Researcher ORCID Identifier

Stephen G. Dimmock:

Joseph D. Farizo:

William C. Gerken:

Dataset Creation Date


Release Date



University of Kentucky Libraries


We document the prevalence and variety of frauds committed by investment managers. We show that prior legal and regulatory violations, conflicts-of-interest, and monitoring disclosures available via the Security and Exchange Commission’s Form ADV are useful for predicting fraud. Additional tests show that fraud by rogue employees is more predictable than firm-wide fraud, but both types of fraud are significantly predictable. We revisit the fraud prediction model of Dimmock and Gerken (2012) and test its performance out-of-sample (using fraud cases discovered since that article’s publication). We find the model has significant predictive power for the out-of-sample cases. To encourage additional research in this area, we have made the data used in this chapter publicly available here.

Digital Object Identifier (DOI)


© 2018 Dimmock, Farizo, and Gerken

This dataset is distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided that the dataset creators and source are credited, that changes (if any) are clearly indicated, and that the derivative work is distributed under the same license.

Supporting Information

A list of variables with descriptions is available as the additional file noted below.

File Format

Dataset: DTA file (.dta)

List of variables: Microsoft Excel worksheet (.xlsx)

File Size

Dataset: 630 MB

List of variables: 31 KB

Related Publications

Dimmock, Stephen G., and William C. Gerken. “Predicting Fraud by Investment Managers.” Journal of Financial Economics 105, No. 1 (2012): 153-173.

“Misconduct and Fraud by Investment Managers" by Stephen G. Dimmock, Joseph D. Farizo, and William C. Gerken in the forthcoming Wiley handbook “Corruption and Fraud in Financial Markets: Malpractice, Misconduct and Manipulation” edited by Carol Alexander and Doug Cumming


Dataset creators' personal profiles:

Variable-Descriptions.xlsx (31 kB)
List of variables