Hedge Fund Due Diligence

1. Stephen Brown, NYU William Goetzmann, Yale Bing Liang, U Mass Christopher Schwarz, UC Irvine Hedge Fund Due Diligence

4. Previous Work: Lessons From Hedge Fund RegistrationBrown Goetzmann Liang and Schwarz • Is SEC registration useful? • What correlates to operational risk? • Can we predict operational event?

5. SEC Registration Form ADV • Form ADV Item 11: legal and regulatory problems. • A “Problem” fund = a fund whose management company answered ‘Yes’ to ANY question on Item 11 of Form ADV. • Past Suspension • Past Fine • Other Legal or Regulatory issue

6. 1526 358 Problem 1814 10295 Non-Problem Problem vs. Non-Problem Funds 100% 80% 60% 40% 20% 0% Hedge Funds All Advisers

7. Problems and External Conflicting Relationships **, * significant at one and five percent respectively Mandatory Disclosure and Operational Risk: Evidence from Hedge Fund Registration, Stephen Brown, William Goetzmann, Bing Laing, Christopher Schwartz, Not for reproduction or citation or quotation without authors’ permission

8. Problems and Internal Conflicts

9. 5 4.17 4.19 3.57 4 3.38 3 2.14 1.8 2 1.31 0.58 0.52 1 0.44 0 t-value -0.25 -1 -1.18 -1.21 -1.56 -1.56 -2 -2.15 -2.53 -3 -4 -5 -4.73 -6 Log(Assets) Fund Age Std Dev Onshore Incentive Fee High Water Relationship Direct Percent own (Years) Mark Domestic 75% Conflict of Interest and Future Performance Regression t-values. Blue = problem fund **, * significant at one and five percent respectively From Mandatory Disclosure and Operational Risk: Evidence from Hedge Fund Registration, Stephen Brown, William Goetzmann, Bing Laing, Christopher Schwartz, Draft: October 11, 2007. Not for reproduction or citation or quotation without authors’ permission

10. Predicting Problems • We need PAST ADV problems • Difficult to get from SEC • Instead, we can use observables • “Rotation” of Lipper-TASS variables and “Rotation” of ADV characteristics that are most highly correlated. • Canonical correlation. • Use this model with historical data.

11. The ω-Score: • A propensity score for “problem” • Uses TASS data (widely available) • As good as we can do without a research and due diligence team • A linear factor model • Does it predict fund disappearance? • Back-test

12. Operational Risk Measure Predicting Returns From Estimating Operational Risk For Hedge Funds, The Ω-Score, Stephen Brown, William Goetzmann, Bing Laing, Christopher Schwarz. Not for reproduction or citation or quotation without authors’ permission.

13. How About Fund Failure? • Is the half-life of the fund related to the omega score? • Other factors – age.

14. Operational risk and the half life of USD funds Half Life 0.7 90 months 0.6 80 0.5 70 Financial risk (prior monthly ) 0.4 0.3 60 0.2 50 0.1 40 0.8 0.4 0.6 1.0 1.2 Operational Risk -Score

15. Due Diligence • Market alternative to regulation • Not all funds register • Due diligence before investing

16. Current Study: Private Sector Data • 445 DD funds over past decade • DD decision endogenous • Past returns. • Different thresholds for problems based on returns. • Focus on honesty

17. Why DD gets Done: Logit

18. Returns Before and After DD

19. Flows Before and After DD

20. Conditional Flow Response

21. Problem Fund Probit

22. Misrepresentation Probit

23. Fund Flows and Operational Risk

24. Performance and Operational Risk

25. Conclusion • Hedge fund opacity demands trust • Problem funds have: • Verification problems • Minor auditors • More likely to self-price