Random thought 1:
Note that reverse p-hacking can also occur, when an author wants low p-values. In the study below, for example, the deck could be stacked with all sorts of dubious/spurious "anomaly variables" that no one ever took seriously. Then of course a very large number would wind up with low p-values. I am not suggesting that the study below is guilty of this; rather, I simply had never thought about reverse p-hacking before, and this paper led me to think of the possibility, so I'm relaying the thought.
Related random thought 2:
Replicating Anomalies
by Kewei Hou, Chen Xue, Lu Zhang - NBER Working Paper #23394
Abstract:
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.
Related random thought 2:
It would be interesting to compare anomalies published in "top journals" and "non-top journals" to see whether the top journals are more guilty or less guilty of p-hacking. I can think of competing factors that could tip it either way!
by Kewei Hou, Chen Xue, Lu Zhang - NBER Working Paper #23394
Abstract:
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.
NBER version at http://papers.nber.org/papers/w23394?utm_campaign=ntw&utm_medium=email&utm_source=ntw
Ungated version at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2961979
Ungated version at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2961979
