An event study analyzes the effects of economic events on security prices. In efficient markets where prices reflect all publicly available information and expected cash flows, any new value-relevant information that impacts investors' assessment of future cash flows will be reflected immediately.1 Therefore, one can measure the economic effect of an event using prices over a relatively short time period.2
After the Supreme Court endorsed the fraud-on-the-market doctrine in Basic v. Levinson3 in 1988, event studies became widely used in securities litigation to assess claims of loss causation as well as evaluate the "materiality" of alleged misstatements or fraudulently omitted information.4 In the recent Halliburton II decision (Halliburton Co. v. Erica P. John Fund, Inc., No. 13-317) the Supreme Court explicitly contemplated the use of event studies by defendants in class certification to rebut the presumption of reliance:
"Suppose a defendant at the certification stage submits an event study looking at the impact on the price of its stock from six discrete events, in an effort to refute the plaintiffs' claim of general market efficiency.... Suppose one of the six events is the specific misrepresentation asserted by the plaintiffs....[and] the evidence shows no price impact with respect to the specific misrepresentation challenged in the suit. The evidence at the certification stage thus shows an efficient market, on which the alleged misrepresentation had no price impact."5
Consequently, future class certification decisions are more likely to involve the adjudication of competing event study analyses to determine the price impact of alleged corrective disclosures. Experts will thus need to consider more carefully the limitations of traditional event study analysis as well as recent developments in the field.
TRADITIONAL EVENT STUDIES
The primary purpose of an event study is to determine whether a particular event is associated with a significant change in price. In securities litigation, the typical example of a loss-inducing event is a corrective disclosure by a company. A corrective disclosure corrects one or more of a company's previous statements that allegedly were false or misleading. Event studies thus help determine two critical links between alleged misrepresentations or omissions made by a defendant and the damages allegedly suffered by the plaintiff class:
1. First, event studies determine whether an alleged misrepresentation or corrective disclosure was associated with a price impact;
2. Second, if there was a price impact, event studies determine how much of this price impact was potentially caused by the alleged misrepresentation or corrective disclosure as opposed to other, unrelated factors.
The effort to resolve these issues using financial econometrics and statistical techniques is a major advantage of event study analysis. However, a key question is how to exclude confounding events to isolate the portion of price movements resulting only from the disclosure of interest.
EVENT STUDY PROCEDURE
Event studies traditionally examine daily returns and follow an established procedure:6
- Event definition: Define the event of interest (that is, the news announcement or disclosure of interest) for the subject company and identify the period over which to examine the event (the "event window");
- Expected and residual returns: Determine a method to calculate the "normal" or "expected" return for the subject company over the event window. The expected return is also referred to as the "conditional expected return," or the expected return conditional on that day's observed market and industry returns. The difference between this expected return and the company's actual return is the residual return (or abnormal return);
- Estimation and testing procedure: Choose an appropriate period over which to estimate expected returns. The standard approach is to run a regression model to isolate the firm-specific stock price return (the percentage daily price change) after controlling for effects of the market, the firm's industry, and, if appropriate, other relevant non-firm-specific factors.
- Statistical inference: Conduct a statistical test to determine whether the residual firm-specific return is significantly different from zero on the event date. If the information released on the event day is value-relevant to an investor, the residual return should be statistically significantly different from zero. The goal here is to avoid improperly rejecting the possibility that the return reflects normal fluctuations and not the event. To address this problem, the observed return should be sufficiently large that this mistake only occurs a small percentage of the time. This percentage is the significance level or size of the test.7 Notably, a statistically significant8 residual return is consistent with the arrival of new company-specific information, but need not result from fraud-related, company-specific information.
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Footnotes
1 Brealey, Richard A., Stewart C. Myers and Franklin Allen, Principles of Corporate Finance, 11th Edition, New York: McGraw-Hill/Irwin (2013), Chapter 6, p. 131. The text states that "only cash flow is relevant" to making investment decisions.
2 Campbell, John Y., Andrew W. Lo and A. Craig MacKinlay, The Econometrics of Financial Markets, Princeton University Press (1997), Chapter 4, p. 149.
3 Basic Inc. v. Levinson, 485 U.S. 224 (1988).
4 Ferrell, Allen and Atanu Saha, "The Loss Causation Requirement for Rule 10b-5 Causes-of-Action: The Implication of Dura Pharmaceuticals v. Broudo," August 2007, Harvard Law and Economics Discussion Paper No. 596, p. 5.
5 Halliburton Co. v. Erica P. John Fund, Inc., No. 13-317, June 23, 2014, (Halliburton II), pp. 19-20. Emphasis added.
6 Campbell, Lo, and MacKinlay, op. cit., Chapter 4, pp. 150-152.
7 This percentage represents the probability of concluding that the event caused a price impact when it did not. This is called a "Type I" error. It is common to fix the size of a test at 5%. See Brav, Alon and J.B. Heaton, "Event Studies In Securities Litigation: Low Power, Confounding Effects, And Bias," March 2015, p. 11.
8 The statistical significance of residual returns is measured by their t-statistics which account for the normal variation in the stock price. A t-statistic of 1.96 or greater in absolute value signifies that there is a 5% chance or less that the residual return was simply
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