论文标题
同行评审的研究有助于预测股票回报吗？
Does Peer-Reviewed Research Help Predict Stock Returns?
论文作者
论文摘要
我们通过利用资产定价文献的独特特征来研究经济研究中包含的增量信息。该领域提供标准化的绩效指标，大规模复制和幼稚的数据挖掘，作为使用经济研究的替代方法。我们发现，在2.0多个t统计量的29,000个会计比率中，挖掘会计率可导致类似于同行评审研究的横断面回报可预测性。对于这两种方法，在原始样本周期之后，约有50％的可预测性保留。由同行评审的风险解释或均衡模型支持的预测因素在样本后表现不佳，这表明同行评审系统地错误标记错误为风险，尽管只有20％的预测因子被标记为风险。数据挖掘产生了经济研究的其他特征，包括随着原始样本周期结束和样本后衰减的速度的回报率上升。它还揭示了诸如投资，发行和应计的主题 - 在发布之前数十年。
We examine the incremental information contained in economic research by leveraging unique features of the asset pricing literature. This field offers standardized performance measures, large scale replications, and naive data mining as an alternative to using economic research. We find that mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional return predictability similar to peer-reviewed research. For both methods, about 50% of predictability remains after the original sample periods. Predictors supported by peer-reviewed risk explanations or equilibrium models underperform other predictors post-sample, suggesting peer review systematically mislabels mispricing as risk, though only 20% of predictors are labelled as risk. Data mining generates other features of economic research including the rise in returns as original sample periods end and the speed of post-sample decay. It also uncovers themes like investment, issuance, and accruals -- decades before they are published.
