Econometrics of Event Studies

Topics: Statistical hypothesis testing, Standard deviation, Null hypothesis Pages: 49 (14931 words) Published: November 14, 2010
“Econometrics of Event Studies”
S. P Khotari and Jerold B. Warner

Forthcoming in B. Espen Eckbo (ed.), Handbook of Corporate Finance: Empirical Corporate Finance, Volume A (Handbooks in Finance Series, Elsevier/North-Holland), Ch. 1, 2006

Econometrics of Event Studies

S.P. Kothari Sloan School of Management, MIT

Jerold B. Warner William E. Simon Graduate School of Business Administration University of Rochester

May 19, 2006

Key words: Event study, abnormal returns, short-horizon tests, long-horizon tests, crosssectional tests, risk adjustment

This article will appear in the Handbook of Corporate Finance: Empirical Corporate Finance (Elsevier/North-Holland), which is edited by B. Espen Eckbo. We thank Espen Eckbo, Jon Lewellen, Adam Kolasinski, and Jay Ritter for insightful comments, and Irfan Safdar and Alan Wancier for research assistance.



The number of published event studies exceeds 500, and the literature continues to grow. We provide an overview of event study methods. Short-horizon methods are quite reliable. While long-horizon methods have improved, serious limitations remain. A challenge is to continue to refine long-horizon methods. We present new evidence illustrating that properties of event study methods can vary by calendar time period and can depend on event sample firm characteristics such as volatility. This reinforces the importance of using stratified samples to examine event study statistical properties.


Table of Contents 1. Introduction and Background 2. The Event Study Literature 2.1 The stock and flow of event studies 2.2 Changes in event study methods: the big picture 3. Characterizing Event Study Methods 3.1 An event study: the model 3.2 Statistical and economic hypotheses 3.3 Sampling distributions and test statistics 3.4 Criteria for “reliable” event study tests 3.5 Determining specification and power 3.6 A quick survey of our knowledge 3.7 Cross-sectional tests 4. Long-Horizon Event Studies 4.1 Background 4.2 Risk adjustment and expected returns 4.3 Approaches to Abnormal Performance Measurement 4.4 Significance tests for BHAR and Jensen-alpha measures 4.4.1 Skewness 4.4.2 Cross-correlation 4.4.3 The bottom line

1. Introduction and Background This chapter focuses on the design and statistical properties of event study methods. Event studies examine the behavior of firms’ stock prices around corporate events.1 A vast literature written over the past several decades has become an important part of financial economics. Prior to that time, “there was little evidence on the central issues of corporate finance. Now we are overwhelmed with results, mostly from event studies” (Fama, 1991, p. 1600). In a corporate context, the usefulness of event studies arises from the fact that the magnitude of abnormal performance at the time of an event provides a measure of the (unanticipated) impact of this type of event on the wealth of the firms’ claimholders. Thus, event studies focusing on announcement effects for a short-horizon around an event provide evidence relevant for understanding corporate policy decisions. Event studies also serve an important purpose in capital market research as a way of testing market efficiency. Systematically nonzero abnormal security returns that persist after a particular type of corporate event are inconsistent with market efficiency. Accordingly, event studies focusing on long-horizons following an event can provide key evidence on market efficiency (Brown and Warner, 1980, and Fama, 1991). Beyond financial economics, event studies are useful in related areas. For example, in the accounting literature, the effect of earnings announcements on stock prices has received

We discuss event studies that focus only on the mean stock price effects. Many other types of event studies also appear in the literature, including event studies that examine return variances (e.g., Beaver, 1968, and Patell, 1976), trading volume (e.g.,...
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