The effects of accounting complexity - Lundquist College of ...

Abstract: 

Accounting complexity and meeting expectations 

Joshua J. Filzen 

University of Oregon – Lundquist College of Business 

Kyle Peterson 

University of Oregon – Lundquist College of Business 

First Draft: January 4, 2010 

Current Draft: March 26, 2010 

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We examine whether firms with complex accounting are more likely to meet or beat analysts‘ 

expectations. We proxy for accounting complexity using the residuals from a regression that 

explains accounting policy disclosure length. Our results suggest that firms with complex 

accounting are more likely to just beat expectations than just miss expectations (i.e., they can 

meet expectations at the margin). However, in general, complex firms are more likely to have 

extreme absolute forecast errors. Additional tests reveal that the increased propensity to meet 

expectations is at least partially driven by expectations management. However, the expectations 

management is one-sided; complex firms appear to walk down expectations, but not prevent the 

walk up of expectations. These results suggest managers take advantage of accounting 

complexity to meet analyst expectations in limited circumstances. 

Keywords: accounting policies, disclosure, analyst expectations, earnings management 

JEL classification: D82, G12, M41 

We thank Lian Fen Lee and Isho Tama-Sweet for their helpful comments and suggestions. We 

are solely responsible for all errors and omissions. Josh Filzen gratefully acknowledges support 

from the Accounting Circle at the Lundquist College of Business.

1. Introduction 

The benefits of meeting analysts‘ earnings expectations are well documented in the 

literature (Dechow and Skinner 2000, Barth, Elliott, and Finn 1999, and Skinner and Sloan 

2001). Given these benefits, it is not surprising research also suggests managers manage 

earnings to meet or beat analysts‘ expectations (Degeorge, Patel, and Zeckhouser 1999; Payne 

and Robb 2000; Abarbanell and Lehavy 2003; Burgstahler and Eames 2006; Matsumoto 2002; 

Barton and Simko 2002; Graham, Harvey, and Rajgopal 2005). Prior research also suggests that 

in specific settings where accounting is more complex that firms manage their earnings. Picconi 

(2006) and Bergstresser, Desai, and Rauh (2006) find that investors and analysts do not 

understand the effect of changes in pension plan parameters on future earnings and managers 

exploit this limited understanding to manage earnings. We attempt to understand the effect of 

accounting complexity on meeting expectations in a broader context. Specifically, we examine 

whether accounting complexity makes it more likely for managers to meet analyst expectations. 

We also test whether accounting complexity is associated with less extreme earnings forecasts, 

consistent with earnings smoothing. Finally, we examine whether firms with complex 

accounting are more likely to meet expectations using expectations management. 

We view accounting complexity similar to Peterson (2009b) and SEC (2008) in that 

complexity increases uncertainty related to accounting information for both preparers and 

financial statement users. We conjecture that accounting complexity increases the probability of 

meeting expectations due to two effects. First, firms with complex accounting require more 

subjective judgment by managers, increasing their ability to manage earnings without detection. 

Second, prior literature also suggests complexity creates information asymmetry between 

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managers and investors or analysts, making it easier for managers to manage analysts/investors 

expectations (Peterson, 2009a; Li, 2008; Miller, 2008). 

Our proxy for complex accounting is the abnormal length of accounting policy 

disclosures in the 10-K. Abnormal length is measured using the residuals from a regression of 

the number of words in the accounting policy disclosures on explanatory variables that include 

year and industry fixed effects, other financial indicators, and the number of business segments. 

Peterson (2009a) finds that abnormally long accounting policies are associated with more 

dispersed beliefs of analysts and investors. 

Our sample is 83,837 firm-quarters from 1994-2008 with necessary data on Compustat, 

CRSP, and IBES for which we are also able to extract accounting policies from 10-K filings 

from the SEC Edgar database. We hypothesize that accounting complexity is positively 

associated with the propensity to meet or beat analyst expectations. We first estimate a logistic 

regression of firms with forecast errors close to zero to test whether accounting complexity is 

associated with meeting expectations. Our results are consistent with firms with complex 

accounting being more likely to report small positive forecast errors than small negative errors, 

suggesting complexity gives managers an edge in meeting expectations. 

We also examine the effect of accounting complexity on forecast errors more broadly to 

better understand if managers of complex firms are also able to reduce the incidence of extreme 

forecast errors. Results consistent with this would provide evidence that managers are able to 

smooth forecast errors, not just beat expectations at the margin. We do not make any strong 

predictions about this effect because of two countervailing effects. On the one hand, managers 

may be able to manage earnings or expectations sufficient to allow smoothing across time. On 

the other hand, Peterson (2009a) finds that abnormal accounting length is positively associated 

2

with analyst forecast error and dispersion because it is more difficult to forecast for complex 

firms. This increased forecasting difficulty may make it more difficult for managers to engage in 

smoothing, and at the same time provide managers with incentives to make their earnings more 

predictable (smoother). We separate firm quarters into seven bins based on the size of the 

absolute forecast error. We examine the distributional properties of forecast errors in a 

univariate and multivariate setting and find that firms with complex accounting are more likely 

to have extreme forecast errors, which is inconsistent with smoothing but consistent with the 

findings in Peterson (2009a). 

Because meeting expectations could be the result of earnings management or 

expectations management, we also test whether complex accounting is associated with 

expectations management. Specifically, we perform two sets of tests to answer this question. 

First, we re-estimate the logit regression from our first hypothesis using the first forecast of the 

period (as opposed to the most recent forecast). We expect that if expectations management is 

present our results will weaken when using the first forecast of the period rather than the last. 

While the coefficient is positive and significant on our proxy for accounting complexity (p-value 

less than 0.01) when using the most recent forecast for the quarter, it is insignificant when using 

the first forecast of the quarter (p-value of 0.715). These results indicate that the expectations for 

complex firms changed during the quarter to make it more likely they beat expectations. 1 We 

also test two other logistic regressions that provide evidence of expectations management. The 

first tests whether the firm beat expectations, but would not have done so based on the first 

forecast in the quarter, which would be consistent with expectations management (and therefore 

1 While we cannot say definitively that the firm engaged in expectations management, the trend of the forecasts 

over the period suggest analysts did change their expectations for complex firms that allowed them to beat 

expectations. 

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expect a positive coefficient on complexity). The second tests whether the firm missed 

expectations, but would have beat based on the first forecast in the quarter, which would be 

inconsistent with expectations management (and therefore we expect a negative coefficient on 

complexity). The first test results are consistent with complex firms exhibiting expectations 

management. The coefficient from the second test (for those firms that missed the forecast) is 

signed as predicted, but is not statistically different from zero. These results suggest complex 

firms are able to walk down forecasts to meet expectations, but not prevent the walk up of 

expectations. 

Our research contributes to the understanding of how firms meet analyst expectations. 

Prior research suggests that incentives (e.g., compensation and maintaining streaks) and 

constraints (e.g., balance sheet) influence whether firms are able to meet expectations. The 

evidence presented here suggests the complexity of the accounting environment also contributes 

to managers‘ ability to meet expectations. This ability to meet expectations appears to be 

partially driven by expectations management. In addition, our research provides some large 

sample empirical evidence to support the beliefs and the anecdotes that arise from events like 

those associated with Enron and Fannie Mae that investors may be skeptical about the earnings 

of complex firms (Chanos, 2006; Greenberg, 2008; Miller and Bahnson, 2002). 2 Finally, by 

examining accounting complexity using the complete set of accounting policies, this research 

contributes to the call by Fields, Lys and Vincent (2001) to examine the effects of accounting 

choice more broadly. 

2 Miller and Bahnson (2002) identify the following statement made by Warren Buffet. ―If I pick up an annual 

report and I can‘t understand a footnote, I probably won‘t—no, I won‘t—invest in that company because I know that 

they don‘t want me to understand it.‖ 

4

The next section develops the hypotheses, including a discussion of prior literature. 

Section 3 discusses the data and research design. Section 4 presents the results of the tests, and 

Section 5 concludes. 

2. Prior Literature and Hypotheses 

2.1 Meeting analyst expectations 

Our proxy for earnings management is a firm‘s propensity to meet or beat analysts‘ 

expectations. Bartov, Givoly, and Hayn (2002) and Kasznik and McNichols (2002) document a 

market reward for meeting analysts‘ forecasts, while Skinner and Sloan (2002) document a larger 

response to negative earnings surprises than to positive earnings surprises. These studies provide 

evidence of a motivation for managers to meet or beat analysts‘ expectations. In addition, 

Degeorge et al. (1999) use distributional analysis to provide evidence that firms engage in 

earnings management to meet or beat analysts‘ expectations. Finally, Brown and Caylor (2005) 

explain that in the period of 1996-2002 meeting or beating analyst expectations (MBE) is the 

most important threshold for managers and suggest researchers focus on MBE of quarterly 

earnings when examining earnings management in more recent periods. 

Besides the findings from prior literature discussed above, there are other advantages to 

using MBE in our tests as opposed to examining discretionary accruals. Healy and Wahlen 

(1999) explain that an output measure like MBE allows researchers to capture all forms of 

earnings management. McNichols (2003) argues that no single model (e.g. an accrual model) 

can capture the heterogeneous discretion that managers have when deciding how to manage 

earnings. Also, there is no consensus in the literature that there is a link between discretionary 

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accruals and MBE. Graham et al. (2005) survey CFOs and find that although managers are 

concerned with meeting analyst expectations, they are more likely to decrease discretionary 

spending and delay projects than manage accruals. Although Matsumoto (2002) finds some 

limited support for the use of discretionary accruals to MBE, Athanasakou, Strong, and Walker 

(2009) find no evidence of the use of accruals management to MBE in a sample of UK firms. 

For these reasons, we focus our research on the MBE outcome-based approach to provide 

inferences of earnings management. 

2.2 Hypotheses 

Peterson (2009b) defines accounting complexity as ―uncertainty related to the mapping of 

transactions or potential transactions and standards into the financial statements.‖ The SEC‘s 

Committee on Improvements to Financial Reporting (ACIFR) defines complexity for preparers 

as the difficulty ―to properly apply [U.S. GAAP] and communicate the economic substance of a 

transaction‖ and for investors as the difficulty in understanding the ―economic substance of a 

transaction or event and the overall financial position and results of a company‖ (SEC, 2008). 

We conjecture that accounting complexity should be positively associated with managers‘ 

ability to meet or beat analyst expectations. The basis for this expectation rests on the following 

two reasons, which are supported by a small set of studies examining accounting complexity. 

First, firms with complex accounting are assumed to have more opportunities to manage earnings 

due to additional subjective accrual estimates. 3 This is consistent with the research by Picconi 

(2006) and Bergstresser et al. (2006), who find that investors and analysts do not understand the 

3 Subjective estimates could be thought of as necessary to induce uncertainty, which is an essential attribute of 

complex accounting. For example, the least subjective account on the balance sheet is cash, which would also be 

considered low complexity. 

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effect of changes in pension plan parameters on future earnings. They find managers exploit this 

complexity by increasing rates of return assumptions on pension assets: (1) to offset the effect of 

anticipated future bad news, (2) when the assumptions have a greater impact on earnings, and (3) 

when managers are attempting to acquire other firms or exercise stock options. This research on 

pensions is similar to other earnings management papers that examine specific areas of 

accounting and find evidence that suggest earnings management (Fields, et al., 2001 and Ronen 

and Yaari, 2008). 

Second, in light of prior research, we also view accounting complexity as potentially 

increasing the information asymmetry between managers and financial statement users. This 

information asymmetry makes it easier for managers to manage analysts/investors expectations, 

which increases the probability that the firm will meet those expectations. 4 Research supports 

this view of complexity increasing information asymmetry. For example, Peterson (2009a) finds 

that long abnormal accounting policies are associated with increased trading volume and return 

volatility around firms‘ earnings announcement. In a related setting, both Li (2008) and Miller 

(2008) argue that longer 10-K filings make it more difficult for investors to understand and 

interpret the financial information contained in the 10-K. Li (2008) shows that managers use 

longer disclosures to mask poor future performance. 5 Miller (2008) documents that firms with 

longer 10-K filings are associated with fewer trades by, and more disagreement among, smaller 

investors. We conjecture that these two effects, subjective accrual estimates and managing 

4 Our tests, discussed in Section 3, are not specific in identifying the mechanisms used to manage expectations. 

These mechanisms could include management forecasts (Kasznik, 1999 and Burgstahler and Eames, 2006), 

conference calls (Cotter, Tuna, and Wysocki, 2006), or other disclosures. 

5 Li (2008) uses both a measure of annual report length and the Fog Index to measure the difficulty users have in 

reading financial statements. We do not use the Fog Index in this study because accounting policy disclosures are 

more technical in nature relative to the whole annual report, reducing the index‘s ability to capture cross-sectional 

variation for these disclosures across firms. 

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expectations, will be especially prevalent at the margin (i.e., firms close to analysts‘ 

expectations). 

H1: Firms with more complex accounting are more likely to meet or beat analysts‘ 

expectations at the margin than firms with less complex accounting. 

While H1 predicts managers of complex firms are better able to meet expectations at the 

margin, we also examine whether the ability of complex firms to meet expectations applies more 

broadly. Complexity may be such a valuable tool that it allows managers to string together very 

consistent streams of meeting expectations. 6 However, in this broader context, managers of 

complex firms face a countervailing effect on their desire and ability to smooth. Peterson 

(2009a) finds that complex firms have earnings forecasts with higher dispersion and error, 

suggesting it may be more difficult for analysts to forecast earnings for complex firms, and 

subverting the manager‘s ability to meet expectations generally. Consistent with the approach of 

Abarbanell and Lehavy (2003), we examine the association between accounting complexity and 

the magnitude of forecast errors. Because we cannot predict ex ante which force (the ability to 

smooth vs. the difficulty in forecasting) is stronger, our hypothesis is stated as a conditional 

prediction: 

H2: If managers of complex firms engage in smoothing, those firms should have less 

extreme forecast errors than firms with less complex accounting. 

Finally, we examine whether the ability of complex firms to meet analyst expectations is 

a result of expectations management. Bartov et al. (2002) find a premium to meeting or beating 

6 Our question here is similar in principle to research that suggests managers engage in earnings smoothing. 

Gaver, Gaver, and Austin (1995) document a relationship between discretionary accruals and bonus plans that is 

more consistent with income smoothing. DeFond and Park (1997) also provide evidence that firms smooth 

earnings; however, the results in Elgers, Pfeiffer Jr., and Porter (2003) suggest smoothing is not present when using 

randomization tests. Graham et al. (2005) survey CFOs and find that CFOs are both interested in meeting analyst 

expectations and having smooth earnings. 

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expectations even when it is the result of expectations management. Richardson, Teoh, and 

Wysocki (2004) find the walk-down of expectations is more pronounced when managers are 

selling stock. We follow this prior research by examining whether complex firms are more 

likely to have expectation paths that suggest expectations were managed. Because of increased 

information asymmetry resulting from accounting complexity discussed above, we believe firms 

with more complex accounting can more easily manage expectations. Therefore, we predict the 

following: 

H3: Firms with more complex accounting are more likely to have forecast paths that are 

consistent with expectations management than less complex firms. 

3. Research Design 

3.1 Measuring accounting complexity 

We follow Peterson (2009a) to proxy for accounting complexity by estimating abnormal 

accounting policy disclosure length (AB ACC POL) as discussed below. As with any proxy, and 

especially a new one, we discuss a few issues with measurement of this construct. 

Theoretical (e.g., Diamond, 1985; Diamond and Verrecchia, 1991; Barry and Brown, 

1985; see also Botosan, 1997) and empirical (see Botosan, 1997; Lang and Lundholm, 1996) 

research suggests that additional disclosure is processed by the market and reduces information 

asymmetry and cost of capital (i.e., additional disclosure is better). Because our proxy for 

accounting complexity is really a measure of abnormal disclosure length, our prediction that 

accounting policy disclosure length increases uncertainty appears to contradict this widely held 

result. 7 We make two points regarding this seeming contradiction. First, our measure could 

7 Botosan (1997) also makes the claim that disclosure quality is difficult to measure separate from disclosure 

quanitity (or the level of disclosure). However, researchers have generally assumed that quality and quantity are 

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capture both complexity and more accurate, quality disclosure at the same time. Abnormally 

long accounting policy disclosures could be high quality disclosures about complex transactions 

that would still lead to our hypotheses. This idea was articulated by John Dingell, a U.S. 

Congressman, in the following statement about Enron in 2001: ―What we are looking at here is 

an example of superbly complex financial reports. They didn‘t have to lie. All they had to do 

was obfuscate it with sheer complexity‖ (McLean, 2001). Second, we rely on prior empirical 

studies that suggest disclosure length may not always have favorable effects on the information 

environment (Li, 2008; Peterson, 2009a; Miller, 2009). 

Our measurement of abnormal accounting policy disclosure length (AB ACC POL) is 

consistent with Peterson (2009a). This measure is the residuals from a regression of the log 

number of words of the accounting policies disclosure on explanatory variables described in 

equation (1). We obtain accounting policy disclosures from 10-K filings from Edgar using the 

Python programming language. See Section 4.1 for more detail about this process. 

LOG( 

ACC POL LEN ) 

it 

� �1 

� �2BTM 

it �1 � �3LOG( 

ASSETS) 

it �1 

� �4LOG( 

SEG) 

it �1 

�� 

FinancialIndicators 

� ��Year 

� �� 

Industry � � 

where ACC POL LEN is the number of words counted from the accounting policy disclosure 

section of the firm‘s 10-K. BTM is the book-to-market ratio of the firm at the beginning of the 

year. LOG(ASSETS) is the natural logarithm of total assets of the firm at the beginning of the 

year. LOG(SEG) is the natural logarithm of the number of operating segments for the firm 

measured at the beginning of the year. Below is a list of the financial indicator variables 

positively related. While distinguishing between quality and quantity might be an (unattainable) empirical ideal, 

relying upon a disclosure quantity proxy (as in this paper) still sheds light on our understanding of the effects of the 

quantity of disclosure. 

10 

it 

(1)

included in the regression above that would normally require disclosure in the accounting policy 

section: 8 

Variable Name Description 

Compustat Data 

Item Name 

R&D Research and Development xrd 

DEFERRED REVENUE Deferred Revenue drc or drlt 

INTANGIBLES Intangibles or Goodwill gdwl or intan 

MINORITY INT Minority Interest mib or mii 

FCURRENCY Foreign Currency Translations cicurr 

CAPITAL LEASE Capital Lease dclo 

PENSION Pension pnca 

STOCK COMP Stock Compensation stkco 

DISC OPS Discontinued Operations do 

ACQUISITIONS Acquisitions aqc 

CAPITALIZED INTEREST Capitalized Interest intc 

DERIVATIVES Derivatives cidergl 

Finally, we include industry and year indicators to capture industry and time trends in 

accounting policy disclosures. The results of these regressions are untabulated, but are 

consistent with those presented in Peterson (2009a). The model provides significant explanation 

of the length of the accounting policy disclosures with an R 2 of 0.579. 

8 One criticism of including the financial indicator controls may be that we are controlling for some portion of 

accounting complexity in this measure. We note that we find consistent results with those presented in the paper 

when using a mean adjusted measure by industry-year. 

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3.2 Tests 

Our main dependent variables of interest are all calculated using analyst forecast error 

(FCSTERR), which is calculated as: 

FCSTERRit = Actual IBES EPSit – Mean EPS Forecastit (2) 

We obtain analyst estimates from the IBES Summary History – Unadjusted statistics file 

and actual EPS numbers from the IBES Actuals file. We adjust for stock splits with the 

approach suggested by Robinson and Glushkov (2006) which utilizes the CRSP cumulative 

adjustment split factors from the daily file. The EPS forecast used is the last consensus forecast 

of the quarter before the earnings announcement date. 

3.2.1 Tests of H1 

Our first test examines whether complex firms are able to meet expectations at the 

margin. Therefore, we test a logit model where the dependent variable (Miss_Beat) is one if 0 � 

FCSTERRit < 0.02 and zero if -.02 � FCSTERRit < 0. 

The classification of just meeting or beating based on forecast error is not measured 

consistently in prior literature. Some studies (Prawitt et al. 2009; McAnally, Srivastava, and 

Weaver 2009) use scaled forecast error, while others use cents (Athanasakou et al. 2009; Cheng 

and Warfield 2005; Frankel, Johnson, and Nelson 2002; Yu 2008). We use two cents as our 

designation of being close to forecast 9 ; however, our results are somewhat sensitive to this 

particular categorization. Scaling forecast error by price, which is common in the literature, with 

9 In untabulated tests, we exclude 0.02 in the dependent variable to ensure the bins for beating and missing are 

equally sized. The interval then becomes: Miss_Beatit = 1 if 0 � FCSTERRit < .02. This does not affect the results 

presented in the tables. 

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�� 

just beating/missing at 0.001, obtains consistent results. However, varying the interval based on 

cents provides mixed results. Using an interval -.02≤FCSTERR≤.01 provides consistent results, 

but using -.01≤FCSTERR≤.01 obtains insignificant results for H1. We believe that a slightly 

larger interval (2 cents versus 1 cent) better captures the incentives and abilities of managers to 

meet expectations when they are close to those expectations. 10 To test H1 we run the following 

logit regression: 

P(Miss_ Beat it ) � f (� 1�� 2ABACC POL it � �� 3�20Controls � � it ) (3) 

Miss_Beat and AB ACC POL were described above. We include a variety of control 

variables that have been shown in prior literature to be correlated with meeting or beating analyst 

expectations. We include controls for institutional ownership, implicit claims, value relevance of 

earnings, growth, size, and changes in earnings (Matsumoto, 2002). In addition, we include 

variables to control for previous patterns of meeting or beating expectations (Barton and Simko, 

2002), uncertainty in the forecasting environment (Matsumoto, 2002; Cheng and Warfield, 

2005), leverage (Prawitt et al., 2009), number of business segments (Prawitt et al., 2009), cash 

flow volatility (Prawitt et al., 2009), outstanding shares (Cheng and Warfield, 2005), auditor 

qualities (Frankel et al. 2002), levels of operating cash flows (Frankel et al. 2002), net operating 

assets (Cheng and Warfield, 2005), and analyst following (Cheng and Warfield, 2005). See 

Appendix A for a detailed list of control variables. 11 

10 In studies that use cents, the interval for establishing ―just beat‖ vs. ―just miss‖ has not been consistently 

measured. Athanasakou et al. (2009) use the interval -£.02≤FCSTERR


Our tests of H2 require a more complete examination of the distribution of forecast errors 

than our test of H1. First, we create quintile portfolios of firms according to AB ACC POL with 

quintile five including the firms with highest AB ACC POL. Consistent with Abarbanell and 

Lehavy (2003), for each quintile, we group forecast errors into increasingly larger and non- 

overlapping symmetric intervals moving out from zero forecast errors. For example, the forecast 

error range of [-0.01, 0) & (0, 0.01] includes all forecast errors that are greater than or equal to - 

0.01 and (strictly) less than zero and observations that are greater than zero and less than or equal 

to 0.01. We have eight total bins as follows: 

BINit = 0 if FCSTERRit = 0 

BINit = 1 if FCSTERRit = [-.01, 0) & (0, .01] 

BINit = 2 if FCSTERRit = [-.02, -.01) & (.01, .02] 





BINit = 7 if FCSTERRit = [Min, -.1) & (.1, Max] 

To determine whether the distribution of forecast errors differs across AB ACC POL quintile, we 

employ the Kolmogorov-Smirnov equality-of-distributions test (Conover, 1971; Smirnov, 1939). 

The test measures whether the distribution functions associated with two populations (as inferred 

by two samples) differ significantly. It tests for more generic differences between the two 

distribution functions, not only means or medians (Conover, 1971). Specifically, the test statistic 

is the maximum absolute difference between the two samples‘ cumulative distribution functions. 

them from this model. However, consistent with our predictions, the inclusion of industry and year effects in this 

model does not affect the results. 

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In addition to the Kolmogorov-Smirnov test for H2, we also estimate a multinomial logit 

model to validate the results in a multivariate framework. We use the same variable, BIN, as the 

multinomial dependent variable in our model as described in (4). 

P BIN ) � f ( � �� 

AB ACC POL �� 

� Controls �� 

) 

(4) 

( it 1 2 

it 3 20 

it 

Control variables in this model are the same as the ones we use to test H1. We utilize the most 

extreme forecast error bin (BIN=7) as the base outcome for the model. Therefore, a negative 

coefficient for β2 would be consistent with accounting complexity increasing the probability of 

extreme forecast errors, whereas a positive coefficient would suggest managers of complex firms 

can meet expectations consistently. 


We perform two tests to determine whether accounting complexity is associated with 

expectations management (test of H3). First, we perform a test similar to one in Bartov et al. 

(2002). We first run a logistic regression of firms that just beat and just missed expectations 

(firms between -0.02 and 0.02 forecast error) using the most recent, or last, forecast error. This 

regression is the same regression to test H1. We then re-estimate the regression, but measure the 

dependent variable using the first consensus forecast of the quarter. If the coefficient using the 

last forecast is positive and significant and the coefficient using the first forecast is negative or 

insignificant, the results would be consistent with expectations management. Any other findings 

would be inconsistent with expectations management. The controls are exactly the same as in 

(3). 

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Our second set of tests also requires estimating logistic regressions. We use two 

dependent variables that provide evidence of managing expectations. The first dependent 

variable is measured only for those firms that met or beat the analysts‘ most recent forecast. The 

dependent variable is one if the firm would have missed the forecast using the first forecast of 

the quarter and zero otherwise (MET_EXPMGMT). A dependent variable of one is consistent 

with expectations management. Our second dependent variable is measured only for those firms 

that missed the analysts‘ most recent forecast. The dependent variable is one if the firm would 

have met or beat the forecast using the first forecast of the quarter and zero otherwise 

(MISS_EXPMGMT). A dependent variable of one in this case is inconsistent with expectations 

management because the change in the forecast caused the firm to miss the forecast. Using these 

dependent variables, we estimate the following regression: 

P(EXPMGMT it )� f(� 1 �� 2 ABACCPOL it �� 3�20 Controls�� it ) (5) 

We include the same control variables as in the multinomial logit regression to control for 

�� 

incentives to manage expectations. Positive (negative) coefficients on AB ACC POL for 

MET_EXPMGMT (MISS_EXPMGMT) would be evidence consistent with H3. 

4. Sample and Results 

4.1 Sample 

Using the Python programming language, we collect all available 10-K and 10-K405 

filings from Edgar for the years 1994-2008. We then perform a search of these 10-Ks using the 

Python programming language to obtain the accounting policies section of the notes to the 

financial statements. See Peterson (2009a) for a more detailed description of this process. We 

are able to collect 60,180 annual accounting policy length observations. Since we are interested 

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in the likelihood of meeting or beating analyst quarterly expectations, we assume that the 

accounting policies reported in the 10-Ks are in effect for the entire year. In order to test our 

hypotheses, we obtain quarterly financial data from COMPUSTAT Xpressfeed quarterly files. 

Necessary annual financial data come from the COMPUSTAT Xpressfeed annual files. We 

obtained segment data from the COMPUSTAT legacy files. Institutional ownership data was 

obtained from Thompson Reuters. Analyst forecast data was obtained from IBES. See Table 1 

Panel A for a description of the sample. 

Panel B of Table 1 gives the frequency of the sample by year. The sample has relatively 

even coverage across the years except for 1994 and 1995 because coverage of 10-Ks on the 

Edgar database is sparse in those years. Panel C lists the sample by industry relative to a 

benchmark of firms that have data on Compustat, CRSP, and IBES. Industry classification is the 

30 industry classification from Fama and French (1997). The sample is slightly over-weighted in 

Healthcare, Personal and Business Services, and Business Equipment and underweighted in the 

Banking, Insurance, Real Estate, Trading industry relative to the benchmark sample; otherwise, 

the samples appear comparable to firms in the intersection of Compustat/CRSP/IBES. 

4.2 Descriptive Statistics 

Table 2 presents univariate statistics for all variables included in equation (3). Note that 

the distribution of Miss_Beat in our sample is consistent with patterns described in Burgstahler 

and Dichev (1997) since there is a disproportionate number of observations ―just beating‖ over 

―just missing‖. Also, as expected, the mean of AB ACC POL is zero because it is the residual of 

a regression that includes an intercept. Also, the mean of FCSTERR is 0.02, which is consistent 

with the average firm beating expectations. We also note the mean number of consecutive 

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quarters the firm has beat expectations is 3.84 and the average number of analyst estimates is 

7.19, suggesting that many of the firms in our sample have relatively high analyst coverage. 

4.3 Results of Tests of H1 

Table 3 presents the results of the logit regression model presented in equation (3). 

Supporting H1, the coefficient on AB ACC POL is positive (0.1072) and significant (p- 

value=0.004). This result is consistent with firms with greater accounting complexity having a 

higher propensity to ―just beat‖ (Miss_Beat=1) than to ―just miss‖ (Miss_Beat=0) analyst 

expectations. 

Table 3 also presents the coefficients for the control variables included in the regression. 

Most control variables included are statistically significant, which underscores their importance 

in explaining the other incentives and characteristics that cause or allow firms to meet or beat 

analysts‘ expectations. Consistent with prior research we find positive coefficients on STREAK, 

INSTPERC, LOGMVE, CHGEARN, LEVERAGE, and NUMEST indicating their positive 

influence on just meeting or beating expectations over just missing expectations. 

We also examine marginal effects to address the economic importance of the results. 

From the results in Table 3, we calculate the marginal effect of AB ACC POL as the change in 

the predicted probability as AB ACC POL moves one standard deviation centered at the mean 

holding all other variables constant at their mean values. We find that for a one standard 

deviation change in AB ACC POL (at the mean), there is a 0.71% increase (p-value=.004) in the 

likelihood of ―just meeting or beating‖ analysts‘ earnings forecasts versus ―just missing‖. While 

this marginal effect is not particularly large, we believe this highlights the fact that accounting 

complexity does play an economically meaningful role in the likelihood of a firm to meet or beat 

analysts‘ earnings forecasts. Compared to other variables in the model, AB ACC POL has a 

18

marginal effect similar to that of BTM, TENURE, and INSTPERC. We note that STREAK has 

the highest absolute marginal effect (at the mean) of 5.49%, followed by LOGSHARES with - 

2.90%. 


Table 4 presents the univariate results to test H2 through an analysis of the distribution of 

forecast errors by levels of complexity. Panel A presents the distribution of FCSTERR across 

quintiles of accounting complexity. Note that the first two one-cent ranges (BINs 1 and 2) are 

consistent with the findings in Table 3 that support H1. The ratio of positive to negative 

FCSTERR is increasing (non-monotonically) in complexity quintiles. For BIN 1, we see that in 

the lowest complexity quintile (Q1) the ratio is 1.893, whereas at the highest complexity quintile 

(Q5) the ratio is 2.141. The increase in this ratio is consistent with H1 since it shows that as 

accounting complexity increases, there is a higher frequency of positive FCSTERR relative to 

negative FCSTERR. BIN 2 is consistent with this as well with the ratio moving from 2.002 to 

2.239 from Q1 to Q5. 

Inconsistent with smoothing behavior, Table 4 panel A shows that in the most extreme 

FCSTERR bin (BIN 7) there is a higher frequency of observations (3,034) for firms with the 

highest accounting complexity (Q5) over the firms with the least complex accounting (2,478). 

Table 4 panel B confirms that the distribution of FCSTERR in Q5 is statistically different from 

all other quintiles separately and the quintiles combined using the Kolmogorov-Smirnov tests (all 

p-values=0.000). However, comparing other adjacent quintiles (e.g. Q2 and Q3) does not reject 

the Kolmogorov-Smirnov tests suggesting the distribution of forecast errors is similar for these 

accounting complexity quintiles. Together, these results suggest that firms with more complex 

accounting have more extreme forecast errors vs. firms with less complex accounting, 

19

inconsistent with smoothing behavior, but consistent with the difficulty analysts have in 

forecasting firms with complex accounting. Even though there are incentives to engage in 

smoothing activity, managers may not be able to overcome the heterogeneity in analysts beliefs 

as documented in prior studies to be able to effectively implement such a strategy outside of a 

close range. 

Table 5 presents the multivariate test results of H2. Based on our conditional hypothesis, 

negative coefficients would be inconsistent with smoothing behavior, but consistent with a 

higher propensity to have more extreme forecast errors. Consistent with the results in Table 4, 

Table 5 shows the coefficients on AB ACC POL are all negative. The interpretation of this 

result is that firms with complex accounting are less likely to have smaller forecast errors over 

larger forecast errors. As the BIN number increases (i.e. forecast error becomes more extreme) 

the statistical significance goes away. We also note the coefficient for BIN 1 is less negative 

than the surrounding BINs, which is still somewhat consistent with the results of H1. Overall, 

these results are not consistent with firms with more complex accounting being able to beat 

expectations more generally, but instead are consistent with firms with complex accounting 

having more extreme forecast errors. However, firms with more complex accounting are more 

likely to meet analysts‘ expectations than miss expectations if they are close to analyst 

expectations. 


Table 6 presents the results of logit regressions used to test for the presence of 

expectations management. The results in specification (1) of Table 6 are identical to those in 

Table 3, and are presented to compare with the results in specification (2) using the first forecast 

20

of the quarter. As stated above, the positive coefficient (0.1072) on AB ACC POL (p- 

value=0.004) shows that more complex firms have a higher propensity to ―just beat‖ over ―just 

miss‖ when using the last forecast of the quarter. Model (2) presents the exact same logit 

regression model, except we replace the most recent forecast with the beginning of the quarter 

consensus forecast to calculate FCSTERR. Consistent with H3, we find that the coefficient on 

AB ACC POL in specification (2) is not different from zero (p-value=0.715). The inconsistent 

results across models (1) and (2) for AB ACC POL is consistent with changes in analysts‘ 

earnings expectations over the quarter being at least partially responsible for the increase in the 

likelihood of meeting or beating expectations for complex firms. In contrast to the coefficients 

on AB ACC POL, we note that the control variables are generally consistent across models (1) 

and (2), suggesting the effect of these factors on meeting or beating does not depend on 

managing expectations. 12 

Table 6 also includes additional tests of expectations management. Model (3) in Table 6 

presents the results of a logit model for firms who met expectations using the last forecast of the 

period, but missed expectations using the first forecast. Of these firms, we are interested in 

determining whether AB ACC POL increases its propensity to meet or beat when it would not 

have met expectations using the first consensus forecast of the quarter. Consistent with H3 (and 

the results from Models (1) and (2)) we find a positive coefficient (0.0725) on AB ACC POL (p- 

value=0.027) showing that firms with more complex accounting appear to engage in 

expectations management to meet or beat analyst‘s earnings expectations. 

12 For example, the coefficients on STREAK are positive and significant in both specifications (0.0633 and 

0.0521). This suggests that firms with longer streaks of beating analysts‘ forecasts are more likely to beat the 

current forecast regardless of whether the first or last forecast of the quarter is used. 

21

Finally, Model (4) in Table 6 presents results of a logit model for firms who did not meet 

expectations using the last forecast of the quarter, but would have beat expectations using the 

first forecast. Of these firms, we are interested in determining whether AB ACC POL decreases 

its propensity to miss when it would have met or beat expectations using the first consensus 

forecast of the quarter. Since we would not expect a firm engaging in expectations management 

to manage forecasts away from a forecast that is beatable, a negative coefficient would be 

consistent with ―normal‖ expectations management. While we do find the anticipated sign on 

the coefficient (-0.0220) on AB ACC POL, the p-value of 0.778 makes it statistically 

indistinguishable from zero. 

Overall, we find evidence consistent with H3 that firms with more complex accounting 

are more likely to have forecast paths that are consistent with expectations management. This is 

consistent with the idea that expectations management is at least partially responsible for the 

ability of firms with more complex accounting to meet or beat analysts‘ expectations. However, 

this expectations management appears to be one sided; complex firms only appear to be able to 

walk down earnings, but not prevent the walk up of earnings. 

5. Conclusion 

We examine the effects of complex accounting on the propensity of firms to meet or beat 

analysts‘ earnings expectations. We find that firms with more complex accounting have a higher 

propensity to just meet or beat analysts‘ expectations than to just miss analysts‘ expectations. 

However, our results also reveal that firms with more complex accounting are also more likely to 

have larger forecast errors, suggesting they are less likely to be engaged in smoothing activity 

more generally. Taken together, these results suggest complex accounting is exploited by 

managers to meet or beat analysts‘ expectations when expectations are close to actual earnings. 

22

However, due to the presence of extreme forecast error in firms with more complex accounting, 

these firms do not appear to be able to utilize this complexity to smooth forecast errors. 

Finally, we recognize that managers may be engaged in some form of expectations 

management as well as earnings management. Therefore, we explore whether firms with more 

complex accounting are able to use expectations management to increase their propensity to 

meet or beat analysts‘ expectations. We find evidence consistent with expectations management 

being at least partially responsible for the results. 

Overall, our study provides evidence that accounting complexity plays a statistically and 

economically significant role in the likelihood of a firm to meet or beat analysts‘ forecasts. Our 

study is the first to provide large sample evidence of this effect. In addition, we provide some 

empirical evidence supporting investor skepticism of earnings of complex firms. However, we 

encourage more work to be done in this area to more fully understand the effects of accounting 

complexity on managers‘ financial decisions and on users of the financial statements. 

23

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27

Appendix A - Description of Key Control Variables 

This appendix reports the control variables used in our regression analysis. See Table 2 for descriptive statistics on these 

variables. See Tables 3 and 5 for the results from tests using these variables as controls. Winsorized means the variable 

was winsorized at 1% and 99% by year to control for outliers. Unless otherwise noted, variable names identified above in 

the calculation column correspond to the quarterly COMPUSTAT Xpressfeed dataset. 

Variable Name Description Calculation 

STREAK The ability of the firm to meet or beat 

analyst expectations repeatedly. 

28 

The number of consecutive prior quarters the firm as met 

or beat analyst expectations. 

INSTPERC Institutional ownership. Sum(shares)/(mean(shrout1)*1,000,000). Where shares 

are institutional shares held at the end of the quarter, 

shrout1 are total shares outstanding in Millions as of the 

filing date, both taken from the tfn.s34 dataset. 

RD Research and development expense. xrdq/atq 

LABOR A proxy for implicit claims. (1-ppegtq)/(atq+dpactq) 

NEGEARN A proxy for the value relevance of 

earnings. 

The number of prior 4 quarters with negative earnings 

(ibq). 

BTM Growth. ceqq/(prccq*cshoq). Winsorized. 

LOGMVE Size. prccq*cshoq. Winsorized. 

CHGEARN Seasonal change in earnings. Earnings (ibq) less earnings from the same quarter of the 

prior year, all scaled by same quarter prior year earnings. 

Winsorized. 

CV The coefficient of variation of 

FCSTERR. 

The standard deviation of the consensus forecast used to 

calculate FCSTERR scaled by the mean. Winsorized. 

LEVERAGE Leverage. (dlttq+lctq)/atq. Winsorized. 

LOG_OP_SEG Number of Operating Segments. The natural logarithm of the number of operating 

segments for the firm obtained from the COMPUSTAT 

Legacy dataset. 

CFVOL Cash flow volatility. The standard deviation of the companies' cash flow from 

operations (oancfy) for the previous 5 years. Winsorized. 

LOGSHARES Natural logarithm of shares outstanding. ln(cshoq) 

TENURE Audit Tenure. The total number of years the firm has been audited by 

the current audit firm from COMPUSTAT. 

SCFO Scaled operating cash flow. Operating cash flow (oancfy) scaled by total assets (atq). 

Winsorized. 

NOA Net operating assets. Scaled beginning of the period net operating assets 

[(ceqq-cheq+dlcq+dlttq)/ saleq]. Winsorized. 

NUMEST Analyst following. The number of analysts following the firm making up the 

consensus forecast used to calculate FCSTERR. 

BIGN A proxy for audit quality. An indicator variable equal to 1 if a firm is audited by a 

"Big 5" audit firm.

TABLE 1 

Sample Selection 

This table presents descriptive statistics of our sample. Panel A details our sample selection process. Panel B 

reports the sample by fiscal year. Panel C reports the sample by Fama French (1997) designated industries using 

SIC available from COMPUSTAT Xpressfeed quarterly files. 

Panel A: Sample Size 

COMPUSTAT Quarterly Xpressfeed observations (1994-2008) 

Missing IBES Data (175,933) 

Missing CRSP adjustment factors (7,447) 

Missing 10-K filing from Edgar (39,621) 

Missing Accounting policy Length (45,823) 

Missing data necessary to calculate other variables (49,642) 

Final sample 83,837 

Panel B: Sample by Year 

Fiscal Year Frequency Percent 

1994 1,146 1.37 

1995 2,624 3.13 

1996 5,098 6.08 

1997 5,953 7.10 

1998 6,395 7.63 

1999 4,989 5.95 

2000 4,955 5.91 

2001 5,289 6.31 

2002 5,631 6.72 

2003 5,830 6.95 

2004 6,507 7.76 

2005 6,935 8.27 

2006 7,870 9.39 

2007 8,652 10.32 

2008 5,963 7.11 

Total Sample 83,837 

100.00 

29 

402,303

Panel C: Sample by Industry 

Benchmark Sample 

Frequency Percent Frequency Percent 

Food Products 3,763 1.72 1,523 1.82 

Beer & Liquor 663 0.30 213 0.25 

Tobacco Products 264 0.12 49 0.06 

Recreation 4,063 1.86 1,725 2.06 

Printing and Publishing 2,340 1.07 945 1.13 

Consumer Goods 3,063 1.40 1,151 1.37 

Apparel 2,664 1.22 1,295 1.54 

Healthcare, etc. 22,877 10.45 9,867 11.77 

Chemicals 3,636 1.66 1,589 1.90 

Textiles 1,054 0.48 398 0.47 

Construction and Construction Materials 5,562 2.54 2,288 2.73 

Steel Works Etc 3,050 1.39 1,166 1.39 

Fabricated Products and Machinery 7,395 3.38 2,940 3.51 

Electrical Equipment 2,620 1.20 1,046 1.25 

Automobiles and Trucks 2,816 1.29 972 1.16 

Aircraft, ships, and railroad equipment 1,186 0.54 556 0.66 

Precious Metals, etc. 1,215 0.55 292 0.35 

Coal 327 0.15 143 0.17 

Petroleum and Natural Gas 7,695 3.51 3,875 4.62 

Utilities 6,479 2.96 1,482 1.77 

Communication 5,932 2.71 2,093 2.50 

Personal and Business Services 28,386 12.97 11,918 14.22 

Business Equipment 27,813 12.70 12,369 14.75 

Business Supplies and Shipping Containers 3,416 1.56 1,225 1.46 

Transportation 6,040 2.76 2,612 3.12 

Wholesale 6,719 3.07 2,844 3.39 

Retail 11,813 5.40 5,209 6.21 

Restaurants, Hotels, Motels 3,566 1.63 1,580 1.88 

Banking, Insurance, Real Estate, Trading 38,888 17.76 9,056 10.80 

Everything Else 3,618 1.65 1,416 1.69 

Total Sample 218,923 100.00 83,837 100.00 

30

TABLE 2 

Univariate Statistics 

This table reports univariate statistics for key variables used in our analysis. Statistics presented include the number 

of observations (N), mean, standard deviation, and key points in the distribution. Forecast error (FCSTERR) is 

defined as (Adjusted Actual IBES EPS - Mean Consensus EPS estimate). Miss_Beat=0 if -.02≤FCSTERR

TABLE 3 

Test of Meeting or Beating Expectations 

This table presents coefficient estimates from a Logit regressions of Miss_Beat on AB ACC POL. Miss_Beat is 

defined as 0 if -.02≤FCSTERR

TABLE 4 

Analysis of Distribution of Forecast Errors by Accounting Complexity 

This table reports tests of differences in distributions of FCSTERR across quintiles of complexity. Panel A reports the distribution of FCSTERR over 

quintiles of AB ACC POL (labeled as Complexity Quintiles). The Pos/Neg ratio presented is the ratio of positive/negative forecast errors (FCSTERR). 

Quintile 1 represents the lowest levels of AB ACC POL and Quintile 5 represents the highest levels of AB ACC POL. Panel B reports the results of 

nonparametric tests of equality-of-distributions of FCSTERR across quintiles of complexity (Kolmogorov-Smirnov tests). A small p-value (high D- 

Statistic) for the test rejects the null hypothesis that the two samples come from the same distribution. 

Panel A: Distribution of FCSTERR across Quintiles of Accounting Complexity 

Complexity Q1 Complexity Q2 Complexity Q3 Complexity Q4 Complexity Q5 

BIN FCSTERR N Pos/Neg N Pos/Neg N Pos/Neg N Pos/Neg N Pos/Neg Total 

0 FCSTERR=0 2,634 

2,452 

2,540 

2,362 

2,294 

12,282 

1 [-.01, 0) & (0, .01] 975 1.893 1,124 2.005 1,173 1.903 1,094 1.949 1,027 2.141 5,393 

2 [-.02, -.01) & (.01, 0.02] 3,476 2.002 3,461 2.093 3,340 2.163 3,306 2.146 3,058 2.239 16,641 

3 [-.03, -.02) & (.02, .03] 2,399 2.238 2,370 2.269 2,386 2.255 2,477 2.384 2,231 2.257 11,863 

4 [-.04, -.03) & (.03, .04] 1,213 2.175 1,270 2.191 1,167 2.613 1,213 2.175 1,206 2.579 6,069 

5 [-.05, -.04) & (.04, .05] 1,160 1.893 1,214 1.990 1,206 2.141 1,194 2.038 1,223 2.128 5,997 

6 [-.1, -.05) & (.05, .1] 2,434 1.753 2,460 1.805 2,517 1.806 2,634 1.721 2,692 1.727 12,737 

7 [Min, -.1) & (.1, Max] 2,478 1.214 2,417 1.048 2,437 1.051 2,489 1.017 3,034 1.004 12,855 

33 

Panel B: Results of nonparametric Kolmogorov-Smirnov equality-of-distributions of FCSTERR tests 

Comparison D-Statistic P-value 

Complexity Q1 vs. Complexity Q5 0.031 0.000 




Complexity Q1-4 vs. Complexity Q5 0.027 0.000 






Complexity Q1 vs. Complexity Q2 0.009 0.571

TABLE 5 

Multinomial Logit Regression Model Estimates 

This table reports coefficient estimates from the multinomial logit model presented in equation (4): 

P(BINit) = f(β1 + β2AB ACC POLit + ∑β3-20Controls + εit) 

BIN is defined as presented in Table 4, with BIN ranging from 0 to 7. Abnormal Accounting Policy Length (AB 

ACC POL) is defined as the residuals from equation (1). Note that category seven (BIN=7) is used as the base 

outcome in the model presented. The interpretation of the coefficients is the propensity to be in the category 

presented vs. the base outcome (BIN=7 in this case). Note that per the properties of the multinomial logit, 

inferences are not affected by choice of base outcome. Standard errors are heteroskedasticity-robust, clustered by 

firm to adjust for firm-level correlation over time. See Appendix A for a detailed listing of included control 

variables. *Denotes significance at the 10% level; **Denotes significance at the 5% level; ***Denotes significance 

at the 1% level. 

Variable 

AB ACC POL -0.1833 *** -0.1097 * -0.1621 *** -0.1144 ** -0.0687 -0.0811 -0.0096 

STREAK 0.0610 *** 0.0546 *** 0.0528 *** 0.0476 *** 0.0469 *** 0.0371 *** 0.0315 *** 

INSTPERC 0.2254 ** 0.3131 *** 0.2699 *** 0.2771 *** 0.1653 0.2498 ** 0.1049 

RD -4.6173 *** -4.5912 *** -1.4016 ** -1.3183 * 0.5073 0.0109 0.6786 

LABOR 0.5369 *** 0.6784 *** 0.4661 *** 0.3279 *** 0.1598 0.1896 ** -0.1631 ** 

NEGEARN -0.4737 *** -0.3487 *** -0.3928 *** -0.3032 *** -0.2508 *** -0.2175 *** -0.1541 *** 

BTM -1.9983 *** -2.1209 *** -1.7496 *** -1.5772 *** -1.1607 *** -1.0745 *** -0.7625 *** 

LOGMVE -1.4157 *** -1.7331 *** -1.1388 *** -1.0498 *** -0.7008 *** -0.7950 *** -0.5108 *** 

CHGEARN 0.0216 *** 0.0194 *** 0.0222 *** 0.0269 *** 0.0180 *** 0.0248 *** 0.0201 *** 

CV -0.0719 0.3116 *** -0.0746 -0.0433 -0.0541 0.0008 0.0838 * 

LEVERAGE -1.6930 *** -2.1199 *** -1.4909 *** -1.3892 *** -1.1910 *** -1.1296 *** -0.9078 *** 

LOG_OP_SEG -0.0997 *** -0.1003 ** -0.1311 *** -0.0938 *** -0.0648 * -0.0431 -0.0393 

CFVOL -4.6208 *** -4.0738 *** -3.7231 *** -2.9343 *** -1.7628 *** -1.8613 *** -1.1420 *** 

LOGSHARES 1.5144 *** 1.7908 *** 1.1959 *** 1.1230 *** 0.7729 *** 0.8642 *** 0.5437 *** 

TENURE -0.0580 *** -0.0463 *** -0.0513 *** -0.0352 *** -0.0258 *** -0.0250 *** -0.0112 ** 

SCFO 0.9027 *** 1.9451 *** 1.2744 *** 1.2322 *** 1.3210 *** 1.0582 *** 0.5092 ** 

NOA -0.0025 -0.0046 -0.0047 -0.0047 -0.0037 -0.0055 * -0.0021 

NUMEST 0.0370 *** 0.0282 *** 0.0294 *** 0.0160 ** 0.0175 *** 0.0116 * 0.0044 

BIGN 0.3749 *** 0.2705 *** 0.3071 *** 0.1828 ** 0.1378 * 0.1582 * 0.0501 

CONSTANT 5.1836 *** 5.3741 *** 4.6690 *** 4.0136 *** 2.0659 *** 2.2513 *** 2.3657 *** 

N 83,837 

Chi-squared 3,684.0 

BIN=0 BIN=1 BIN=2 

34 

BIN=3 BIN=4 BIN=5 BIN=6

TABLE 6 

Logit Regression Model Estimates to Test Expectations Management 

This table presents coefficient estimates from Logit regressions of various dependent variables on AB ACC POL. 

Miss_Beat is defined as 0 if -.02≤FCSTERR

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