Detecting Deceptive Discussions in Conference Calls

Abstract

We estimate classification models of deceptive discussions during quarterly earnings conference calls. Using data on subsequent financial restatements (and a set of criteria to identify especially serious accounting problems), we label each call as “truthful” or “deceptive”. Our models are developed with the word categories that have been shown by previous psychological and linguistic research to be related to deception. Using conservative statistical tests, we find that the out-of-sample performance of the models that are based on CEO or CFO narratives is significantly better than random by 6%-16% and statistically dominates or is equivalent to models based on financial and accounting variables. We find that the answers of deceptive executives have more references to general knowledge, fewer non-extreme positive emotions, and fewer references to shareholder value. In addition, deceptive CEOs use significantly more extreme positive emotion and fewer anxiety words.

Keywords: Deception, Restatements, Linguistic Analysis

JEL Classification: G3, M4