We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic ) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10‐K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10‐K filing amendments. Our out‐of‐sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.

Technological advances are creating a shift in the information disclosure environment allowing more investors to interact with management. We examine three key levels of trader‐management interaction to assess the accuracy of traders' market‐tested value estimates and resulting market price. These data require an engaging experiment and a complex, contextually rich asset, which we create by playing a popular gaming app before the experiment. Participants view financial information, ask management questions, estimate value, and trade. We find that receiving non‐personalized question responses improves trader estimates of value and market price efficiency relative to when traders ask questions but do not expect a response. This occurs because traders exert more effort estimating value and trading. However, receiving personalized versus non‐personalized responses harms value estimates and market efficiency. This occurs because traders receiving personalized responses fixate on the interaction with management, dividing their attention and diverting it away from valuing and trading the asset.

We present new theory and experimental findings indicating that investors ascribe value to firms that use higher financial reporting quality (FRQ), controlling for the influence of higher FRQ on their estimates of these firms' fundamental value. To guide our investigation, we draw on the cooperation literature in accounting, finance, and psychology. We identify expanded audit reports, particularly auditor commentary, as a mechanism that credibly communicates whether a firm uses higher FRQ. Auditor commentary increases investors' willingness to pay (WTP) more for shares of a firm using higher FRQ than a competing firm using lower FRQ. We also provide process evidence that investors perceive higher FRQ as cooperative behavior by measuring their affective responses and cognitive beliefs, which mediate the influence of audit commentary on investors' increased WTP for higher FRQ. A second experiment bolsters the link between investors' affective and cognitive responses to a firm's FRQ and perceived cooperative behavior.