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This paper introduces machine learning–based methods designed to measure the evasiveness and incoherence of responses from more-informed individuals during real-time strategic conversations. It tests the efficacy of these methods using the question-and-answer segments of earnings conference calls, where managers are subjected to scrutiny by analysts. The article underscores the largely untapped potential for extracting valuable financial insights from the dialogues between managers and analysts during these calls—a data source that current fintech solutions have largely ignored. Furthermore, the research breaks new ground by integrating machine learning with asset pricing, a promising avenue in light of rapid technological advances in artificial intelligence. From a practical standpoint, the study provides less-informed participants in strategic conversations with tools to identify when their more-informed counterparts are being evasive or incoherent. This ability allows them to pose more incisive questions, leading to better-informed decisions in various fields, including investing and hiring. Moreover, the paper contends that as AI technology continues to evolve, it will compel more-informed parties to adopt greater transparency. This shift will enhance both the efficiency and the transparency of markets and institutions, ultimately benefiting society as a whole.
Chen et al. (Wed,) studied this question.