This paper uses data on detected misstatements—earnings restatements—and a dynamic model to estimate the extent of undetected misstatements that violate GAAP. The model features a CEO who can manipulate his firm's stock price by misstating earnings. I find the CEO's expected cost of misleading investors is low. The probability of detection over a five‐year horizon is 13.91%, and the average misstatement, if detected, results in an 8.53% loss in the CEO's retirement wealth. The low expected cost implies a high fraction of CEOs who misstate earnings at least once at 60%, with 2%–22% of CEOs starting to misstate earnings in each year 2003–2010, inflation in stock prices across CEOs who misstate earnings at 2.02%, and inflation in stock prices across all CEOs at 0.77%. Wealthier CEOs manipulate less, and the average misstatement is larger in smaller firms.

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%.

The wide-spread reporting of non-GAAP earnings suggests efficiency gains from doing so. By estimating a dynamic investment model, we examine the real implications of investors using both GAAP and non-GAAP earnings to value firms. When investors use the firm’s GAAP earnings only, the firm’s manager—who cares about current stock prices—underinvests, and his investment is sensitive to transitory earnings. Non-GAAP earnings can improve investment efficiency by adjusting for these transitory earnings, but can also hide inefficient investment by introducing opportunistic bias. Although non-GAAP earnings induce overinvestment, they dominate GAAP-only reporting. Counterfactual analysis reveals supplementing GAAP earnings with biased non-GAAP earnings increases firm value by 3.4 percent relative to GAAP-only reporting. Precluding bias reduces overinvestment and further increases firm value by 1 percent. Data Availability: Data are available from the sources cited in the text.

We use detailed assessments of CEO personalities to explore the nature of CEO overconfidence as it is commonly measured. Longholder, the option-based measure of CEO overconfidence introduced by Malmendier and Tate (2005a) and widely used in the behavioral corporate finance and economics literatures, is significantly related to several specific characteristics that are associated with overconfident individuals as well as individuals with lower ability. Similar relations hold for overconfidence measures based on CEOs’ earnings guidance. Investment-cash flow sensitivities are larger for both Longholder and less able CEOs. After controlling for ability and other characteristics, Longholder CEOs’ investments remain significantly more sensitive to cash flows. These results suggest that overconfidence, as measured by Longholder, is correlated with lower ability but still reflects empirically distinct aspects of overconfidence.

We construct a novel measure of disclosure choice by firms. Our measure is computed using linguistic analysis of conference calls to identify whether a manager's response to an analyst question is a “non‐answer.” Using our measure, about 11% of analyst questions elicit non‐answers from managers, a rate that is stable over time and similar across industries. A useful feature of our measure is that it enables an examination of disclosure choice within a call. Analyst questions with a negative tone, greater uncertainty, greater complexity, or requests for greater detail are more likely to trigger non‐answers. We find performance‐related questions tend to be associated with non‐answers, and this association is weaker when performance news is favorable. We also find analyst questions about proprietary information are associated with non‐answers, and this association is stronger when firm competition is more intense.

We quantify the real implications of trade-offs between firm information disclosure and long-term investment efficiency. We estimate a dynamic equilibrium model in which firm managers confront realistic incentives to misreport earnings and distort their real investment choices. The model implies a socially optimal level of disclosure regulation that exceeds the estimated value. Counterfactual analysis reveals that eliminating earnings misreporting completely through disclosure regulation incentivizes managers to distort real investment. Lower earnings informativeness raises the cost of capital, which results in a 5.7% drop in average firm value, but more modest effects on social welfare and aggregate growth.

In this paper, we use accounting fundamentals to measure systematic risk of distress. Our main testable prediction—that this risk increases with the probability of recessionary failure, P(R|F)—is based on a stylized model that guides our empirical analyses. We first apply the lasso method to select accounting fundamentals that can be combined into P(R|F) estimates. We then use the obtained estimates in asset-pricing tests. This approach successfully extracts systematic risk information from accounting data—we document a significant positive premium associated with P(R|F) estimates. The premium covaries with the news about the business cycle and aggregate failure rates. Additional tests underscore the importance of the “structure” imposed through recessionary-failure-probability estimation. The “agnostic” return predictor that relies only on past correlations between the same fundamental variables and returns exhibits markedly different properties.