Detecting Deceptive Discussions in Conference Calls
利用季度财报电话会议中的语言特征构建分类模型，识别管理层讨论中的欺骗行为。模型基于CEO和CFO的叙述，其预测表现优于随机猜测，且与财务变量模型相当。欺骗性语言表现为更多提及一般知识、更少极端正面情绪和股东价值提及。
ABSTRACT We estimate linguistic‐based classification models of deceptive discussions during quarterly earnings conference calls. Using data on subsequent financial restatements and a set of criteria to identify severity of accounting problems, we label each call as “truthful” or “deceptive.” Prediction models are then developed with the word categories that have been shown by previous psychological and linguistic research to be related to deception. We find that the out‐of‐sample performance of models based on CEO and/or CFO narratives is significantly better than a random guess by 6–16% and is at least equivalent to models based on financial and accounting variables. The language of deceptive executives exhibits more references to general knowledge, fewer nonextreme positive emotions, and fewer references to shareholder value. In addition, deceptive CEOs use significantly more extreme positive emotion and fewer anxiety words. Finally, a portfolio formed from firms with the highest deception scores from CFO narratives produces an annualized alpha of between −4% and −11%.