Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55290
Title: Evaluating long-horizon event study methodology
Authors: Ang, JS
Zhang, S 
Issue Date: 2015
Publisher: Springer New York
Source: In CF Lee & JC Lee (Eds.), Handbook of financial econometrics and statistics, p. 383-411. New York: Springer New York, 2015 How to cite?
Abstract: We describe the fundamental issues that long-horizon event studies face in choosing the proper research methodology and summarize findings from existing simulation studies about the performance of commonly used methods. We document in details how to implement a simulation study and report our own findings on large-size samples. The findings have important implications for future research. We examine the performance of more than 20 different testing procedures that fall into two categories. First, the buy-and-hold benchmark approach uses​ a benchmark to measure the abnormal buy-and-hold return for every event firm and tests the null hypothesis that the average abnormal return is zero. Second, the calendar-time portfolio approach forms a portfolio in each calendar month consisting of firms that have had an event within a certain time period prior to the month and tests the null hypothesis that the intercept is zero in the regression of monthly portfolio returns against the factors in an asset-pricing model. We find that using the sign test and the single most correlated firm being the benchmark provides the best overall performance for various sample sizes and long horizons. In addition, the Fama-French three-factor model performs better in our simulation study than the four-factor model, as the latter leads to serious over-rejection of the null hypothesis. We evaluate the performance of bootstrapped Johnson's skewness-adjusted t-test. This computation-intensive procedure is considered because the distribution of long-horizon abnormal returns tends to be highly skewed to the right. The bootstrapping method uses repeated random sampling to measure the significance of relevant test statistics. Due to the nature of random sampling, the resultant measurement of significance varies each time such a procedure is used. We also evaluate simple nonparametric tests, such as the Wilcoxon signed-rank test or the Fisher's sign test, which are free from random sampling variation.
URI: http://hdl.handle.net/10397/55290
ISBN: 9781461477501
9781461477495
DOI: 10.1007/978-1-4614-7750-1_14
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