Evaluating Long-Horizon Event Study Methodology

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 twenty 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 overrejection 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 longhorizon 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.

Three Decades of IPO Markets in Canada: Evolution, Risk and Return

“…We estimate abnormal returns following IPOs using the buy‐and‐hold abnormal return (BHAR) method, the classical approach that consists of comparing the performance of portfolios composed of firms of each group (Ang and Zhang, ). From 1997 to 2015, the S&P/TSX index outperforms the BMO Small Cap unweighted index (BMO Small Cap) by about 292 basis points annually, indicating a strong reverse size effect.…”

Is Window-Dressing around Going Public Beneficial? Evidence from Poland

“…They also report that it is unlikely that the endogeneity of clustering of IPOs explains the long-term underperformance. The problem of testing endogenous events (not only IPOs) for small capital markets like Warsaw Stock Exchange in Poland, could be developed in a separate study in the future to continue the discussion of Schultz (2003), Viswanathan and Wei (2008), or Ang and Zhang (2015).…”

Value investing via Bayesian inference

“…Open Access funding enabled and organized by Projekt DEAL. ORCID Marcel Rueenaufer https://orcid.org/0000-0003-0482-4958 ENDNOTES 1 Kothari (2001) and Ang and Zhang (2014) provide overviews of problems related to longer-horizon event studies. Since valuation models have specific data requirements and longer term event studies require a specification of a "normal" benchmark return and risk, misspecifications and data problems can limit the usefulness of the results.…”