In this episode, I have three guests on the show with me: Kewei Hou of Ohio State University, Chen Xue of the University of Cincinnati, and Lu Zhang of Ohio State University.
Kewei, Chen, and Lu have coauthored a paper titled “Replicating Anomalies,” a large-scale replication study that re-tests hundreds of so-called “anomalies” in financial markets. An anomaly is a predictable pattern in stock returns, or stated differently, it is a deviation from the efficient markets hypothesis. Their abstract reads as follows:
The anomalies literature is infested with widespread p-hacking. We replicate the entire anomalies literature in finance and accounting by compiling a largest-to-date data library that contains 447 anomaly variables. With microcaps alleviated via New York Stock Exchange breakpoints and value-weighted returns, 286 anomalies (64%) including 95 out of 102 liquidity variables (93%) are insignificant at the conventional 5% level. Imposing the cutoff t-value of three raises the number of insignificance to 380 (85%). Even for the 161 significant anomalies, their magnitudes are often much lower than originally reported. Out of the 161, the q-factor model leaves 115 alphas insignificant (150 with t < 3). In all, capital markets are more efficient than previously recognized.
We discuss the process of replicating these anomalies, issues involving the use of equal-weighted vs value-weighted returns, and the problems of p-hacking in finance research.
Works Cited
Hamermesh, Daniel S. 2007. “Replication in Economics.” Canadian Journal of Economics 40(3):715?733.
Other Links
The Marginal Revolution post on this paper.
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