资产定价研究中的公开性偏差
Publication Bias in Asset Pricing Research
摘要：
研究人员更有可能分享显著的发现。因此，发表的研究结果往往夸大了现实世界现象的严重性。这种偏见是资产定价研究的一个自然关注点，该研究发现了数百个回报预测因子，但对它们的起源几乎没有共识。发表偏倚的经验证据来自大规模荟萃研究。横截面回报可预测性的元研究已经确定了四个程式化的事实，证明出版偏差不是一个主导因素：（1）几乎所有的发现都可以复制，（2）可预测性持续样本外，（3）经验$t$ -统计量远大于2.0，（4）预测因子弱相关。这些事实中的每一个都在至少三项元研究中得到了证明。经验贝叶斯统计将这些事实转化为出版偏差校正。三项元研究的估计发现，平均修正（收缩）仅占样本内平均回报的10%至15%，并且推断错误方向的风险（错误发现率）小于10%。元研究还发现，在多种测试算法中，$t$ -统计障碍超过3.0，在替代投资组合测试中，回报率弱30%至50%。这些事实很容易被误解为发表偏倚效应的证据。我们澄清了这些误解和其他误解，包括将“大部分错误发现”与"许多无关紧要的发现“、"数据窥探”与"流动性影响“以及"失败的复制”与"无关紧要的临时交易策略“混为一谈。“横向文献之外的元研究很少。来自横截面元研究的四个事实为未来的研究提供了一个框架。我们说明了一个初步的重新审查股权溢价的可预测性。
英文摘要：
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.
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