finding bernie madoff: detecting fraud by investment managers stephen g. dimmock and william c....
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Finding Bernie Madoff: Detecting Fraud by Investment Managers
Stephen G. Dimmock and William C. Gerken
Fraud
On December 11, 2008 Bernie Madoff was charged with a $65 billion investment fraud.
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
The Questions
We test if fraud is ex ante predictable. If so, what predicts fraud?
Is it possible to improve the disclosure requirements mandated by the SEC?
Are there real economic consequences to fraud?
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Our Study
The main disclosure requirement in U.S. securities law is that investment advisors with more than $25 million in assets must file Form ADV with the SEC.
We use a panel of all ADV filings from 2000-2006.
13,579 distinct investment managersOver 20 million investorsMore than $32 trillion in assets under management
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Data
Panel of all Form ADV filings from 2000 through 2006. We also have disclosure reporting pages (DRP) which list all criminal violations in detail through 2007.
Current forms are available at: http://www.adviserinfo.sec.gov
Firms must file annually or in the event of a material change.
Measure variables as of August 1st and DRP filings over subsequent year
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Data: Filings and Removal
Aug-03 Aug-05Nov-03 Feb-04 May-04 Aug-04 Nov-04 Feb-05 May-05
1-Apr-04File AnnualForm ADV
15-Jan-05File Amended Form ADV and DRP
Disclosing Employee Conviction
18-Feb-05File Amended Form ADV and
DRP after Firing Employee
24-Mar-05File New Form ADV: No Crime Disclosed
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Internal Policies and Fraud
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
All No DRP ≥ 1 DRP Difference
Interest in Client Transaction 31.6% 30.9% 74.6% 43.7%***
Soft Dollars 58.9% 58.1% 74.1% 16.0%***
Custody of Assets 26.1% 25.6% 64.6% 39.0%***
Broker/Dealer 40.5% 39.0% 85.7% 46.7%***
Other Affiliation 56.1% 54.3% 92.1% 37.8%***
Small Client Focus 23.9% 22.7% 42.9% 20.1%***
Separate CCO 16.0% 14.6% 31.2% 16.6%***
History of Violations 16.4% 16.2% 84.5% 68.3%***
Predicting Fraud: Table 4 Part 1
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
All Fraud All Fraud Felonies Rand Eff Neg Bin
History of Violations 0.416*** 0.461*** 1.438***
[3.29] [3.20] [3.58]
History of Crime 0.757*** 0.764***
[5.62] [5.24]
History of Reg. 0.379*** 0.453***
[2.94] [3.15]
History of Civil 0.022 0.053
[0.17] [0.36]
Predicting Fraud: Table 4 Part 2
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Interest in Transactions 0.263** 0.239** 0.153 0.285* 0.693*
[2.29] [2.03] [1.19] [1.79] [1.89]
Soft Dollars 0.293** 0.259** 0.247** 0.307** 1.15***
[2.45] [2.28] [2.03] [2.15] [3.02]
Broker/Dealer 0.392** 0.394** 0.268* 0.454** 1.46***
[2.55] [2.56] [1.92] [2.14] [2.67]
Log(Account Size) -0.09*** -0.08*** -0.07*** -0.10*** -0.21***
[3.84] [3.43] [2.91] [3.58] [2.91]
Robustness
Fraud and number of employees is highly correlated. We control for this but want to be sure this does not inadvertently drive our results.
We estimate a placebo model, where the dependent variable equals one if the firm reports a non-investment crime such as drunk driving.
Also, we split the sample into small and large firms.
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Predicting Fraud: Tradeoff
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
73.3% identified at 5% false positive rate
59.3% identified at 1% false positive rate
33.2% identified at 0.2% false positive rate
Alpha and Fees
Using data from PSN (institutional funds) and CRSP MF (mutual funds), we determine if fraud risk is compensated.
No relation between fraud risk and alpha
No relation between fraud risk and fees
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Hidden Information
Firms are required to disclose crimes and regulator violations for 10 years, unless the offender leaves the firm.
If the offender leaves, the violation disappears.
Many violations disappear without explanation.
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Predicting Fraud with Hidden Information
Can removed information predict fraud?
Can information that is difficult to observe due to the format of Form ADV predict fraud?
Include the same controls as in Table 4, but do not show them in the interest of brevity.
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Predicting Fraud with Hidden Information
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
History of Investment Crime 0.687*
[1.76]
Number of Investment Crimes 0.027**
[2.54]
Removed DRP 0.433**
[2.09]
Unexplained Missing DRP 0.029**
[2.10]
Repeat Crime 0.80***
[3.87]
History of Crime 0.176 0.57*** 0.59*** 0.62*** 0.61***
[0.46] [3.96] [4.11] [4.01] [4.94]
Predicting Fraud with Hidden Information
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
False Positive RateNo Hidden
InfoRemoved
UnexplainedMissing DRP
RepeatCrimes
0.05% 13.4% 19.8% 21.7% 22.1%
0.10% 17.4% 30.2% 34.8% 28.5%
0.20% 27.9% 39.5% 40.6% 38.4%
0.50% 46.5% 50.0% 50.7% 53.3%
1% 59.3% 61.6% 60.9% 61.6%
5% 73.3% 73.3% 72.5% 73.3%
10% 88.4% 87.2% 88.4% 86.0%
20% 91.9% 94.2% 97.1% 94.2%
Consequences: Firm Death
Does fraud kill firms?
Estimate a survival hazard model of firms dying in the next year
Report hazard ratios – show the relative probability of firm death compared to other firms
Include controls used in previous regressions
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Consequences: Firm Death
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
New DRP Last Year 2.454**
[2.02]
First Crime 5.490*** 5.323***
[3.28] [3.24]
Repeat Crime 1.177 0.546
[0.23] [-0.64]
Removed DRP 1.024
[0.82]
Unexplained Missing DRP 1.052***
[3.47]
New Felony 3.481***
[2.71]
Consequences: Flows
Do investors withdraw their money following the disclosure of fraud?
Estimate panel regressions with firm fixed-effects and controls for: returns, portfolio value, assets under management, firm age, # of employees, and time fixed-effects
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Consequences: Contemporaneous Flows
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
New DRP Last Year -0.29
[0.33]
First Crime -0.315** -0.317**
[1.98] [2.00]
Repeat Crime 0.090 0.083
[0.95] [0.87]
Removed DRP 0.003
[1.25]
Unexplained Missing DRP 0.004
[1.34]
New Felony 0.027
[0.31]
Conclusion
Fraud is predictablePredict 73.3% of frauds with public information
Conflicts of interest and history of violations
Improve predictions using hidden information for high fraud risk firms
Investors react to fraudTransparent disclosure: 549% increase in firm
death, 32% outflowsNon-transparent disclosure: No Effect
Conclusion
Four simple changes would improve investor welfare:
1. Report the number of past violations
2. Disclose investment and non-investment crimes separately
3. Force disclosure of violations in the past year even if removed
4. Require firms to disclose the number of violations removed before 10 years has passed
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
Conclusion
Since the SEC has this hidden information on record, and firms are required to report it, the marginal cost of disclosing this information to investors is essentially zero.
Disclosing this information would allow investors to avoid frauds and likely increase the market penalty for fraud.
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion
What happens if investors use our results?
Introduction Data Prediction Alpha and Fees Hidden Info Reaction Conclusion