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ItemReal Earnings Management and the Strategic Release of New Products( 2022)Prior studies on real earnings management (REM) mainly focus on the estimation of abnormal operating and investing activities using Compustat data at the firm level. We extend this literature by providing micro-level evidence regarding how financial reporting pressures influence new product release decisions, i.e., product-level REM. Specifically, we compare how public and private studios time the release of their movies differently. We find that, facing pressure to boost quarterly revenues and earnings, public studios are more likely to release movies with high expected revenues in the third month of a quarter than private studios. To corroborate our findings, we examine variations in movie release patterns within public studios. We find that among public studios, when their recent past performance is poor, they are more likely to release movies with high expected box office revenues in the third month of a quarter. Furthermore, among movies with high expected revenues, those movies in genres with a more targeted release window (e.g., romance movies and horror movies), and those with directors who have worked with the studio in the past, are less likely to be released in the third month of a quarter. This result suggests that studios choose the least costly path to achieve financial reporting goals. One negative consequence of this financial reporting motivated product release strategy is that movies released in the third month of a quarter have lower international box office revenues. Taken together, these results provide evidence of the existence and consequences of product-level real earnings management.
ItemAI Readership and Financial Reporting( 2022)Investors and regulators have used artificial intelligence and machine learning techniques in financial statement analysis. This study explores managers’ reactions to AI readership of financial statements and the evolution of accounting stewardship in the age of new technologies. We document that AI readership reduces financial misreporting that is sensitive to machine detection, but we find no evidence that AI readership is related to human-sensitive misreporting. The results suggest a disciplinary role of AI readership in financial reporting, and managers cater to AI readership by only changing the machine-sensitive part of financial reporting quality. This relationship is stronger when firms have higher transient institutional investor ownerships, in highly litigated industries, and in the presence of peer restatements.