Econometrics of Event Studies Part 1

“Econometrics of Event Studies” Econometrics of Event Studies Key words: Event study, abnormal returns, short-horizon tests, long-horizon tests, cross-sectional tests, risk adjustment 2 ABSTRACT 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. 3 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 1 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., Beaver, 1968, and Campbell and Wasley, 1996), operating (accounting) performance (e.g., Barber and Lyon, 1996), and earnings management via discretionary accruals (e.g., Dechow, Sloan, and Sweeney, 1995, and Kothari, Leone, and Wasley, 2005). 5 much attention. In the field of law and economics, event studies are used to examine the effect of regulation, as well as to assess damages in legal liability cases. The number of published event studies easily exceeds 500 (see section 2), and continues to grow. A second and parallel literature, which concentrates on the methodology of event studies, began in the 1980’s. Dozens of papers have now explicitly studied statistical properties of event study methods. Both literatures are mature. From the methodology papers, much is known about how to do – and how not to do – an event study. While the profession’s thinking about event study methods has evolved over time, there seems to be relatively little controversy about statistical properties of event study methods. The conditions under which event studies provide information and permit reliable inferences are well-understood. This chapter highlights key econometric issues in event study methods, and summarizes what we know about the statistical design and the interpretation of event study experiments. Based on the theoretical and empirical findings of the methodology literature, we provide clear guidelines both for producers and consumers of event studies. Rather than provide a comprehensive survey of event study methods, we seek to sift through and synthesize existing work on the subject. We provide many references and borrow heavily from the contributions of published papers. Two early papers that cover a wide range of issues are by Brown and Warner (1980, 1985). More recently, an excellent chapter in the textbook of Campbell, Lo, and MacKinlay (1997) is a careful and broad outline of key research design issues. These standard references are recommended reading, but predate important advances in our understanding of event study methods, in particular on long horizon methods. We provide an updated and much needed overview, and include a bit of new evidence as well. 6 Although much emphasis will be on the statistical issues, we do not view our mission as narrowly technical. As financial economists, our ultimate interest is in how to best specify and test interesting economic hypotheses using event studies. Thus, the econometric and economic issues are interrelated, and we will try to keep sight of the interrelation. In section 2, we briefly review the event study literature and describe the changes in event study methodology over time. In Section 3 we discuss how to use events studies to test economic hypotheses. We also characterize the properties of the event study tests and how these properties depend on variables such as security volatility, sample size, horizon length, and the process generating abnormal returns. Section 4 is devoted to issues most likely encountered when conducting long-horizon event studies. The main issues are risk adjustment, cross- correlation in returns, and changes in volatility during the event period. 2. The Event Study Literature: Basic Facts 2.1 The stock and flow of event studies To quantify the enormity of the event study literature, we conducted a census of event studies published in 5 leading journals: the Journal of Business (JB), Journal of Finance (JF), Journal of Financial Economics (JFE), Journal of Financial and Quantitative Analysis (JFQA), and the Review of Financial Studies (RFS). We began in 1974, the first year the JFE was published. Table 1 reports the results for the years 1974 through 2000. The total number of papers reporting event study results is 565. Since many academic and practitioner-oriented journals are excluded, these figures provide a lower bound on the size of the literature. The number of papers published per year increased in the 1980’s, and the flow of papers has since been stable. The 7 peak years are 1983 (38 papers), 1990 (37 papers), and 2000 (37 papers). All five journals have significant representation. The JFE and JF lead, with over 200 papers each. Table 1 makes no distinction between long horizon and short horizon studies. While the exact definition of “long horizon” is arbitrary, it generally applies to event windows of 1 year or more. Approximately 200 of the 565 event studies listed in Table 1 use a maximum window length of 12 months or more, with no obvious time trend in the year by year proportion of studies reporting a long-horizon result. No survey of these 565 event study papers is attempted here. For the interested reader, the following are some examples of event study surveys. MacKinlay (1997) and Campbell, Lo, and MacKinlay (1997) document the origins and breadth of event studies. The relation of event studies to tests of market efficiency receives considerable attention in Fama (1991), and in recent summaries of long-horizon tests in Kothari and Warner (1997) and Fama (1998). Smith (1986) presents reviews of event studies of financing decisions. Jensen and Ruback (1983), Jensen and Warner (1988), and Jarrell, Brickley and Netter (1988) survey corporate control events. Recently, Kothari (2001) reviews event studies in the accounting literature. 2.2 Changes in event study methods: the big picture Even the most cursory perusal of event studies done over the past 30 years reveals a striking fact: the basic statistical format of event studies has not changed over time. It is still based on the table layout in the classic stock split event study of Fama, Fisher, Jensen, and Roll (1969). The key focus is still on measuring the sample securities’ mean and cumulative mean abnormal return around the time of an event. 8 Two main changes in methodology have taken place, however. First, the use of daily (and sometimes intraday) rather than monthly security return data has become prevalent, which permits more precise measurement of abnormal returns and more informative studies of announcement effects. Second, the methods used to estimate abnormal returns and calibrate their statistical significance have become more sophisticated. This second change is of particular importance for long-horizon event studies. The changes in long-horizon event study methods reflect new findings in the late 1990s on the statistical properties of long-horizon security returns. The change also parallels developments in the asset pricing literature, particularly the Fama-French 3-factor model. While long-horizon methods have improved, serious limitations of long-horizon methods have been brought to light and still remain. We now know that inferences from long-horizon tests “require extreme caution” (Kothari and Warner, 1997, p. 301) and even using the best methods “the analysis of long-run abnormal returns is treacherous” (Lyon, Barber, and Tsai, 1999, p. 165). These developments underscore and dramatically strengthen earlier warnings (e.g., Brown and Warner, 1980, p. 225) about the reliability - or lack of reliability - of longhorizon methods. This contrasts with short-horizon methods, which are relatively straightforward and trouble-free. As a result, we can have more confidence and put more weight on the results of short-horizon tests than long-horizon tests. Short-horizon tests represent the “cleanest evidence we have on efficiency” (Fama, 1991, p.1602), but the interpretation of longhorizon results is problematic. As discussed later, long-horizon tests are highly susceptible to the joint-test problem, and have low power. Of course these statements about properties of event study tests are very general. To provide a meaningful basis for assessing the usefulness of event studies - both short- and long- horizon – it is necessary to have a framework that specifies: i) the economic and statistical hypotheses in an event study, and ii) an objective basis for measuring and comparing the performance of event study methods. Section 3 lays out this framework, and summarizes general conclusions from the methodology literature. In the remainder of the chapter, additional issues and problems are considered with more specificity.