We study out-of-sample returns on 153 anomalies in equities documented in the academic literature. We show that machine learning techniques that aggregate all the anomalies into one mispricing signal are profitable around the globe and survive on a liquid universe of stocks. We investigate the value of international evidence for selection of quantitative strategies that outperform out-of-sample. Past performance of quantitative strategies in regions other than the United States does not help to pick out-of-sample winning strategies in the U.S. Past evidence from the U.S., however, captures most of the return predictability outside the U.S.

中文翻译：

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进行异常研究是否值得？具有国际证据的机器学习方法

我们研究了学术文献中记录的 153 种股票异常的样本外回报。我们表明，将所有异常情况汇总为一个错误定价信号的机器学习技术在全球范围内都是有利可图的，并且可以在流动的股票世界中生存。我们调查了国际证据在选择优于样本外的定量策略方面的价值。美国以外地区量化策略的过往表现无助于在美国挑选样本外获胜策略 然而，来自美国的过往证据涵盖了美国以外地区的大部分回报可预测性