Overlap in business aspects serves as a proxy for firm relatedness. Employing an unsupervised topic modelling methodology from machine learning, we characterize the attention allocations of earnings conference call participants (corporate executives, financial analysts, and investors) over the topics discussed. We construct a novel topic similarity measure that captures incremental, difficult-to-observe, and time-varying firm relatedness. However, valuable information from topic peers is not incorporated into stock price in a timely fashion. A long-short strategy based on the returns of topic peers yields a monthly alpha of approximately 69 basis points. Furthermore, return predictability stems primarily from similar business models, customer management, and influential macroeconomic situations. Return predictability is more pronounced among focal firms with higher information complexities and arbitrage costs. Overall, this study provides a novel approach to automatically summarise firms' business aspects in focus and highlights the asset pricing implications of investors' underreactions to non-obvious and dynamic firm relatedness hidden in earnings conference calls.

业务方面的重叠可以作为公司相关性的代表。采用机器学习中的无监督主题建模方法，我们描述了收益电话会议参与者（公司高管、财务分析师和投资者）对所讨论主题的注意力分配。我们构建了一种新颖的主题相似性度量，可以捕获增量、难以观察和随时间变化的公司相关性。然而，来自主题同行的有价值的信息并没有及时纳入股价。基于主题同行回报的多空策略产生约 69 个基点的每月阿尔法。此外，回报的可预测性主要源于相似的商业模式、客户管理和有影响力的宏观经济形势。在信息复杂性和套利成本较高的焦点公司中，回报可预测性更为明显。总体而言，这项研究提供了一种新颖的方法来自动总结公司重点关注的业务方面，并强调投资者对隐藏在收益电话会议中的不明显和动态公司关联性反应不足对资产定价的影响。