论文标题
资产定价研究的出版偏见
Publication Bias in Asset Pricing Research
论文作者
论文摘要
研究人员更有可能分享著名的发现。结果，已发表的发现倾向于夸大现实现象的幅度。这种偏见是资产定价研究的自然问题，该研究发现了数百个回报预测因素，几乎没有共识。 关于出版偏见的经验证据来自大规模的元研究。横截面回报可预测性的元研究已经解决了四个风格化的事实，证明出版偏见并不是主要因素：（1）几乎所有发现都可以复制，（2）可预测性持续存在，（3）经验$ t $ statixistical远离2.0，并且（4）预测较大，并且（4）相关性较弱。这些事实中的每一个都至少在三个元研究中得到了证明。 经验贝叶斯的统计数据将这些事实变成了出版偏见。来自三个元研究的估计发现，平均校正（收缩）仅占样本中平均收益的10％至15％，并且推断朝错误方向（错误的发现率）的风险小于10％。 元研究还发现，在多个测试算法中，$ t $统计的障碍超过3.0，在替代投资组合测试中，回报率弱了30％至50％。这些事实很容易被误解为出版偏见效应的证据。我们澄清了这些误解和其他误解，包括将``大部分错误的发现''与``许多微不足道的发现''相结合，``数据侦探''与``数据侦探''与``流动性效应''和``流动性效应''和``失败的复制''与``失败的复制'' 横截面文献之外的元研究很少见。横断面元研究的四个事实为将来的研究提供了一个框架。我们用初步的股权高级可预测性重新审查。
Researchers are more likely to share notable findings. As a result, published findings tend to overstate the magnitude of real-world phenomena. This bias is a natural concern for asset pricing research, which has found hundreds of return predictors and little consensus on their origins. Empirical evidence on publication bias comes from large scale meta-studies. Meta-studies of cross-sectional return predictability have settled on four stylized facts that demonstrate publication bias is not a dominant factor: (1) almost all findings can be replicated, (2) predictability persists out-of-sample, (3) empirical $t$-statistics are much larger than 2.0, and (4) predictors are weakly correlated. Each of these facts has been demonstrated in at least three meta-studies. Empirical Bayes statistics turn these facts into publication bias corrections. Estimates from three meta-studies find that the average correction (shrinkage) accounts for only 10 to 15 percent of in-sample mean returns and that the risk of inference going in the wrong direction (the false discovery rate) is less than 10%. Meta-studies also find that $t$-statistic hurdles exceed 3.0 in multiple testing algorithms and that returns are 30 to 50 percent weaker in alternative portfolio tests. These facts are easily misinterpreted as evidence of publication bias effects. We clarify these misinterpretations and others, including the conflating of ``mostly false findings'' with ``many insignificant findings,'' ``data snooping'' with ``liquidity effects,'' and ``failed replications'' with ``insignificant ad-hoc trading strategies.'' Meta-studies outside of the cross-sectional literature are rare. The four facts from cross-sectional meta-studies provide a framework for future research. We illustrate with a preliminary re-examination of equity premium predictability.
