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By Fred R. Kaen

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2. Brown–Warner simulation To directly address the issue of event study properties, the standard tool in event study methodology research is simulation procedures that use actual security return data. The motivation and specific research design is initially laid out in Brown and Warner (1980, 1985), and has been followed in almost all subsequent methodology research. Much of what is known about general properties of event study tests comes from such large-scale simulations. 7 Different event study methods are simulated by repeated application of each method to samples that have been constructed through a random (or stratified random) selection of securities and random selection of an event date to each.

The standard deviation of the portfolio returns can be used to assess the significance of the event-window average abnormal return. The cross-sectional 4 Standard tests are “classical” rather than “Bayesian”. A Bayesian treatment of event studies is beyond the scope of this chapter. 5 An alternative would be a test statistic that aggregates standardized abnormal returns, which means each observation is weighted in inverse proportion of the standard deviation of the estimated abnormal return. The standard deviation of abnormal returns is estimated using time-series return data on each firm.

165). , Brown and Warner, 1980, p. 225) about the reliability—or lack of reliability—of long-horizon methods. This contrasts with shorthorizon 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 long-horizon results is problematic. As discussed later, long-horizon tests are highly susceptible to the joint-test problem, and have low power.

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