Predicting Fraud by Investment Managers

Predicting Fraud by Investment Managers

July 5, 2012 Research Insights, Corporate Governance
Print Friendly
(Last Updated On: January 24, 2017)

Predicting Fraud by Investment Managers

  • Stephen Dimmock and William Gerken
  • A recent version of the paper  can be found here.


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.

Data Sources:

This paper collects historical Form ADVs from the SEC’s website for all investment advisors from August 2001 to July 2006. Readers can access these forms via The authors also collect data on SEC administrative proceedings and litigation. These data can be accessed via



Bernie Madoff reminded everyone that money does not grow on trees. Looking back, it seems obvious that a perfect monthly return stream of 1% with no volatility is patently absurd and would raise red flags. And yet, very few investors were able to look past their own greed and question the credibility of Madoff. The natural question the authors in this paper address is “How can one predict potential fraud using publicly available information?”

Fund of funds and high-net worth individuals spent a lot of time and effort identifying which managers will succeed and which will fail. Another critical aspect of the due diligence process is to determine which managers have integrity and which do not. A simple look at characteristics on Form ADV suggest that a computer can help facilitate in the due diligence process.

Below are the variables the authors include in their prediction model to see what variables are related to fraud.

And here are the baseline results from their analysis. First, a description of the model:

Panel A of Table 3 shows the results of probit regressions that predict investment fraud using Form ADV disclosures. In column one, the sample is a cross-section of firms. The independent variables are taken from each firm’s Form ADV ling during the sample period; the dependent variable equals one if the firm commits fraud at any time between its first fi ling during the sample period and July 2007. This speci cation includes indicator variables for the year in which the firm first filed Form ADV. In this cross-sectional specifi cation, the z-scores are based on robust standard errors.

Here are the actual numbers (interesting and significant findings are highlighted in red):

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.


The results reflect a lot of common sense: stay away from managers with past problems and poor incentives, dodge firms that control assets and have an in-house broker, and look for firms that have large average account sizes.

Oh, and the biggest red flag? Managers who crank out amazing returns with no volatility on very large assets under management. Yikes!



Note: This site provides NO information on our value investing ETFs or our momentum investing ETFs. Please refer to this site.

Join thousands of other readers and subscribe to our blog.

Please remember that past performance is not an indicator of future results. Please read our full disclosures. The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. This material has been provided to you solely for information and educational purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed by the author and Alpha Architect to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. No part of this material may be reproduced in any form, or referred to in any other publication, without express written permission from Alpha Architect.

Definitions of common statistics used in our analysis are available here (towards the bottom)

About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes,, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.