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%.

We use a proprietary data set with detailed executive compensation information to examine the relationship between the incentives of the tax director and GAAP and cash effective tax rates, the book-tax gap, and measures of tax aggressiveness. We find that the incentive compensation of the tax director exhibits a strong negative relationship with the GAAP effective tax rate, but little relationship with the other tax attributes. We interpret these results as indicating that tax directors are provided with incentives to reduce the level of tax expense reported in the financial statements.