论文标题
横截面回报可预测性中大多数声称的统计发现可能是正确的
Most claimed statistical findings in cross-sectional return predictability are likely true
论文作者
论文摘要
我为横断面回报可预测性出版物中的错误发现率（FDR）开发了简单，直观的界限。最简单的界限仅需要以前论文的汇总统计数据，并表明FDR在以前的九项研究中八个中的八个中最多是25％。更精致的界限显示FDR最多是9％。这些研究包括Harvey，Liu和Zhu（2016），他们``认为金融经济学中大多数声称的发现可能都是错误的。''我证明了Harvey等人的估计是如何暗示9％的FDR，而他们的结论是误解了``误解了``````''''''''''''''''''''''''''''''''
I develop simple and intuitive bounds for the false discovery rate (FDR) in cross-sectional return predictability publications. The simplest bounds require only summary statistics from previous papers, and show the FDR is at most 25% in eight out of nine previous studies. A more refined bound shows the FDR is at most 9%. These studies include Harvey, Liu, and Zhu (2016), who ``argue that most claimed findings in financial economics are likely false.'' I demonstrate how Harvey et al.'s own estimates imply an FDR of 9%, and that their conclusion stems from misinterpreting ``insignificant factor'' as ``false discovery.''
