Using an unsupervised topic modelling methodology, we construct a cross-firm similarity measure based on the various topics extracted from Management Discussion and Analysis texts. Our findings indicate that the returns of firms with similar textual topics predict the focal firms’ future stock returns. A long-short portfolio constructed on this basis yields an annualised alpha of 17.03%. Further analyses show that the return predictability is stronger for stocks subject to limited investor attention and limits to arbitrage. Additionally, our textual linkage measure can also predict future earnings surprises. Overall, mispricing due to sluggish information incorporation acts as a potential explanation for return predictability.

中文翻译：

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通过文本主题相似性揭示公司间联系：金融市场的动量分析


使用无监督主题建模方法，我们根据从管理讨论和分析文本中提取的各种主题构建了跨公司相似性度量。我们的研究结果表明，具有相似文本主题的公司的回报可以预测焦点公司未来的股票回报。在此基础上构建的多空投资组合的年化阿尔法率为 17.03%。进一步分析表明，投资者关注度有限且套利受到限制的股票的回报可预测性更强。此外，我们的文本链接度量还可以预测未来的收益惊喜。总体而言，由于信息整合缓慢而导致的错误定价是回报可预测性的一个潜在解释。