Business aspects in focus, investor underreaction and return predictability
利用机器学习从财报电话会议中提取公司业务主题相似度，发现投资者对同行信息反应不足，据此构建的多空策略月均超额收益约69个基点。
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.