firm’s leverage (LEV) as a proxy for long-term solvency. Simunic 

(1980) argues that leverage problems measure the risk of a client 

failing, which potentially exposes a loss to the auditor. Therefore, we 

expect positive associations between our client’s risk measures (Lev 

and Loss) and audit fees. 

Variable peak is included in the model to account for the peakseason 

audit staff constraint. It could be expected that audit fees are 

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May 23 and 24, 2012 

higher during the busy season because of off-peak pricing during the 

reminder of the year (Chaney et al. 2004). As the vast majority of 

firms listed in the TSE have 29/12 as their financial reporting date 

(i.e., Esfand), audits of these clients are considered as peak-season 

work, and they are expected to have higher audit fees. Thus, we 

include variable Peak in the model and expect a positive association 

with audit fees. Chaney et al. (2004) argue that large clients are often 

audited throughout the year (interim audits) and the workload around 

the balance sheet date may thus, be relatively lower for these firms. 

Therefore and consistent with Caneghem (2009), we use an interaction 

variable between size and peak (peak*size), to account for the interim 

audits of larger clients, and expect a negative association with audit 

fees. 

Prior research suggests that, because of competition in the audit 

market, tendering over audit engagement may lead to low balling 

(DeAngelo 1981; Coate and Leob 1997; Camerman 2005). Also, a 

new client may be considered as a major inherent risk for the auditor 

and therefore, result in a higher audit fee (Camerman 2005). In a 

meta-analysis of audit fee studies, Hey et al. (2004) find some support 

for the assertion that audit fees are usually lower in audits where the 

auditor is relatively new to the engagement. Thus, we control for 

auditor’s new client using variable switches and expect a negative 

relation with audit fees. 

Finally, in examining audit fees, it is also important to control for 

industry because of differences in regulations between industries or 

differences in their risk levels (Jubb et al. 1996). Also, because of 

inflation, audit fees may vary across different years for the same 

client. Therefore to control for these effects, variables IND and Year 

are included in the regression model. 

182


4-1-3- Sample selection and data 

The sample for this study is selected from all Iranian companies listed on 

TSE during 2000-2007 which their audit fee data was disclosed on financial 

reports 7 . Data on audit fees are collected from annual reports. Other data are 

extracted from the Iranian securities and Exchanges Organization database 

(RDIS 8 ), and TADBIR PARDAZ database. This resulted in a sample of 

1340 firm-year observations. 

4-2- Descriptive statistics 

Table 1 reports the summary statistics of the sample. The 

dependent variable, audit fee has mean of IR200.48 (million) with the 

standard deviation of 229.89. The mean audit fee for firms audited by 

the IAO is IR243.2 (million) and IR168.2 for firms audited by non- 

AO auditors. The sample has a high asset turnover of 0.78 and highly 

leveraged (mean 0.71). The IAO has a market share of 0.43 of the full 

sample which is much lower that the market dominance of Big N audit 

firms documented in prior studies. 21% of the sample reported loss 

during the study period and 10% of firms have switched to another 

auditor during the study period. Interestingly, 78% of the sample has 

their audit during the peak season. 

Insert Table 1 here 

Table 2 reports the Pearson correlation matrix for the dependent 

and independent variables. The correlation between AFee and Lev, 

peak, lnsize, peak×size is positive and significant suggesting, other 

things equal, an increase in the level of audit fees with an increase in 

the levels of these variables. The correlation between auditor and 

AFee is significant and positive (p-value=0.000), suggesting that 

audits by the IAO are associated with higher audit fees. With the 

exception of correlation between peak and its interaction with size 

(peak×size), correlations among other independent variables are 

mostly low (below 0.40), with the highest correlation of 0.32 between 

the loss and the Lev independent variables 9 . 


183


5- Results 


May 23 and 24, 2012 

This study estimates Model 1 as a pooled-cross sectional models 

controlling for industry and year fixed effects for the full sample 

(column 1) and also for sub-samples of firms with the IAO as the 

auditor (column2) and firms with private audit firms (Column 3). 

Estimating Model 1 for the full sample allows the intercept to vary 

across auditor type (IAO versus non-IAO firms), but restricts the other 

coefficients to be the same across both groups. Estimating Model 1 for 

two sub-samples allows the coefficients to vary across two subsamples. 

All OLS regressions are estimated using robust standard 

errors clustered by firm to alleviate serial correlation and 

hetroskedasticity issues arising from pooled data (Rogers 1993). The 

results are presented in Table 3. 


For the full sample, with the exception of Lev, Loss and InvRec, 

the results for all other variables are significant and are in the expected 

directions consistent with prior research. Larger firms (lnsize); firms 

with higher assets turnover (ATurn); firms with balance sheet date in 

the peak season (Peak), and new audit engagement pay higher audit 

fees. The result for variable switches contradicts the notion that clients 

change their auditors to obtain a lower audit fee (Hay et al. 2006). The 

result of the interaction variable (peak*size) is negative and 

significant, suggesting that larger firms with balance sheet date at the 

peak season pay a lower audit fees. This result is consistent with the 

argument that larger clients are often audited throughout the year 

(interim audits) and the workload around the balance sheet date may 

thus be relatively lower for these firms results in lower audit fees 

(Chaney et al. 2004). Also, the results for year indicators variables are 

all significant and positive, indicating audit fees, on average, have 

increased over time. 

184


The coefficient of auditor is positive and significant (0.483 and pvalue=0.000). 

This suggests that firms audited by the Iranian Audit 

Organisation (IAO) pay higher audit fees relative to firms audited by 

smaller private audit firms. This shows that IAO charges a fee 

premium on its audits. The fee premium on audits by Big N auditors 

in prior research was attributed to the “brand name reputation” or the 

“market power (size)” of Big N auditors (Basioudis and Fifi 2004). 

Our result shows that when the auditor’s brand name (reputation) is 

absent, it is the auditor’s size (market power) that drives the fee 

premium. This is consistent with the market power hypothesis which 

argues that large audit firms act as a cartel and impose higher prices 

on their clients (Palmrose 1986). 

The other two columns in Table 3 present the results for estimating 

Model 1 for the sub-sample of the IAO (public sector auditor) and 

non-IAO (private sector auditors), respectively. These models allow 

the coefficients to vary across two sub-samples. With the exception of 

InvRec and Loss, the results for all other variables are consistent with 

the results for the full sample with differences in fee structure between 

two groups. For example, while audit effort (lnsize and ATurn) and 

audit workload measures (peak and Peak×lnsize) all have the 

predicted signs in both sub-samples, coefficients are significantly 

higher for the sample of the IAO’s firms. For audit risk measures, 

while the coefficient of Lev is significant and positive for the IAO’s 

sub-sample, it is not significant in the private auditors’ sub-sample, 

and the coefficient of variable Loss is only significant and negative for 

the sub-sample of private auditors, indicating that loss reporting firms 

are able to pressure their auditors to lower their fees due to their 

auditors’ lack of market power relative to the public auditor. These 

results suggest differences in the pricing of audit risk (as measured by 

Lev and Loss) among two groups. Also, the coefficient for variable 

switches is significant and positive only for the sub-sample of private 

audit firms, indicating that only private auditors are able to charge 

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May 23 and 24, 2012 

their new clients with higher audit fees. Overall, these results show 

differences in the determinants of audit fee between both groups of 

auditors. 

5-1- Pre-post 2002 regulatory change 

There was a regulatory change that directly affected the structure 

of the audit market and the supply side of the audit services in Iran in 

early 2002. In 1993, the Act of “using professional services of 

Certified Public Accountants” was passed by the Iranian Parliament. 

Due to some issues, this Act was not into force till late 2001. With the 

establishment of the Iranian Association of Certified Public 

Accountants (IACPA) in late 2001, the Act was enforced in 2002. 

According to the Act, public companies are allowed seek professional 

services from members of the IACPA. This Act has lead to an 

increase in the supply side of the audit services, as many audit firms 

have been created under this Act, and the public sector is encouraged 

to use the professional services of IACPA’s members. Before the 

establishment of IACPA, the Iranian audit market was monopolized 

by the Iranian Audit Organization (IAO) (MoulkAraei, 2005) 10 . This 

Act resulted in a substantive increase in the number of audit firms in 

the market, and in turn, increased competition in the market for audit 

services. Our data shows that the number of audit firms has increased 

from 24 audit firms in 2000 to 59 in 2006. The Act allows listed firms 

to appoint an audit firm from licensed IACPA members, but most 

government-owned firms need to have the IAO as their auditor. This 

indicates that the effect of increased competition seems to be among 

private audit firms. This increased level of competition among private 

audit firms may affect the pricing of their audit services. To examine 

this effect, we restrict our sample to firms with private auditors and 

partition the study’s period into 2000-2002 and 2003-2006 subperiods 

and re-estimates model (1). The results are reported in Table4. 


186


As shown in this table, with the exception of results for switches 

and Loss in the pre-2002 period and the results for peak and peak×size 

for the 2003-2006 period, the results for all other variables in both 

periods are similar to those reported for the full sample. However, the 

magnitude of variable coefficients significantly changed after the 

regulatory change. For example, the coefficients of variables firm’s 

size (lnsize) and asset’s turnover (Aturn) are decreased by 50%. Also, 

variables Peak and its interaction firm’s size (peak ×size) are 

significant only in prior 2002, indicating that audits during the peak 

time of the year are not affecting audit fees after 2002. This may be a 

subsequent of an increase in the number of audit firms in the market. 

The coefficient for audit firm switches (switches) is positive and 

significant only after 2002, indicating that firms that have changed 

their auditors after the regulatory change are paying higher audit fees. 

This could be due to the new clients higher start-up costs or firms 

paying a higher audit fees to the new auditor asking a cleaner audit 

report (opinion shopping). This contradicts the documented results in 

prior research which shown new clients pay lower fees. 11 Also, the 

coefficient of Loss is negative and significant only after 2002 12 . This 

shows that after the increased competition in the market, loss making 

firms were able to convenience their auditors for a lower audit fees. 

This is consistent with the argument that poor financial condition 

might increase fee pressure and lead auditors to lower audit fees 

(Neimi 2002). The results for variable InvRec which measures firm’s 

complexity, differs across two sub-periods. While this variable is not 

significant prior 2002, it has a positive impact on audit fees in 2002- 

2006 periods, indicating that after the regulatory change, client’s 

complexity, as measured by InvRec, is considered as determinant 

factor for audit fees. 

In overall, the results of the sub-period analyses show that after the 

regulatory change in 2002, which leads to an increase in the supply 

side of audit services, and in turn, an increased competition in the 

187



May 23 and 24, 2012 

market, there is a shift in the determinants of audit fees in the market. 

While before 2002, audit fees are mainly derived by auditing 

workload (peak) and firm’s size (lnsize and ATurn), after the 

regulatory change the effect of firm’s size proxies on audit fees is 

lower, and auditing workload does not have an impact on audit fees. 

6- Conclusions and limitations 

We examine audit pricing in the Iranian audit market and tests 

whether there is a fee premium for auditor size. We find that the IAO 

(the public sector audit firm) receives a fee premium on its audit, 

suggesting that it is the market power (size) of large audit firms that 

derives the audit fee premium. Prior audit fees studies attribute the fee 

premium charged by Big N auditors to their either brand name 

reputation or their market power (e.g, Hey et al. 2006). We show that 

when the brand name of the auditors is absent, it is the size of the 

audit firm that derives the fee premium. Consistent with similar 

studies on audit fees, we find that client’s size, auditing workload, 

new clients and loss reported clients are able to explain the variations 

of audit fee in Iran. The results also show differences in audit fee 

structures between public sector and private sector auditors. While 

coefficients of variables related to audit efforts and audit workload are 

significantly higher for the sub-sample of firms with IAO as the 

auditor, private auditors charge their new clients higher fees and loss 

making firma are able to pressure their auditors for lower audit fees. 

As a result of a regulatory change in Iran in 2002 which leads to 

the establishment of the IACPA and an increase in the number of 

private audit firms which allows private audit firms to provide audit 

services to listed firms in the TSE, the Iranian audit market has 

experienced an increase in the supply side of the market and, in turn, 

an increased level of competition between audit firms. Partitioning the 

study period for a sample of private audit firms before and after the 

regulatory change allows this study to see how this change in the 

188


market has affected the pricing of audit services. The results show that 

an increase in the supply side of audit services leads to a change in the 

determinants of audit fees. While the magnitude of clients’ size in 

determining audit fees declines, audit workload time is not a 

determinant factor for setting audit fees. Also, as a result of increased 

competition in the market, loss making firms are able to increase fee 

pressure on their auditors and pay a lower audit fees. 

One of the main limitations of this study is that the audit fee data 

before 2000 is not publicly available. As a result, the reported results 

in the sub-period analysis for pre 2002 period might be affected by the 

smaller sample size relative to the post 2002 period. Another 

limitation is that, given the very specific Iranian setting (presence of 

both public and private sector auditors and the lack of Big N auditors), 

cautious is needed in generalizing the results to other settings. 

The findings in this study have implications for the Iranian 

auditing profession. For example, the documented differences in audit 

pricing between two audit sectors may have an impact on the 

competitive nature of audit tendering process. Also, the results of the 

sub-period analysis may be useful for regulators to see how changes in 

the supply side of audit services and an increased competition in the 

audit market affect the pricing of audit fees. The findings in this study 

also provide some venues for future research. As this study finds that 

new clients of private auditors pay higher audit fees, future research is 

needed to examine this higher audit fees is related to the higher” startup” 

costs associated with new clients or is an indication of that the 

new auditor is willing to issue a more favorable audit report (audit 

opinion shopping). Also, it would be interesting to study whether there 

are differences in audit pricing between industry specialist and nonspecialist 

auditors. Given the mandatory audit firm rotation policy 

introduced by the Iranian Securities and Exchange Organisation 

(SEO) introduced in 2006 and the regulatory change in 2002, future 

189



May 23 and 24, 2012 

studies can examine whether auditors engage the policy of lowballing. 

Endnotes 

1 The law requires the election of the auditor by the shareholders at the 

shareholders’ annual general meeting. 

2 Based on the IACPA rules, to establish an audit firm, a minimum of 3 CAPs is 

required. 

3 The IAO and other government auditors are certified members of the IACPA. 

4 The SEO has a rule that al l listed firms in TSE must choose their auditors among 

“SEO’ trusted auditors” who are members of the IACPA. An audit firm member 

of IACPA can be a “SEO’s trusted auditor” when it meets all the requirements 

that have been set by the SEO. Even this penalty is not that severe as after few 

years, they can re-apply to be listed a “trusted auditor”. 

5 According to the IACPA’s disci plinary rules, only under two ci rcumstances the 

license of the auditor is revoked: 1) providing fake documents in time of CPA 

application, and 2) direct evidence on auditor’s material economic interests 

with the cl ient 

6 There is evidence that with the increase in the level of audit ri sk, auditors 

increase their audit effort to reduce the litigation ri sk (Schwartz 1997; 

Radhakrishnan 1999). 

7 Until recently (2011), there was not a requirement for firms listed on the TSE to 

discl ose audit fees in financial reports. Despite this, many companies voluntarily 

discl ose their auditor remunerations. 

8 This database is available at :www.rdis.ir 

9 This suggests that multi-collinearity does not seem to be a problem in the data. 

However, in the regression analysis, other tests like vari ance inflation factor 

(VIF), and condition index were considered, showing no effect of multicollinearity. 

10 Data in this study shows that the 64% market share of the IAO during 2000- 

2001 was decreased to 35% duri ng 2002-2006. 

11 The non-significant results for the prior 2002 period could be due to the small 

number of audit firm switches in this period. There are only 2 cases of auditor 

switches in this period. 

12 The results of estimating Model (1) for a sample of firms with the IAO as the 

auditor during two sub-periods (not tabulated) show that the coeffici ent for 

vari able loss is not significant. This shows that the negative association 

190


between vari able Loss and audit fees, documented in Table 3, is driven by 

sample of firms with private audit firms only after the 2002 period. 

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Table 1: Descriptive statistics 

n Mean or % Median St. Dev. 

Afee (IR million) * 1435 231.17 150.00 289.38 

Aturn 1435 0.79 0.725 0.492 

Lev 1435 0.72 0.719 0.341 

Switches 1435 11% - - 

Peak 1435 71% - - 

Loss 1435 21% - - 

InvRec 1435 0.43 0.421 0.213 

Auditor 1435 41% - - 

Size (IR million) * 1435 469,562 165,070 1,201,682 

1 million Rials = $US527 in 2000 and $US108 in 2007. Afee: is the audit 

fee in millions of Rials. Aturn: auditee’s asset turnover measured as the ratio 

of total sales divided by the ending total assets. Lev is the ratio of total debts 

to total assets. Switch = 1 is the client switched its auditor in year t, 0 

otherwise. Peak = 1 if the client’s balance sheet date us at 29/12, 0 

otherwise. Loss = 1 if the client reported loss in year t-1, 0 otherwise. InvRec 

is the ratio of sum of inventories and receivables to ending total assets. 

Auditor = 1 if the auditor is the IAO, 0 otherwise. Size is the client’s ending 

total assets in year t. The raw values for AFee and Size are shown for 

descriptive purposes only; logarithmic transformations are used in the 

regression model. 

197


ATurn 0.056 


May 23 and 24, 2012 

Table 2 

Pearson Correlation Coefficients 

lnAFee ATurn Lev Switches Peak Loss InvRec Peak*size Auditor lnsize 

0.162 

Lev 0.017 

0.073 

Switches 0.029 

0.327 

Peak 0.154 

0.000 

Loss -0.096 

0.003 

InvRec -0.025 

0.004 

Peak×lnsize 0.254 

0.000 

Auditor 0.128 

0.000 

lnsize 0.547 

0.000 

-0.037 

0.076 

-.020 

0.164 

-0.024 

0.133 

-0.271 

0.000 

0.261 

0.000 

-0.047 

0.020 

0.035 

0.036 

-0.146 

0.000 

0.027 

0.026 

-0.018 

0.219 

0.264 

0.000 

0.019 

0.250 

-0.008 

0.382 

0.031 

0.135 

0.048 

0.000 

-0.005 

0.430 

0.118 

0.000 

0.010 

0.366 

-0.012 

0.333 

-0.213 

0.000 

-0.020 

0.237 

198 

0.035 

0.108 

0.024 

0.193 

0.973 

0.000 

0.151 

0.000 

0.070 

0.006 

P-values are in parentheses. Correlations are based on 1340 firm-year 

observations over 2000-2006. lnAfee: is the natural logarithm of audit fee 

(million Rials). Aturn: auditee’s asset turnover measured as the ratio of total 

sales divided by the ending total assets. Lev is the ratio of total debts to total 

assets. Switch = 1 is the client switched its auditor in year t, 0 otherwise. 

Peak = 1 if the client’s balance sheet date us at 29/12, 0 otherwise. Loss = 1 

if the client reported loss in year t-1, 0 otherwise. InvRec is the ratio of sum 

of inventories and receivables to ending total assets. Peak×lnsize is the 

interaction term between peak and client’s size. Auditor = 1 if the auditor is 

the IAO, 0 otherwise. lnSize is the natural logarithm of client’s ending total 

assets in year t. 

-0.102 

0.000 

0.009 

0.108 

-0.014 

0.304 

-0.124 

0.000 

-0.009 

0.193 

0.034 

0.110 

-0.176 

0.000 

0.150 

0.000 

0.274 

0.000 

0.009 

0.000



May 23 and 24, 2012 

Year 2008 1.326 0.000 1.505 0.000 1.236 0.000 

Year 2009 1.375 0.000 1.540 0.000 1.317 0.000 

Industry fixed effect included 

F test 93.15 0.000 46.76 0.000 

52.54 0.000 

Adj.R 2 0.536 0.572 

0.507 

n 1435 582 

853 

lnAfee: is the natural logarithm of audit fee (million Rials). Aturn: auditee’s 

asset turnover measured as the ratio of total sales divided by the ending total 

assets. Lev is the ratio of total debts to total assets. Switch = 1 is the client 

switched its auditor in year t, 0 otherwise. Peak = 1 if the client’s balance 

sheet date us at 29/12, 0 otherwise. Loss = 1 if the client reported loss in year 

t-1, 0 otherwise. InvRec is the ratio of sum of inventories and receivables to 

ending total assets. Peak×lnsize is the interaction term between peak and 

client’s size. Auditor = 1 if the auditor is the IAO, 0 otherwise. lnSize is the 

natural logarithm of client’s ending total assets in year t. 

200



May 23 and 24, 2012 

Market Incentive’s ability to Predict Forecasts 

Management 

Abstract 

Vahid Biglari � 

Faculty of Business and Accountancy, University of Malaya 

Dr. Ervina Alfan 


Dr. Rubi Ahmad 


The results of previous studies about forecasts management are mixed. 

Some studies find that forecasts are informative and therefore forecasts 

restrict future earnings management. Other studies claim that forecasts are 

delusive and therefore lead to higher income increasing earnings 

management. To contribute in explaining the mixed results in the 

literature, this study examines the effect of stock transaction status on 

forecasts management. To do the experiment, out of 850 companies in 

NYSE, the researchers selected 311 based on availability of their forecasts 

information. The selected companies were divided into three groups of 

Buy, Hold and Sell. To do the experiment, Management Forecast Error 

and Forecasts Management of the abovementioned three groups were 

compared. The results indicated that in order to avoid negative earnings 

surprises, Sell side companies should engage more in downward Forecast 

Management. However, the result did not show any congruence with Bye 

and Hold companies. The findings also depicted that stock market 

*Corresponding Author: vahidbiglari@siswa.um.edu.my


incentives influence management voluntary disclosure. Moreover, the 

practical implications of this study can guide regulators to the necessity of 

considering market incentives and highlight the importance of voluntary 

disclosure. 

Keywords: Management Forecast Error – Management’s Forecasts – 

Forecasts Management - Stock Market Incentives- Buy - Sell 


Introduction 

Financial reporting is a process of communicating economic 

information to permit informed judgements and decisions by users of 

information1. Users of information, including investors, analysts and 

brokers usually lay emphasis on reported earnings. Investors form 

expectations about companies’ future profit. However, management 

are tempted to beat the stock market’s expectations(Choi, Walker, & 

Young, 2006; Graham, Harvey, & Rajgopal, 2005). Beating 

expectations shall produce positive consequences on stock price. 

Since management compensation is normally based on the stock price, 

the increase in the stock price would result in the interest of 

management. 

The traditional view about the stock price based compensation or 

option based compensation suggest that if management interests are 

aligned with the increase in the stock price, then management will 

have enough motivation to raise investors’ interests. However, the 

weakness of doing so is that it may cause management convey wrong 

information to investors and other stock market participants, as they 

want to increase stock price (Bundy, 2007). In such a case the increase 

in the stock price is not as a result of the company’s actual 

performance, but because of the managers’ ‘manipulation’. 

Although, there have been efforts to codify strict regulations to place 

management’s interest in line with investors’ interest, recent 

observations show that management do not fully pursue the investors’ 

expectations. To support this idea, Burgestahler and Eames(2006) 

proposed that managers forgo long-term growth and permanent value 

203



May 23 and 24, 2012 

increasing opportunities to avoid short term negative earnings 

surprises. The problem is more serious when it comes to voluntary 

disclosure. Gong, Li, & Xie, (2009 ) claim that since there are less 

strict regulation in voluntary disclosure, such kind of disclosure could 

contain strong bias signals. Earnings prediction (forecast) is among 

the well-known forms of voluntary financial disclosure. 

Firms often voluntarily provide earnings guidance. A key feature of 

this guidance is that the manner in which firms gather and 

communicate their forward-looking information is far less regulated 

than their mandatorily disclosed financial statement information 

(Hutton & Stocken, 2009). There have been some regulations to 

encourage management to issue more frequent earnings guidance. For 

example, The SEC's Safe Harbor Rule (1979) was enacted to 

protect managements from possible legal liability associated with 

errors in "good faith" forecasts. Also PSLR Act (1995), which 

shelters managers from litigation arising from unattained forwardlooking 

statements. 

Despite the regulatory encouragement for management to disclose 

more forward looking information, several studies claimed that 

various elements do induce managers to be bias in prediction of 

earnings2, causing increase in market expectations (Frost, 1997; 

Koch, 2002; J. Rogers & Buskirk, 2008). Therefore, a need arises to 

identify the factors that cause managers to willingly issue imprecise 

forecasts. Such forecasts might misguide capital market players and 

lead to misallocation of investments, which would result in dangerous 

and tragic consequences. A case might be named as Enron which was 

directly related to Earnings Management (EM) and opportunistic 

forecasts where managers engaged in income increasing activities to 

reach to those false optimistic projections. 

Enron failure along with other recent financial debacles have 

necessitated the improvement of accounting reports (Coffee, 2003; 

Healy & Palepu, 2003). Since income is most striking item in 

financial statements (Dechow & Scherand, 2004), the increase of its 

quality may enhance accounting information. Through the partial 

report of earning, EM and Forecast Management (henceforth FM) 

204


affect income quality. Being aware of the reason and process of FM 

and EM can be considered as the first step of preventing the bias of 

reported income (Erickson, Hanlon, & Maydew, 2004; Joshua, Tzur, 

& Yaari, 2006; Ronen & Yaari, 2007). This paper is among the 

studies that intend to reveal the rationale behind why and how FM 

occurs. 

Apart from usefulness of this study to identify market incentives of 

Management Forecasts Management (MFM) that helps analysts and 

investors to do better valuation of the companies stocks, from the 

theoretical prespective, this research helps explaining the mixed 

results that exists in the managements’ forecasts literature. 

From the aspect of informativeness of forecasts there has been two 

contraversial views in the literature. 

One view is that management use forecasts to convey insider 

information to outsiders. In fact, by forecasting earning they reduce 

information asymmetry. Indeed, by reducing information asymmetry, 

management tend to reduce the firm’s cost of capital (Lev and 

Penman, 1990). So when the company’s financial position is 

satisfactory, Due to their inclination to convey more positive 

information to shareholders, management increase prediction’s 

frequency (Kai et al 2009). However, issuing earnings’ prediction 

restricts management to do earnings management, i.e. they cannot 

manage earnings in a desirable direction. In fact, earnings forecasts 

lead to less earnings management (Duta and Gigler 2002). 

The other view is that, since stock price is sensitive to management 

forecasts (Das et al. 2008), management tends to report their forecast 

to an upper level of what it really is. In addition, because stock price is 

sensitive to managers’ forecast error (Kasznik and McNichols 2002; 

and Bartov et al. 2002), the more negative the forecast error is , that is 

reported earnings is less than predicted earnings, the more it is 

conceived as a sign of bad news and such bad news lead to dramatic 

fall of the stock price (D. Burgstahler & Dichev, 1997; Degeorge, 

Patel, & Zeckhauser, 1999). To prevent such stock price’s downfall, 

management tend to engage in income decreasing Forecast 

Management (FM) and income increasing earnings management to 

205



May 23 and 24, 2012 

beat the forecasts and have positive earnings surprises (Brown & 

Caylor, 2005; Gong et al., 2009 ; Ron Kasznik, 1999). 

These two views are seemingly contradictory. Because in the first 

approach it is believed that by doing sincere forecasts, management 

give true information to the market and therefore restrict themselves to 

do earnings management. Therefore forecasts are truthful. The second 

approach is that management use earnings and forecasts management 

as a tool to create positive earnings surprise that lead to stock price 

fake appreciation or prevent stock price depreciation. A question that 

arises by considering these two contrasting views is that what is the 

underlying element that has been overlooked in the previous 

literature? This research seeks to find a so far disregarded element that 

is the cause of that contradiction. Thus, the main question of this 

research is whether there is any form of market incentive that causes 

management to do forecasts management to have positive earnings 

surprise in some situations but don’t do earnings and forecasts 

management when those situations are not present. In other words, 

this research suggests that, as a measure of market incentive, the 

volume of stock asks and bid can explain why sometimes management 

undertake earnings and forecasts management and why sometimes 

they don’t do them. In fact, whenever the stock volume of bid is 

higher, i.e. the firm has more opportunities for growth and investor’s 

preference, management has more opportunities to do income 

increasing earnings management to reach their forecasts (Abarbanell 

and Lehavy 2002). On the other hand whenever the stock volume of 

ask is higher, i.e. there is less opportunities for firm’s growth and 

firms has high financial leverage, or is facing financial 

distress management has more incentives to do income decreasing 

forecasts management to have positive earnings surprise (Ajinkya, 

Bhojraj, & Sengupta, 2005; J. L. Rogers & Stocken, 2005). That is 

because management is facing with several risks namely risk of 

termination of management contract (Stein, 1989) litigation risk 

(Brown & Caylor, 2005; Cao & Narayanamoorthy, 2005; R. Kasznik 

& Lev, 1995) and takeover risk (Beniluz, 2010; DeAngelo, 1988; 

Warner, Watts, & Wruck, 1988; Weisbach, 1988). 

206


By focusing on market incentive as a factor that triggers forecasts 

management, this paper tries to address the reason for the existing 

contradiction in the literature that why sometimes Forecasts are found 

to be informative and sometimes forecasts are found to be 

opportunistic. Therefore, the focus of this study is to examine the 

effect of fundamental market factors on management incentives in 

relation to Forecasts Management (FM).This procedure may develop a 

framework which could be used as a signaller for delusive financial 

reporting. 

Companies’ Market Status and Management Forecasts 

This section mentions to some of the researches that justify the effect 

of market factors on FM. Das, Kyonghee, & Sukesh (2008) suggest 

that expectation management will lead to increase in stock prices. 

Also Beyer (2009) showed that there is a strong relation between 

forecasted earning and stock price. Frankel and McNichols and 

Wilson (1995) show that earning forecast lead to deacrease In the 

companies cost of capital. Therefore change in the stock price can be a 

strong enough incentive for management to manipulate forecasts. 

Although changing the stock price and therefore the market status of 

the company can be a good enough incentive for management to bias 

their forecasts, depending on the financial status of the companies, 

they report their forecasts differently. Whereas koch (2002) findings 

show that in companies that are financially distressed, management 

forecasts are less credible than firms which are not financially 

distressed, Miller (2002) found that, companies that have higher 

probability of growth are more likely to disclose their information to 

market. This is consistent with the results documented by Frankel and 

McNichols and Wilson (1995), who found that firms seeking more 

external financing tend to issue management forecasts more 

frequently, 

Consistent with koch (2002) and Miller (2002) in this research we 

hypothesise that the companies that have unsatisfactory market 

207



May 23 and 24, 2012 

perspective issue more biased forecasts than the companies who are 

satisfactory market perspective. 

Research Objective 

The aim of this research is to identify the possible market factors that 

influence managers to issue biased forecast. These reasons are 

captured in a model that examines the occurrence of FM based on the 

aspect of managerial motivation. 

The remainder of this paper is organized as follows. The next section 

develops the hypotheses. After that, the methodology for testing the 

hypotheses will be discussed. Then findings will be shown. Last 

section summarizes and concludes the findings of this paper. 

Hypotheses 

Prior researches have confirmed that management engage in FM to 

create earnings surprise (Ron Kasznik, 1999; Xin, 2007). The reason 

for doing so is that, management perceives that the negative price 

effect of managing forecasts downward is less than the future positive 

price effect of positive earnings surprises (Beyer, 2009; Downing & 

Sharpe, 2003). According to the transaction status of companies’ 

stocks, the call for Buy (Sell) of the stocks may significantly outweigh 

the call for Sell (Buy) of the same stocks. Based on whether Buy 

(Sell) for stocks outweigh Sell (Buy) the company’s stock transaction 

status shall be classified as Buy (Sell). Since sell side companies 

suffer from unsatisfactory stock market, it is expected that their 

motivation to do downward forecast management be higher. By doing 

a downward forecast management the status of stock market is not 

affected significantly. However, a positive future earnings surprise 

could positively shock the stock market and improve stock market 

status. It is expected that Sell companies have higher rate of positive 

forecast error (earnings surprises) than Buy companies in a small 

period, i.e. close to zero forecast error. Based on the research 

208


questions and the clarification of the points in the ongoing paragraph 

the hypothesis of the study has been formulated as following: 

Hypothesis 1: The ratio of positive to negative forecast error 

significantly differs among the companies that represent different 

stock market status (Buy and Sell). 

According to first Hypothesis 1, the sell companies would reflect 

higher rate of positive forecast error. As the literature reveals, the 

assumption is that management of Sell side companies would either 

employ income increasing EM to reach to forecasted earning (Habib 

& Hossain, 2008), or may engage in income decreasing forecast 

management(Durtschi & Easton, 2005) to reach to zero or positive 

forecast error. In other words, if the company is in sell position, 

management may issue lower forecast than the real one to lessen the 

expectation of outsiders (Wang, 2003). Based on the result of the 

firm’s ordinary operation management would then report an earning 

which is as equal as or higher than the forecast (report positive 

earnings surprise). By doing so, management raise the bids for 

company’s stocks, and subsequently the increase of the company’s 

stock price. 

Stunda (2008) suggested that firms that do meet analysts’ forecasts are 

more likely to issue estimations that they could achieve. Therefore 

from the first hypothesis, it would be suggested that Sell companies 

are more likely to beat their own forecasts. Provided that the first 

hypothesis is accepted, and in accordance with Stunda’s (2008) 

allegation, it can be implied that the companies that are classified as 

Sell are more likely to meet the analysts’ forecasts. This is due to the 

downward expectation management of the analysts. 

Similarly, it is suggested that the difference between the ratios are 

deemed to be related to forecast management. That is, sell firms do 

engage more in doing forecast management activities to meet or beat 

their forecasts. So, the second hypothesis is developed as follows: 

Hypothesis 2: The sell companies engage more in forecasts 

management activities than buy companies. 

Research method 

209


of management forecasts. In other words, what factors are shaping 

manager’s decisions of precise or imprecise forecasts? It is thought 

that stock market status could be a motive that affects manager’s 

decisions to issue forecasts. As a result, we found that stock market 

status significantly impacts management decision on issuing forecasts. 

This study may have both theoretical and practical implications. 

Theoretically it may widen the appreciation of opportunistic motives 

of management for doing forecasts management. Additionally it may 

shed light on some other issues including the necessity to improve 

management’s voluntary disclosure and show the gaps which may be 

existed in the domain of other studies regarding the counter effects of 

downward forecast management and positive earnings surprises. 

Practically as it was shown the study addressed the four hypotheses 

which illustrated the interrelation of forecasts errors, earnings 

management and forecasts management. Finally, in addition to the 

fact that the findings are in parallel to other researches in case of 

opportunistic earnings and forecasts management (Abarbanell & 

Lehavy, 2003; R. Kasznik & Lev, 1995; Ron Kasznik & McNichols, 

2002; Matsumato, 2002), the findings contribute to the literature by 

documenting that the stock market Bid and Ask information are able 

to predict management’s forecasts errors. 

The information about asks and bids of stocks not only are not kept 

for research or statistical analysis but they are simply swapped with 

consecutive new information. A Practically implication of this 

research is that not only these types of information have important 

implications and should be kept for empirical and statistical analysis, 

but also they convey great informational values that can be utilized by 

researchers and investors, as it might shed light on invisible attributes 

of the stocks. 

This study adds new findings to the management forecast literature. 

The previous studies suggested that since management’s forecast 

convey insider information to the outsiders, it lessen information 

asymmetry and decreases costly litigation of stockholders against the 

company (Lennox & Park, 2006). In addition, it helps the company to 

have clear and transparent financial reporting (Hirst, Koonce, & 

217



May 23 and 24, 2012 

Venkataraman, 2008). The results contributes to the literature since 

they document that management’s forecasts contains errors that are 

predictable considering to the stock market Bid and Ask. The results 

intensely warn investors to evaluate the management’s forecasts 

before forming expectations of the company’s profit, especially when 

there is great temptation in the market for sale of the company’s 

stocks. 

Even though this study found that management use downward 

forecast management to reach positive earnings surprises, Wang 

(2003) suggests that this process should have a short time increasing 

effect on stock price, because investors learn from accuracy of the 

company’s past financial reporting. If management continues doing 

downward forecast management for a number of periods, investors 

will lose their trust to the company. However, as long as the short 

term increase is considered the scenario of earnings management and 

forecast management holds to be valid. 

As Hutton & Stocken (2009) mention forward-looking information is 

far less regulated than their mandatorily disclosed financial statement 

information. Under the Securities Exchange Act of 1934 and the SEC, 

it is more burdensome for plaintiffs to successfully litigate when it is 

more difficult to detect misrepresentation. Private Securities Litigation 

Reform Act of 1995 (PSLR Act) strengthened the 1934 Act provision 

by restricting management's liability to forward-looking statements. as 

a result of such regulations managers are less prosecuted for issuing 

biased forecasts in USA (J. L. Rogers & Stocken, 2005). 

It is suggested that by considering market status of the companies as 

incentive for management to issue imprecise forecasts standard setters 

and regulators can provide better incentive for management for 

providing precise forecast. Regulations should keep higher watchdogs 

on earning projections of the companies that do not have satisfactory 

stock market status. This study focused on the issue of Stock market 

incentives for Earnings Management and Forecasts Management in 

the context of the United States whereas others may take the findings 

of this study as the infrastructure of their work and follow the issue in 

218


some other parts of the world. The researcher here studied these 

market incentives for FM. Other researchers may study on how to 

control malevolent EM and FM. 

While the present study emphasizes market incentives role on 

earnings and forecasts management, the findings should neither be 

overestimated nor underestimated. Gathering data from other 

regulatory contexts may shed more light on the effect of market 

incentives of earnings and forecasts management. 

Endnotes: 

1. A Statement of Basic Accounting Theory, Published June 2001 by 

American Accounting Association . 

2. Some examples of such elements are financial stress (Frost, 1997), 

financial distress (Koch, 2002). Also Rogers &Buskirk(2008) report that 

when doing forecasts,managers selectively disclose good news. 

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223


Table 1 - descriptive statistics for MFE 

No. of the 

companies 


May 23 and 24, 2012 

Mean Median Standard 

Deviation 

224 

Kurtosis Skewness 

311 -0.4913 -0.1518 1.558 1.410 -0.614 

Table 2- The comparison between the ratio of positive to negative 

forecast errors in Buy, Hold and Sell groups in large and small 

distances around zero. 

±25.83 (1stdev) 

±4 (0.15stdev) 

±1(0.03stdev) 

N overall N buy N hold N sell 

388 0.64# 130 0.59 136 0.58139 

5 

293 0.8## 102 

197 

1.09574 

5 

0.74576 

3 

122 

0.76811 

6 

100 0.67*** 91 1.07*** 

74 1 61 0.79*** 62 1.69*** 

±0.3 (0.01stdev) 114 1.375 41 1.05** 35 0.94*** 38 2.8¢*** 

±0.2 

(0.007stdev) 

76 1.53### 24 1.18* 22 0.83*** 30 

3.28£** 

* 

*test of proportion between the ratio of positive to all forecast error is significant between buy 

and sell at .001 

**test of proportion between the ratio of positive to all forecast error is significant between 

buy and sell at 0.05 

***test of proportion between the ratio of positive to all forecast error is significant between


hold and sell at .05 

#binomial test of difference between the frequency of positive to negative forecast error is 

significant at .001 level. 

##binomial test of difference between the frequency of positive to negative forecast error is 

significant at .001 level. 

###binomial test of difference between the frequency of positive to negative forecast error is 

significant at 0.1 level. 

Table 3 – Statistics related to the distribution of change in income 

from first to third quarter after cleaning. 

Mean -6.5792 

Median -2.2566 

Mode .00 

Skewness -.794 

Kurtosis .958 

Table 4 - the result fo running this equation: FR = αICHAN + C + ε 

α 

0.2531 

225 

t-statistic 

2.1197 

probability 

0.0364 

R-squared 0.04099 Akaike info criterion 8.6727 

Adjusted R-squared 0.0409 Durbin-Watson stat 2.2247 

F-statistic 4.7266 

Table 5 - Test of comparison of mean of FM among companies that 

have classifies to three groups of Buy, Hold and sell, according to 

their value of SBA 

N Mean Minimum Maximum 

sell 29 -0.0020 -0.3 0.01 

hold 29 0.0036 -0.3 0.04



May 23 and 24, 2012 

buy 28 0.0023 -0.2 0.02 

Total 86 0.0013 -0.3 0.04 

Buy-sell f 1.99 Sig. 0.16 

Hold-Sell f 2.63 Sig. 0.11 

Buy-Hold f 0.15 Sig. 0.69 

Table 6 – Test of comparison of proportion of negative to all forecasts 

management among three groups of stocks, namely, Buy, Hold and 

Sell. 

all 

±0.2 MFE 

(0.007stdev) 

N overall N buy N hold N sell 

٨۶ 0.64 ٢٨ 

83 

0.3855 

42 

٢٧ 

226 

0.28571 

4§ 

0.25925 

9¥ 

٢٩ 

28 

0.3448 

28 

0.3214 

29 

٢٩ 

٢٨ 

0.55172 

4§ 

٠.۵٧١۴ 

¥ 

§test of proportion between the ratio of negative to all forecast management is significant 

between buy and sell at .05 

¥ test of proportion between the ratio of negative to all forecast management is significant 

between buy and sell at .01


Figure 1 - Distribution of MFE in small distance around Zero for Buy, 

Hold and Sell companies. 

Mean .4268 

Median .0000 

Mode .00 

Skewness -.656 

Kurtosis 1.182 

Figure 2- Distribution of FR after cleaning 

227


Figure 3 - Distribution of FM 


May 23 and 24, 2012 

Figure 4- Summery of Hypothesis 1 and Error! Reference source 

not found. 

1 

2 

Stock 

Transaction 

Groups (Buy, 

Hold, Sell) 

Forecasts 

Management 

228 

Forecast 

s Errors

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