Fraud Power Laws

• 成果类型: Article
• 署名作者: Cheynel, Edwige; Cianciaruso, Davide; Zhou, Frank S.
• 署名单位: Washington University (WUSTL); New Economic School; University of Pennsylvania
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456
• DOI: 10.1111/1475-679X.12520
• 发表日期: 2024
• 页码: 833-876
• 关键词: earnings management; Slippery slope; QUALITY; conservatism; CONSEQUENCES; RESTATEMENTS; contagion

摘要

Using misstatement data, we find that the distribution of detected fraud features a heavy tail. We propose a theoretical mechanism that explains such a relatively high frequency of extreme frauds. In our dynamic model, a manager manipulates earnings for personal gain. A monitor of uncertain quality can detect fraud and punish the manager. As the monitor fails to detect fraud, the manager's posterior belief about the monitor's effectiveness decreases. Over time, the manager's learning leads to a slippery slope, in which the size of frauds grows steeply, and to a power law for detected fraud. Empirical analyses corroborate the slippery slope and the learning channel. As a policy implication, we establish that a higher detection intensity can increase fraud by enabling the manager to identify an ineffective monitor more quickly. Further, nondetection of frauds below a materiality threshold, paired with a sufficiently steep punishment scheme, can prevent large frauds.