Abstract
We propose three nonparametric tests for the null of no event-induced shifts in the distribution of stock returns. One test is the natural extension of the popular Corrado rank test to the case of cross-sectionally dependent returns, while the other two are based on new ideas. Unfortunately only for one of these tests a solid theory for approximating the distribution of the statistic can be derived, but some simulation experiments confirm that normality is a good approximation also for the other two. The new tests are compared to a widely used parametric test (Patell) through simulation experiments and are shown to compare favorably in terms of power. Simulation results are based on bootstrapping daily stock returns from the S&P100 and NASDAQ indexes.
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