Understanding the Event Study - Journal of Business Administration

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Understanding the Event Study 

ารศึกษาเหตุการณ์ (Event Study) เป็นวิธีวิจัย 

ที่ใช้อย่างกว้างขวางสำหรับงานวิจัยทางด้าน 

บริหารธุรกิจ การดำเนินงานวิจัยด้วยการศึกษา 

เหตุการณ์มีส่วนเกี่ยวข้องถึงการระบุเหตุการณ์ 

ที่สนใจ การประมาณการณ์ผลตอบแทนส่วนเกิน และการ 

ทดสอบความมีนัยสำคัญของเหตุการณ์ บทความนี้จะบรรยาย 

ถึงการใช้วิธีวิจัยการศึกษาเหตุการณ์ ขั้นตอนการทำวิจัยด้วย 

วิธีการศึกษาเหตุการณ์ และความแตกต่างของการใช้วิธีวิจัย 

ดังกล่าว 

Dr.Thitima Sitthipongpanich 

Assistant Professor of Department of Finance, 

Faculty of Business Administration, 

Dhurakij Pundit University 

 

ABSTRACT 

vent studies are widely used in business 

research. Conducting an event study 

involves identifying an event of interest, 

estimating abnormal stock returns relating 

to the event and then testing the significance of the 

event. This paper describes the application of event 

study methodology, and reviews the practice and 

variations of the method. 

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คณะพาณิชยศาสตร์และการบัญชี มหาวิทยาลัยธรรมศาสตร์ 

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INTRODUCTION 

An event study is an empirical analysis that 

is normally used to measure the effect of an event on 

stock prices (returns). Although the majority of 

previous literature investigates stock prices, several 

studies examine stock trading volume, or return 

volatility. The event study is of importance because it 

can be used to evaluate the impact of company 

policies on firm value. The empirically conducted 

study is based on the following assumptions. 

• Under the market efficiency hypothesis, the 

impact of an event will be instantly reflected in stock 

prices. Therefore, the market reaction to the event 

can be measured by stock returns over the study 

time period. 

• The event is unforeseen. Abnormal 

(excess) stock returns indicate the market reaction to 

the unanticipated event. 

• During the event window, there are no 

confounding effects, meaning that the effect of other 

events is isolated. 

 

This paper discusses the purposes of an 

event study and provides examples of previous event 

studies. The discussion includes an explanation of 

the applications and procedures in conducting such a 

study. The last part of the paper describes the 

limitations of the event study method. 

 

 

PURPOSES AND EXAMPLES OF 

EVENTS 

An event study is commonly developed to 

attempt to measure whether an unanticipated event 

could have an effect on stock prices and the 

direction and magnitude any perceived effects might 

have on those stock prices. The event study method 

is applied in different research areas, such as 

accounting and finance, management, marketing, 

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information technology, law and economics. Early 

literature on event studies are include an investigation 

of the impact of annual earnings announcement on 

stock prices (Ball and Brown, 1968) and a study of 

the announcement of stock splits on stock returns 

(Fama et al., 1969). 

 

Ball and Brown (1968) concluded that annual 

accounting income data contains information that is 

related to stock prices. They found that income 

forecast errors, which are measured by the difference 

between announced and expected accounting 

earnings, have a positive impact on the abnormal 

performance index around the annual report 

announcement date. 

 

Fama et al (1969) also note that stock prices 

appear to adjust to new information. Stock splits 

generally occur following periods when stock prices 

significantly increase relative to the market. They 

found that, after a split announcement, stock prices 

seem to quickly reflect all available information and 

do not generate any abnormal returns. The results 

demonstrate the efficiency of the capital market. 

 

In this paper, examples of events are 

provided, including firm-specific events and economywide 

events. The firm-specific events mainly involve a 

change in or a new implementation of company 

policy. Examples of firm-specific events used in 

previous event studies are as follows. 

• The announcement and completion of a 

takeover bid/divestiture (Agrawal and Mandelker, 

1990; Lys and Vincent, 1995; Gregory, 1997; Bruner, 

1999) 

• Initial public offering (Ritter, 1991; Loughran 

and Ritter, 1995; Espenlaub et al., 2001) 

• Seasoned equity offering (Loughran and 

Ritter, 1995; Carlson et al., 2006) 

• Earning management practices before IPO 

and SEO (Teoh et al., 1998a; Teoh et al., 1998b) 

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• Obtaining new bank loans or loan renewal 

(Lummer and McConnell, 1989) 

• Selecting an auditor and its reputation 

(Weber et al., 2008) 

• An appointment of a new CEO with 

financial expertise (Defond et al., 2005) 

• Top executive changes (Bonnier and 

Bruner, 1989; Dahya et al., 1998) 

• Sudden CEO vacancy (Lambertides, 2009) 

• Compensation plan policy (DeFusco et al., 

1990; Yermack, 1997; Yeo et al., 1999) 

• An internet name change (Mase, 2009) 

• An advertising campaign (Kim and Morris, 

2003; Choong et al., 2007) 

• Football results of listed European clubs 

(Benkraiem et al., 2009) 

• IT investments (Roztocki and Weistroffer, 

2009) 

• Enterprise resource planning system (ERP) 

implementation (Benco and Prather, 2008) 

 

Economy-wide events are often used in large 

sample event studies, which examine the effect of a 

particular event on relevant firms. The following 

studies are examples of economy-wide events. 

• A new item of legislature (Schipper and 

Thompson, 1983; Schumann, 1988) 

• Sarbanes Oxley Act (Small et al., 2007) 

• A financial crisis (Baek et al., 2004) 

• The “9/11” terrorist attack in New York 

(Homan, 2006) 

 

 

RESEARCH DESIGN AND 

PROCEDURES OF AN EVENT 

STUDY 

Event studies can be designed in different 

ways to reflect specific purposes and research 

questions. The event study can be used in both 


clinical studies and large sample studies. A clinical 

study investigates the effect of an event on stock 

prices of a single company, such as an analysis of 

the market reaction to a takeover announcement; 

where the study focuses on the acquiring firm and/or 

on the target firm (Lys and Vincent, 1995; Bruner, 

1999). Large sample studies examine the impact of 

an event on prices of different sample stocks. 

Examples of this type of event study include the 

effect of company policy implementation in a large 

sample of listed firms, such as a stock split, a top 

management compensation policy, or a director 

appointment policy (Fama et al., 1969; Yermack, 

1997; Defond et al., 2005). 

 

In addition to the effect of an event on stock 

returns, researchers have examined the impact of an 

event on stock volatility (DeFusco et al., 1990; Engle 

and Ng, 1993; Jayaraman and Shastri, 1993), on 

stock trading volume (Benkraiem et al., 2009; 

Karafiath, 2009), or on accounting performance 

(Barber and Lyon, 1996). Furthermore, not only 

company stocks, but also other types of securities 

can be considered in the event study. Prior research 

using event studies has analyzed effects on company 

bonds, where abnormal bond returns can 

demonstrate the market impact of event 

announcements (Gugler et al., 2004; Asquith and 

Wizman, 1990; DeFusco et al., 1990; Steiner and 

Heinke, 2001). 

 

When designing an event study, the length of 

the event periods and the expected availability of 

data will be considered in determining the return 

frequency. Choices of return frequency can be daily, 

weekly, monthly, or annually. Daily and monthly 

returns are commonly found in previous literature. 

Daily data is often used for short-term event studies 

(Lummer and McConnell, 1989; Small et al., 2007), 

whilst monthly data is normally chosen for long-term 

studies (Ritter, 1991; Teoh et al., 1998b). 



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The expected return, , is used as the 

benchmark return in the normal situation to compare 

with the actual return during the event window(s). 

The benchmark return represents the return that is 

not related to the event of interest. There are choices 

of model to estimate expected returns. 

 

3.1) Mean-adjusted return 

 

The mean return is the average return over 

the estimation period. Each stock can use the 

average return during the estimation period as its 

own expected return (Brown and Warner, 1985; 

Lambertides, 2009). 

 

3.2) Market-adjusted return 

 

The expected return is the market return 

at the same period of time, assuming that all 

stocks, on average, generate the same rate of return 

(Ritter, 1991; Bruner, 1999; Teoh et al., 1998b; Weber 

et al., 2008). Using the market-adjusted return method 

does not require an estimation period. 

 

3.3) Market-model-adjusted return 

 

The expected return is computed based on a 

single factor market model. The parameters of the 

market model, i.e. and , are estimated using 

Ordinary Least Square (OLS) regression over the 

estimation period. This method is used to control the 

relation between stock returns and market returns, or 

allows for the variation in risk associated with a 

selected stock. The market-model-adjusted return is 

commonly found as an expected return in previous 

event studies (Bonnier and Bruner, 1989; Lummer and 

McConnell, 1989; Schipper and Thompson, 1983; 

Homan, 2006; Small et al., 2007). 


3.4) CAPM-adjusted return 

 

Using the Capital Asset Pricing Model 

(CAPM), the expected return is the outcome of the 

risk-free rate return plus market risk premium 

(Espenlaub et al., 2001). of the model measures 

the risk of stock (i), assuming that an investor 

requires higher return to compensate for higher risk. 

 

3.5) Reference portfolios 

The expected return in this method is the 

return of the reference portfolio. The portfolio 

comprises stocks based on the criteria of size, the 

book-to-market ratio, or both size and the book-tomarket 

ratio. To calculate this benchmark expected 

return, the estimation period is not required. 

Researchers generally rank all firms into different 

groups by a particular characteristic; for example, into 

ten size-different groups. Then an average return for 

all stocks in each group is calculated as the 

expected return of a reference portfolio. Examples of 

event studies using this method are Ritter (1991) and 

Barber & Lyon (1997) 1 . 

 

3.6) Matched firm approach 

Similar to the reference portfolio method, the 

expected return is assumed to be the same as the 

return of matched stocks during the test period. The 

matching process will be done on the basis of 

relevant risk characteristics and the matched stocks 

not being exposed to the event of interest. The 

common matching characteristics are size (i.e. equity 

market value), the book-to-market ratio, and the 

combination of both. For example, using size 

characteristics, the abnormal return of an event stock 

is the difference between its actual return and the 

portfolio return of matched stocks in the same range 

of equity market value (Ritter, 1991; Loughran and 


1 

Reference portfolio: by size (Ritter, 1991; Barber & Lyon, 1997), by book-to-market ratio (Barber & Lyon, 1997), by size and bookto-market 

ratio (Barber & Lyon, 1997). 


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Ritter, 1995; Barber and Lyon, 1997). Several authors 

have reported the use of combined size and book-tomarket 

matching (Barber and Lyon, 1997; Teoh et al., 

1998a; Hertzel et al., 2002; Carlson et al., 2006). 

 

3.7) Fama-French three factor model 

 

This method is an extension of the CAPMadjusted 

return, combining more risk factors, i.e. a 

market excess return factor, a factor for 

size (small minus big, SMB) and a factor for book-tomarket-equity 

ratio (high minus low, HML) (Fama and 

French, 1993). SMB is the difference between 

average returns of small stock portfolios and those of 

big stock portfolios. HML is the difference in average 

returns between high and low book-to-market stock 

portfolios. This method uses monthly returns over a 

long period of time. It can be implemented as in the 

CAPM and is widely used in previous work (Barber 

and Lyon, 1997; Loughran and Ritter, 1995; Teoh et 

al., 1998b; Hertzel et al., 2002; Dutta and Jog, 2009). 

 

Step 4. Computing abnormal (or excess) 

returns. 

An abnormal return for an individual stock is 

the difference between the actual return on time (t) in 

the event window and the expected return of an 

individual stock. 

 

 

To calculate the cumulative abnormal return 

(CAR) for an individual stock, the abnormal return of 

each stock is aggregated over the event window 

(-T 2 

to T 3 

). 

 

 

 

The buy-and-hold abnormal return (BHAR) 

for an individual stock is the difference between the 

buy-and-hold return of a sample firm and that of the 

benchmark expected return, assuming that an 

investor buys a stock and holds it until the end of the 

event period (Ritter, 1991;Teoh et al., 1998a; Hertzel 

et al., 2002). 

 

 

 

 

The average abnormal return for all sample 

stocks on time (t) can be calculated as follows. 

 

 

 

Step 5. Testing the significance of abnormal 

(excess) returns. 

To test the significance of abnormal returns, 

most event studies use a parametric test of t- 

statistics (e.g. Brown and Warner, 1985; Barber and 

Lyon, 1997); however, non-parametric tests, such as a 

sign test, or a rank test (e.g. Benco and Prather, 

2008; Benkraiem et al., 2009), can be applied to 

confirm the results 2 . 

 

For an individual firm (i), whether or not the 

abnormal return is different from zero can be tested 

by t-statistics as follows. 

 

 

Across firms, the following formulae give 

parametric test statistics, which are used to 

investigate if the average cumulative, or buy-and-hold 

abnormal returns are equal to zero. 

 


2 

Parametric tests have some specific assumptions, such as normal distribution of returns; while non-parametric tests allow for any 

distribution of data. 

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LIMITATIONS OF THE EVENT 

STUDY 

The event study is a simple research method 

which allows uncomplicated interpretation of results. 

The method has been commonly used by researchers 

to examine how events can affect both gains and 

losses in company value as perceived by the market. 

It is also used to measure the market-based 

performance in terms of abnormal stock returns in 

situations where researchers anticipate that 

inaccuracies in financial statements may give an 

inaccurate measure of company performance. 

Although the event study methodology is useful in 

several ways, there are some major limitations. 

 

First of all, the assumptions used in event 

study methodology are not valid in some 

circumstances. Due to market inefficiency observed 

stock prices may not fully and immediately reflect all 

information. Furthermore, events might be anticipated 

in some situations, whilst unforeseen coexisting 

events could also have an effect on the sample 

stocks, which could lead to biased stock returns. 

Therefore, abnormal returns are not entirely the result 

of market reaction to the specific event of interest. 

 

Secondly, variations in estimation and test 

periods are commonly found in event studies. Precise 

estimation periods are not easy to determine. The 

length of the estimation period is subject to a 

tradeoff between improved estimation accuracy and 

potential parameter shifts. Moreover, the estimation 

period is difficult to control for other confounding 

effects if researchers select long test periods, or long 

event windows. 


Thirdly, the choice of model to estimate 

expected returns will have a bearing on the results in 

the magnitude and the significance of abnormal 

returns. For example, using the average return 

method is simple, but it produces upwardly, or 

downwardly biased abnormal returns in bull and bear 

markets, respectively. Ritter (1991) also documents 

that using different market indices to calculate 

market-adjusted returns can show differences in longterm 

performance results. More importantly, if the 

expected return is incorrectly estimated, other factors 

that are not properly controlled could lead to biased 

information in the event study results. 

 

Fourthly, not all stocks trade every day. Thin 

trading over the estimation and test period is a 

problem in event studies. For example, stock and 

market returns might not be available on the selected 

days throughout the estimation period if researchers 

apply the market model or Fama-French three factor 

model. 

 

Fifthly, calendar time clustering of events is a 

problem of cross-sectional dependence if test 

periods, or event dates of sample stocks are 

clustered in the same calendar time period (Brown 

and Warner, 1980). When the test periods of those 

stocks overlap in calendar time, the problem of 

cross-correlation in abnormal returns could exist. 

However, in traditional large sample studies, the event 

of interest is assumed to be isolated from other 

effects. Calendar time is not expected to be 

problematic because the effects of other events are 

supposed to be cancelled out across the large 

sample of firms. 

 



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