Predicting fraud by investment managers

• Main Authors: Dimmock, Stephen G., Gerken, William Christopher
• Other Authors: Nanyang Business School
• Format: Article
• Language: English
• Published: 2013
• Subjects: DRNTU::Business::Finance::Investments
• Online Access: https://hdl.handle.net/10356/100279; http://hdl.handle.net/10220/17824
• Institution: Nanyang Technological University

We test the predictability of investment fraud using a panel of mandatory disclosures filed with the SEC. We find that disclosures related to past regulatory and legal violations, conflicts of interest, and monitoring have significant power to predict fraud. Avoiding the 5% of firms with the highest ex ante predicted fraud risk would allow an investor to avoid 29% of fraud cases and over 40% of the total dollar losses from fraud. We find no evidence that investors receive compensation for fraud risk through superior performance or lower fees. We examine the barriers to implementing fraud prediction models and suggest changes to the SEC's data access policies that could benefit investors.