Interacting Biases, Non-Normal Return Distributions and the Performance of Parametric and Bootstrap Tests for Long- Horizon Event Studies

We report simulations of abnormal buy-and-hold stock return tests. Using benchmark portfolios purged of new-listings and rebalancing biases, we find severe misspecification of parametric tests, due in part to skewness. We document a negative relation between skewness bias and sample size, and an overlapping-horizons bias. Both biases become more severe as the holding period lengthens. The biases interact such that tests can be well-specified in one situation but not another. A two-groups test using winsorized abnormal returns yields correct specification and considerable power in many situations. Bootstrap tests detect more negative than positive abnormal returns, but are well-specified and reasonably powerful.