Event study method

Dr. Nguyen Thu Hien

This review is indebted to

• MacKinlay, A. Craig, 1997, “Event studies in Economics and Finance”, Journal of Economic Literature, Vol XXXV (March 1997), pp. 13-39

• Binder, John J., 1998, “The Event Study Methodology Since 1969”, Review of Quantitative Finance and Accounting, 11 (1998): 111-137

• Corrado, Charles J, 2011, “Event studies: A methodology review”, Journal of Accounting and Finance, Vol. 51 (2011), 207–234

Contents

• Back ground• Method• Issue of model

I. An event study is

• An event study is a statistical technique that estimates the stock price impact of occurrences such as mergers, earnings announcements, …

• The basic notion is to disentangle the effects of two types of information on stock prices:– information that is specific to the firm under

question (e.g., dividend announcement) and – information that is likely to affect stock prices

marketwide (e.g., change in interest rates).

Applications of event studies

• Earliest event study: Dolley (1933)• Ball and Brown (1968) and Fama et al. (1969)

planted seeds of financial research that continue to flourish decades later

• No one really knows how many event studies have been published.

• Kothari and Warner (2005) report that over the period 1974–2000, five major finance journals published 565 articles containing event study results

Applications of event studies

In practice, event studies have been used for two major reasons:

• 1) to test the null hypothesis that the market efficiently incorporates information (see Fama (1991) for a summary of this evidence)

• 2) under the maintained hypothesis of market efficiency, at least with respect to publicly available information, to examine the impact of some event on the wealth of the firm's security holders.

Outline of an event study

1. Event definition2. Selection criteria3. Normal and abnormal returns4. Estimating procedure5. Testing procedure6. Empirical results7. Interpretation and conclusions

II. Models for measuring normal returns

• Constant-mean-return model• Market model• Other statistical model• Economic models

Constant-mean return model

• Assume mean return is unchanged, with constant variance

• Simplest model, often yields resutls similar to more sophisticated models (Brown, Warner 1980, 1985).

• May be because: variance normally not reduce much using more

Market model

• Assume joint normality of asset returns (stock market and the company stock)

• By removing the portion of the return that is related to variation in the market’s return, the variance of abnormal return is reduced. increase ability to detect event effects.

• Higher R2, greater is the variance reduction of the abnormal return, larger is ability to detect abnormal return.

Measuring and analyzing abnormal return

• Estimation of the market model• Statistical properties of abnormal returns• Aggregation of abnormal returns

Economic models

• CAPM: used porpularly in 1970s. Restrictions imposed on stock returns (using market risk premium) is questionable (Fama, French 1996). Possibly, results the studies may be sensitive to the specific CAPM restrictions. Use of CAPM in event studies has almost ceased.

Economic models

• APT (Arbitrage Pricing Theory): with the APT, the most important factor behaves like a market factor and additional factors add relatively little explanatory power.

• Gains from using APT versus the market model are small.

III. Event window and Measuring abnormal returns

• t= 0: event date

Estimation of the market model• Under general conditions ordinary least

squares estimation (OLS) is a consistent estimation procedure for the market model parameters.

• Abnormal return estimation using market model:

• AR is disturbance term of the market model calculated on an out of sample basis.

Estimation of the market model

• Variance of abnormal return:

• First term is variance of disturbance in market model (equation (3)

• Second term is additional variance due to sampling error in and .

• When estimation window is long (large L1), second term approaches zero.

Aggregation of abnormal returns• To draw overall inferences for the event,

aggregation of abnormal return observations is helpful.

• (1) Aggregate through time and (2) across securities

• Aggregating thru time can be done by calculating CAR:

Aggregation of abnormal returns

• Aggregating across security can be done by calculating average AR:

Aggregation of abnormal returns

• Aggregating both thru time and across securities:

• Hypothesis testing: H0: the event has no information impact:

Further issues

• Role of sampling interval• Inferences with event-date uncertainty• Possible biases

Assignments

• Group assignments:• 1/ read and present 2 papers• 2/ test effect of M&A announcement on VN

stock market