「Replicating Anomalies」というNBER論文上がっている(H/T Francis Dieboldungated版(SSRN))。著者はKewei Hou(オハイオ州立大)、Chen Xue(シンシナティ大)、Lu Zhang(オハイオ州立大)。


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.



Some studies exclude stocks with prices per share lower than $1 or $5. We do not impose such a sample screen. Many studies also equal-weight portfolio returns. We instead use value-weights.
We do so for several reasons. First, value-weights more accurately reflect the wealth effect experienced by investors, as emphasized by Fama (1998). Second, Fama and French (2008) document that microcaps are influential in equal-weighted returns. Microcaps are stocks with the market equity below the 20th percentile of NYSE stocks. Microcaps are on average only 3% of the market value of the NYSE-Amex-NASDAQ universe, but account for about 60% of the total number of stocks....
...With NYSE-Amex-NASDAQ breakpoints, microcaps typically account for more than 60% of the stocks in extreme deciles. These microcaps can greatly inflate the anomalies, especially when combined with equal-weights. In contrast, using NYSE breakpoints assigns a fair number of small and big stocks into extreme deciles, alleviating the impact of microcaps.

*2:今回の論文と同じ著者たちが2015年論文WP)で打ち出したモデル。cf. 関連日本語ブログ記事関連日本語論文

トラックバック - http://d.hatena.ne.jp/himaginary/20170515/Replicating_Anomalies