Firm disclosures often reach only a portion of investors, which results in information asymmetry among investors and, therefore, lower market liquidity. This issue is particularly salient for firms that are not highly visible, as they tend not to receive broad news dissemination via traditional intermediaries, such as the press. This paper examines whether firms can reduce information asymmetry by more broadly disseminating their news. To isolate the impact of dissemination, we focus our analysis on firms' use of Twitter and exploit the 140-character message restriction. Specifically, using a sample of technology firms, we examine the impact of using Twitter to send market participants links to press releases that are provided via traditional disclosure methods. We find this additional dissemination of firm-initiated news via Twitter is associated with lower abnormal bid-ask spreads and greater abnormal depths, consistent with a reduction in information asymmetry. Moreover, this result holds mainly for firms that are not highly visible, consistent with them being in greater need of this additional dissemination channel. We also examine the impact of dissemination on a volume-based measure of liquidity, and find that dissemination is positively associated with liquidity. Data Availability: All data are publicly available from the sources indicated in the paper.

We examine firm disclosure choice when information is received on a real-time, continuous basis. We use transaction-level credit and debit card sales for a sample of retail firms to construct a weekly measure of abnormal revenue for each firm. We validate the informativeness of this abnormal real-time revenue information, confirming its positive correlation with abnormal returns, unexpected revenue realizations, and management revenue forecast news. Using revenue forecasts, we find that firms are less likely to disclose abnormally negative news early in the quarter. As the quarter progresses, firms reduce their withholding of negative news. These results are consistent with impending earnings announcements disciplining managers to provide negative news. This pattern of initial withholding and then disclosure exists primarily in firms with high analyst coverage, high institutional ownership, or high litigation risk. Finally, we find increased insider stock sales in weeks with abnormally negative news and no firm disclosure. Overall, our study provides evidence of the informativeness of real-time information and manager discretion in its release.

Many have argued that financial statements created under an accounting model that measures financial instruments at fair value would not fairly represent a bank's business model. In this study we examine whether financial statements using fair values for financial instruments better describe banks' credit risk than less fair-value-based financial statements. Specifically, we assess the extent to which various leverage ratios, which are calculated using financial instruments measured along a fair value continuum, are associated with various measures of credit risk. Our leverage ratios include financial instruments measured at (1) fair value; (2) U.S. GAAP mixed-attribute values; and (3) Tier 1 regulatory capital values. The credit risk measures we consider are bond yield spreads and future bank failure. We find that leverage measured using the fair values of financial instruments explains significantly more variation in bond yield spreads and bank failure than the other less fair-value-based leverage ratios in both univariate and multivariate analyses. We also find that the fair value of loans and deposits appear to be the primary sources of incremental explanatory power. Data Availability: All data are publicly available from the sources indicated in the paper.

This paper asks about the existence, determinants, and implications of toxic content in financial social media. Using machine learning algorithms to measure toxicity in Seeking Alpha articles and comments, we find measurable toxicity concentrated primarily in user-generated comments rather than articles. Comment toxicity is greater for firms with more investor attention or uncertainty. We document three key results about toxicity in capital markets. First, toxicity displays a feedback loop in platform participation: past toxicity is associated with more future toxic contributors for a given firm. Second, firms receiving more toxic comments have greater retail trading volume but less informative retail trades, especially when comments disagree. Third, toxicity is associated with slower price discovery around earnings announcements. Our findings suggest that financial social media toxicity relates to both user behavior and market outcomes and raise important considerations for platform governance in financial markets. Data Availability: The Seeking Alpha articles and comments used in this study were obtained directly from Seeking Alpha under a data-sharing arrangement and are proprietary; we are not permitted to redistribute them. These data may be requested from Seeking Alpha. Compustat, Execucomp, CRSP, and New York Stock Exchange (NYSE) Trade and Quote (TAQ) data are available through Wharton Research Data Services (WRDS) to subscribing institutions.

We examine firm disclosure choice during the initial public offering (IPO) roadshow presentation to understand the informativeness of a management presentation designed to attract investors. Although firms submit a comprehensive registration filing during the IPO, managers also prepare a roadshow presentation, which is shorter and typically allows managers more autonomy to select the information released and how it is discussed. We find that IPO roadshows have significantly more positive, less negative, and less uncertain language than the SEC filing. Using machine learning to classify roadshow sentences into five major topics from the registration statement, we find that roadshows differ in both the topics selected and the language used within each topic. We then examine the predictive ability of the roadshow language, finding that roadshow language predicts future accounting performance, whereas filing language does not. These results highlight the informational role of management presentations, despite the flexibility they grant managers.

Business complexity involves important tradeoffs for managers and investors, but empirical evidence is limited by measurement issues. We construct and validate a measure of business complexity using a GPT model fine-tuned on narrative disclosures and inline XBRL tags. We first show that our measure is associated with slower price formation in capital markets, consistent with complexity increasing processing costs. Next, we apply our measure to study the complexity of debt, an economically important topic that encompasses a wide range of features. The results show that nonstandard debt features such as call and convertibility provisions underlie debt complexity. We also find that debt complexity correlates with more persistent interest expense and better performance when lending conditions worsen, suggesting it is in part an adaptive response to manage financial risk. Overall, our study underscores the tradeoffs of business complexity and provides a flexible measure of complexity for future research. Data Availability: Contact authors for data, model weights, and measure.