This two-part study begins with a rhetorical analysis of the genre of earnings press releases. Then, a quantitative analysis uses capital markets data to assess the investor impact of tone and other stylistic attributes. The genre analysis explores the regulatory context, structural attributes, and dual informational-promotional role of earnings press releases, using individual releases as illustrations. The quantitative analysis explores the relation between the stock market reaction to earnings press releases and quantitative measures of style developed using elementary computer-based content analysis of a corpus of releases. Results suggest that tone influences investors' reactions. An explanation for this result is provided by prospect theory, which predicts that framing financial performance in positive terms causes investors to think about the results in terms of increases relative to reference points. Results also suggest that longer press releases reduce the market impact of unexpected earnings.

This study evaluates alternative measures of the tone of financial narrative. We present evidence that word-frequency tone measures based on domain-specific wordlists—compared to general wordlists—better predict the market reaction to earnings announcements, have greater statistical power in short-window event studies, and exhibit more economically consistent post-announcement drift. Further, inverse document frequency weighting, advocated in Loughran and McDonald (2011), provides little improvement to the alternative approach of equal weighting. We also provide evidence that word-frequency tone measures are as powerful as the Naïve Bayesian machine-learning tone measure from Li (2010) in a regression of future earnings on MD&A tone. Overall, although more complex techniques are potentially advantageous in certain contexts, equal-weighted, domain-specific, word-frequency tone measures are generally just as powerful in the context of financial disclosure and capital markets. Such measures are also more intuitive, easier to implement, and, importantly, far more amenable to replication.

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Similar to a classic-event study, this study examines market reaction to firmsa' earnings announcements. This study extends the examination to include a broad range of concurrent disclosure contained in earnings press releases: financial disclosure captured as accounting ratios; and verbal components of disclosure, both content and style, which are captured using elementary computer-based content analysis. Extending the analysis to such a broad range of concurrent disclosures requires a methodology designed to utilize a large number of predictor variables, and predictive data mining algorithms are specifically designed to do so. Therefore, this study employs a widely used data-mining algorithm—classification and regression trees (CART). Results of the study show that inclusion of predictor variables capturing verbal content and writing style of earnings-press releases results in more accurate predictions of market response.

We examine research relevant to auditing related party transactions to contribute to the PCAOB project on this topic and to provide other policy makers, auditors, and academics with an overview of relevant literature. Specifically, we report on the challenges associated with the identification, examination, and disclosure of related party transactions. Additionally, we address issues and research evidence related to nondisclosure and reliance on management assertions, risk assessment, materiality, fraud detection, the effect of related party transactions on corporate governance, and international auditing issues. Overall, we believe that the findings in academic research and the significance of related party transactions in recent prominent fraud cases are consistent with the PCAOB's reconsideration of auditing of related party transactions. We conclude with implications for further research.

International Financial Reporting Standards (IFRS) allow managers flexibility in classifying interest paid, interest received, and dividends received within operating, investing, or financing activities within the statement of cash flows. In contrast, U.S. Generally Accepted Accounting Principles (GAAP) requires these items to be classified as operating cash flows (OCF). Studying IFRS-reporting firms in 13 European countries, we document firms' cash-flow classification choices vary, with about 76, 60, and 57% of our sample classifying interest paid, interest received, and dividends received, respectively, in OCF. Reported OCF under IFRS tends to exceed what would be reported under U.S. GAAP. We find the main determinants of OCF-enhancing classification choices are capital market incentives and other firm characteristics, including greater likelihood of financial distress, higher leverage, and accessing equity markets more frequently. In analyzing the consequences of reporting flexibility, we find some evidence that the market's assessment of the persistence of Rev Account Stud (2017)