A Simple Approach to Better Distinguish Real Earnings Manipulation from Strategy Changes

• 成果类型: Article
• 署名作者: Christensen, Theodore E.; Huffman, Adrienna; Lewis-Western, Melissa F.; Valentine, Kristen
• 署名单位: University System of Georgia; University of Georgia; The Brattle Group; Brigham Young University
• 刊物名称: CONTEMPORARY ACCOUNTING RESEARCH
• ISSN/ISSBN: 0823-9150
• DOI: 10.1111/1911-3846.12830
• 发表日期: 2023
• 页码: 406-450
• 关键词: CORPORATE GOVERNANCE; cash flow; management; accruals; COMPENSATION; QUALITY; RESTATEMENTS; contagion; ability; errors

摘要

Researchers typically infer real earnings management when a firm's operating and investing activities differ from industry norms. A significant problem with classifying deviations from industry averages as myopic earnings management is that companies can change their operating and investing decisions for strategic business reasons rather than to mislead stakeholders. Using principal components analysis, we systematically evaluate existing measures and develop a comprehensive real activities measure to better capture earnings manipulation. Our measure reflects (i) deviations from industry averages across multiple activities and (ii) other signals of manipulation. This approach is promising because, although there are many sources of abnormal activities, manipulation is more likely the cause when managers engage in multiple income-increasing abnormal activities that coincide with other signals that indicate an elevated risk of manipulation. This simple approach results in a metric that associates negatively with future operating performance and earnings persistence, yields high-power tests, and captures manipulation reasonably well across most life-cycle stages. Importantly, this approach performs better than the standard real earnings management metrics across all dimensions. Specifically, it generates the expected reduction in future earnings and reduced earnings persistence in 82% of the tests compared to 36% and 46% in common alternatives. Also, because this innovation does not require a long time-series or rely on future period realizations for classification, it can be useful in more research settings than other recent innovations in the literature.