We examine changes in CEOs' disclosure styles in quarterly earnings conference calls over their tenure. Our longitudinal analysis of newly hired CEOs shows that CEOs' forward-looking disclosures and their disclosures' relative optimism decline in their tenure. Further, externally hired and inexperienced CEOs are more future-oriented, and younger CEOs exhibit greater optimism in their disclosures. We also find that non-CEO executives' disclosure styles remain time-invariant over their CEOs' tenure. Our evidence is consistent with uncertainty reduction about managers' ability over their tenure (1) reducing the demand for and the supply of forward-looking disclosures, and (2) attenuating managerial career concerns leading to the decline in disclosure optimism. JEL Classifications: D22; D70; D82; D83; L20; M12.

Natural language is a key form of business communication. Textual analysis is the application of natural language processing (NLP) to textual data for automated information extraction or measurement. We survey publications in top accounting journals and describe the trend and current state of textual analysis in accounting. We organize available NLP methods in a unified framework. Accounting researchers have often used textual analysis to measure disclosure sentiment, readability, and disclosure quantity; to compare disclosures to determine similarities or differences; to identify forward‐looking information; and to detect themes. For each of these tasks, we explain the conventional approach and newer approaches, which are based on machine learning, especially deep learning. We discuss how to establish the construct validity of text‐based measures and the typical decisions researchers face in implementing NLP models. Finally, we discuss opportunities for future research. We conclude that (i) textual analysis has grown as an important research method and (ii) accounting researchers should increase their knowledge and use of machine learning, especially deep learning, for textual analysis.

We develop a dictionary of linguistic extremity in earnings conference calls, a setting where managers have considerable latitude in the language they use, to study the role of extreme language in corporate reporting. Controlling for tone (positive versus negative) of language, we document that when managers use more extreme words in earnings conference calls, trading volume around the call increases and stock prices react more strongly. In addition, both effects are more pronounced for firms with weaker information environments. Linguistic extremity also affects analyst opinions and contains information about a firm's future operating performance. As such, our results provide evidence that markets are influenced not just by what managers say, but also how they say it, with extreme language playing an important role in communicating reality and not merely reflecting hyperbole.

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We study the information content and determinants associated with voluntary management disclosures of going concern (GC) uncertainties by IPO issuers. In terms of information content, we examine IPO price revision and initial return and find robust support that management GC disclosures are associated with downward revisions in the IPO offer price and, upon considering the mediating effects of the price revision, also associated with lower initial returns. In terms of determinants, and after controlling for other factors (e.g., issuer distress, start-up status, size, cash burn), we find that the presence of a management GC disclosure is negatively associated with a proxy for issuer financial incentives to withhold “bad news” and positively associated with the extent of risk factors disclosure. Overall, our results provide support for the information content of voluntary management disclosures of GC uncertainties by IPO issuers, the presence of which is associated with agency and risk motivations. JEL Classifications: G24; G32; M13; M41.