An event study is used to measure the relationship between an event that affects securities and the return of those securities. For example, events such as a regulatory change or market shock may affect many securities simultaneously. On the other hand, events such as a policy change or stock split may only impact certain securities.
Event studies are often used to test the “efficient market hypothesis.” The efficient market hypothesis is a theory based on research put forward by Eugene Fama, and states that asset prices fully reflect all information available. Abnormal returns that persist after an event occurs, or abnormal returns that are associated with an anticipated event contradict the efficient market hypothesis. Event studies are also valuable in gauging the significance of an event.
Define the event and identify the time period. The timing of the event is not necessarily the period during which the event occurred, but may be the investment period preceding the announcement of the event
Arrange the security performance data relative to the timing of the event. If information about the event is released completely on a specific day with enough time for traders to react, the day of the announcement is ZERO. Then, measurement periods preceding and following the event are selected. For example, if the 90 trading days preceding the event and the 10 days following the event are designated as the pre= and post-event periods, the pre-event trading days would be labeled t – 90, t – 89, and so on, and the post-event trading days would be labeled t + 1, t +2, and so on.
Separate the security-specific component of return from the security’s total return during the pre-event measurement period.
Estimate the standard deviation of the daily security-specific returns during the pre-event measurement period from 90 days before the event announcement through the day before the announcement (t – 90 through t – 1).
Isolate the security-specific return during the event and post-event periods. To estimate the security-specific return each day during these periods, subtract from each security’s total return the security’s alpha and beta times the market’s return on that day.
Aggregate the security-specific returns and standard deviations across the sample of securities on the event day and the post-event days. Sum the security-specific returns for each day and divide by the number of securities in the sample.
Test the hypothesis that the security-specific returns on the event day and the post-event days differ significantly from zero. If the event is unanticipated and the t-statistic is significant on the day of the event but insignificant on the days following, you can reasonably conclude that the event does affect security returns but does not contradict the efficient market hypothesis. If, on the other hand, the t-statistics continue to be significant on the post-event days, you may conclude that the market is inefficient in that it does not quickly absorb new information. Therefore, the event may contradict the efficient market hypothesis.
How to measure an event is not always obvious. For example, suppose that the event is an annual earnings announcement. The announcement that annual earnings are $3.00 a share is meaningless unless it goes against market expectations. Moreover, the market’s expectations are conditioned by earlier information releases pertaining to earnings. Therefore, the first issue in measuring the event is to separate the unanticipated component of the announcement from the anticipated component.
Another issue with measuring events pertains to the influence of confounding factors. Suppose the event is the announcement of a change in dividend policy. For some securities, this announcement may coincide with an announcement about earnings. This coincident information is called a confounding event, or an event that might distort the effect of the event on the security’s return.