Study Supports SEC Approach to Detecting Hedge Fund Fraud

Vanderbilt B-school research shows risk-based performance flags allow for accurate, cost-effective financial regulation

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May 13, 2010

NASHVILLE – New research from the Vanderbilt Owen Graduate School of Management demonstrates that risk-based performance flags can accurately pre-screen hedge funds for fraud. The findings support strategies currently in use at the Securities and Exchange Commission (SEC), thereby contesting arguments often posed by opponents of additional regulation that government agencies do not have adequate resources to avert financial market scandals.

The SEC added risk-based “examinations” to its regulatory procedures shortly after Bernard Madoff was charged with perpetrating a massive Ponzi scheme. These examinations – similar to the performance flags analyzed in the study – are part of a series of reforms aimed at detecting hedge fund fraud early, thereby reducing the chances that such frauds will occur or go undetected and lead to the type of financial damage seen recently.

According to the study’s author, Nicolas P. B. Bollen, professor of finance at the Owen School, the performance flags – low-cost, statistical tools – allow regulators to successfully identify high-risk hedge funds that can then be subjected to more intensive investigation. The alternative to the pre-screening approach is to examine all hedge funds using the same in-depth regimen. Given the large number of hedge funds and the very rapid pace of change in financial markets, this is at best a challenging task for regulatory agencies with limited professional and financial resources.  
“The approach we’re validating for hedge fund monitoring is in some ways similar to the one used by the IRS to determine which tax returns to audit,” said Bollen. “By statistically parsing through funds and identifying ‘red flags,’ we demonstrate financial regulation can work without being prohibitively expensive.

Bollen also noted that the performance flag approach has application beyond hedge funds. “Pre-screening for fraud can be applied efficiently to deter fraud in a wide range of investments. The flags might be different but the basic strategy is the same,” he said. “And the information we are providing can also benefit investment advisers by making them aware that pre-screening can be a very effective way to protect client portfolios. They will have additional means to identify potential investments that should require especially careful due diligence.”

Methodology at Work

To unearth the findings, the research team reviewed 8,770 existing and defunct hedge funds in the Lipper TASS and Center for International Securities and Derivatives Markets databases between 1994 and 2008. A sample of 195 “problem” funds was identified that had been the subject of SEC enforcement actions or investor lawsuits. The researchers then compared the problem and non-problem funds using performance flags that had been developed prior by Bollen. These flags focused on suspicious patterns in hedge fund returns, including random returns, too few negative returns and too many repeat returns. The team found that funds charged with reporting violations triggered the performance flags at a substantially higher rate than other funds. For example, 51 percent of these funds had random returns compared to just 23 percent for non-problem funds.

Bollen noted that the critical role of databases in identifying potential fraud underscores the importance of requiring hedge funds to disclose key information to designated databases, such as those highlighted above. This is currently a voluntary procedure.  

“Mandatory reporting can only serve to aid regulatory agencies working to root out fraud,” said Bollen. “The increased data would also promote additional research that could protect investors from future schemes.”

Professor Bollen co-authored the study, “Predicting Hedge Fund Fraud with Performance Flags,” with Veronika K. Pool at the Indiana University Kelley School of Business. The study is available for download here.

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