Publication Title

Washington University Law Review


An event study is a statistical method for determining whether some event—such as the announcement of earnings or the announcement of a proposed merger—is associated with a statistically significant change in the price of a company’s stock. The main inputs to an event study are historical stock returns for the companies under study, benchmark returns like the return to the broader stock market, and standard statistical tests like t-tests that are used to test for statistical significance. In securities litigation and regulation, event studies are used primarily to detect the impact of disclosures of alleged fraud on the price of a single traded security.

But are event studies in securities litigation reliable? What is interesting about the use of event studies in securities litigation is that the methodology litigants use in court differs from the methodology that economists apply in their research. With few exceptions, securities litigation event studies are single-firm event studies, while almost all academic research event studies are multi-firm event studies. Multi-firm event studies are generally accepted in financial economics research, and peer-reviewed journals contain them by the hundreds. By contrast, single-firm event studies—the mainstay of modern securities fraud litigation—are almost nonexistent in peer-reviewed journals.

Importing a methodology that economists developed for use with multiple firms into a single-firm context creates three substantial difficulties. First, single-firm event studies suffer from a severe signal-to-noise problem in that they lack statistical power to detect price impacts unless the price impacts are quite large. Inattention to statistical power lowers the deterrent effect of the securities laws by giving a “free pass” to some economically meaningful price impacts and may encourage more small- and mid-scale fraud than is socially optimal given the costs of litigation. Second, single-firm event studies do not average away confounding effects. While this problem is well known, some courts have unrealistic expectations of litigants’ ability to quantitatively decompose observed price impacts into those caused by alleged fraud and those unrelated to alleged fraud. Third, low statistical power and confounding effects combine to generate sizeable upward bias in detected price impacts and therefore in damages. To improve the accuracy of adjudication in securities litigation, we suggest that litigants report the statistical power of their event studies, that courts allow litigants flexibility to deal with the problem of confounding effects, and that courts and litigants consider the possibility of upward bias in the detection of price impacts and the estimation of damages.