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A Law and Finance Analysis of Hedge Funds

Douglas Cumming Associate Professor Ontario Research Chair. University of Cambridge, EFMA April 2009. A Law and Finance Analysis of Hedge Funds. Schulich School of Business York University, Toronto, Canada. Motivation. Hedge funds enjoy scant regulation

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A Law and Finance Analysis of Hedge Funds

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  1. Douglas Cumming Associate Professor Ontario Research Chair University of Cambridge, EFMA April 2009 A Law and Finance Analysis of Hedge Funds Schulich School of Business York University, Toronto, Canada

  2. Motivation Hedge funds enjoy scant regulation US hedge fund market > $1 trillion under management Many funds promising α>5% This implies the existence of >$50 billion in returns in aggregate Not likely! Many disappointed investors in future This has caught the attention of regulators (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  3. Research Questions Does hedge fund regulation influence hedge fund… Performance Alpha Manipulation-Proof Performance Measures Raw return Risk Standard deviation of returns Compensation Structure Fixed Fees Performance Fees (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  4. Prior Research Hedge fund structure and performance Ackermann et al. (1999); Agarwal and Naik (2000a,b, 2004); Agarwal et al. (2006); Amin and Kat (2003); Baqueroet al. (2005); Brown et al. (1999, 2001); Brown and Goetzmann (2003); Brunnermeier and Nagel (2004); Cremers et al. (2005); Edwards and Caglayan (2001); Getmansky (2005); Getmansky et al. (2004); Liang (1999, 2000, 2003); Gupta and Liang (2005) Hedge fund activism and performance Brav, Jiang, Partnoy and Thomas (2007) Hedge fund share restrictions; offshore funds ; registration Aragon (2007); Liang and Park (2007); Brown et al. (2006) (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  5. New Contributions A first look at how hedge fund specific regulations influences performance and risk A first look at how hedge fund regulation influences compensation structure: fixed fees, performance fees (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  6. Primary Findings Hedge fund regulations minimum capitalization restrictions on the location of key service providers wrapper distributions associated with bad performance lower fund alphas lower manipulation-proof performance measures lower average returns associated with lower risk lower standard deviation associated with inefficient fund compensation lower performance fees higher management fees (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  7. (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions How are hedge funds regulated? 24 countries considered Source: PWC

  8. Why might hedge fund regulation help? E.g. 2 funds managed by the same group of fund managers Fund 1: shorting the Standard and Poor’s 500 Index (“S&P’) + mumbo jumbo Fund 2: long on the S&P, + mumbo jumbo Half of the investors of these two hedge funds will lose The hedge fund managers reap the profits of the fixed management fees of both hedge funds and performance fees on one fund Fund investors’ remain unaware due to mumbo jumbo of marketing and promotional material of the hedge funds No regulatory oversight such as registration  regulatory authorities unaware. Distribution channel restrictions, fund registration, and limits of location of key service providers would curb against this type of behaviour and thereby improve hedge fund structure and average performance (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  9. Why might hedge fund regulation Not help? Hedge fund managers and their investors lose freedom to contract and organize their resources in the way that they deem to be most efficient. Minimum hedge fund size, restrictions on the location of key service providers, and limitations on the main market channels for hedge fund distribution could: Constrain the fund to an inefficient scale Give rise to inefficient choice of human resources associated with fund management, Limit investor participation most suited to the particular hedge fund’s strategy. Create barriers to entry If so, this implies worse hedge fund performance and less efficient hedge fund structures in terms of higher management fees and lower performance fees. (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  10. (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions Ambiguous Predictions… Hence the need to examine Data! Data from 24 countries in this paper…

  11. Data: Description HedgeFund.Net (“HFN”) and CISDM 2137 hedge funds from the 24 countries, Jan 2003-Dec 2005 (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  12. Data: Potential Biases Selection bias is possible without the universe of hedge funds consider robustness of the results to excluding different countries, such as the US, from our regression analyses Survivorship bias and instant history bias our analyses focus on a relatively short window of time, namely 2003 to 2005, in which no extreme market events for which we would expect systemic bias in the data. We have also considered the robustness of the results to different periods with longer histories, and the results are quite robust (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  13. (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions Multivariate Analyses Data from 23 countries in this paper…

  14. Analyses of Performance Outcomes Table 4: Manipulation Proof Performance Measure Table 5: Alphas of Multifactor Model Table 6: Average Monthly Returns Table 7: Standard Deviations of Returns Cross sectional Regressions… raises issues discussed in next slide… (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  15. Issues / Robustness Models used to generate alphas and manipulation proof performance measures [robustness to alternatives] Collinearity [robustness to exclusion of certain variables and countries] Endogeneity / location choice [robustness to exclusion of countries, two-step models] Comparability of fund types [controls for fund strategies; robustness to kicking out certain types of funds] (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  16. Present Model 1, 2 or 557?

  17. Key Items in Table 4: Manipulation Proof Performance Measure Sharpe Ratio and other reward-to-risk measures may be manipulated with option-like strategies (Goetzmann et al., 2002) [commonplace among hedge funds] Manipulation-Proof Performance Measure [Goetzmann et al. (2006)]: where rft and xt is the per-period (not annualized) risk free rate and the excess return of the fund over period t. The parameter ρ is the relative risk aversion; historically this number ranges from 2 to 4 for the CRSP value-weighted market portfolio depending on the time and frequency of data used. can be interpreted as the annualized continuously-compounded excess return of the portfolio. MPPM = average per period welfare of a power utility investor in the portfolio over the time period in question. Results robust for three different risk aversions: 2, 3 and 4. Report with value 3. (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  18. Key Items in Table 4: Manipulation Proof Performance Measure MPPMs are lower when: Restrictions on the location of key service providers have lower MPPMs by 5.811 (Model 1) to 8.128(Model 3) Via wrapper have lower MPPMs by 3.808 (Model 1) to 15.228 (Model 4) MPPMs higher when: Yearly Capital Redemption Applied by 2.650 – 4.666 (Models 1,5) (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  19. Key Items in Table 5: Fund Alphas Multifactor Model (Fung and Hsieh, 2004) Seven factors are considered in the model S&P 500 return minus risk-free rate (SNPMRF), Wilshire small cap minus large cap return (SCMLC) change in the constant maturity yield of the 10-year Treasury (BD10RET) change in the spread of Moody's Baa minus the 10-year Treasury (BAAMTSY) bond PTFS (PTFSBD) currency PTFS (PTFSFX) commodities PTFS (PTFSCOM) Issues about the alpha, A JOINT TEST! (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  20. Key Items in Table 5: Fund Alphas Fund alphas lower when: Restrictions on location of key service providers show lower alphas by at least 0.64% (Model 7), and up to 10.037% lower (Model 8) Distributed via company show lower alphas by 14.303% (Model 8) up to 14.948% (Model 7). Fund alphas higher when: An increase in required minimum capitalization for a hedge fund manager from $1 to $2 million is associated with higher alphas by approximately 0.43% (Model 9) (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  21. Key Items in Table 6: Average Returns Average monthly returns are lower when: Restrictions on the location of key service providers have lower monthly average return by 0.546% (Model 11) – 0.863%(Model 13) Via wrapper have lower average return by 0.349% (Model 11) to 1.503% (Model 15) Average monthly returns are higher when: Yearly Capital Redemption Applied by 0.239% – 0.416% (Models 9,11) (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  22. Key Items in Table 7: Standard Deviation Standard deviation of monthly returns are lower when: An increase in required minimum capitalization for a hedge fund manager from $1 to $2 million is associated with lower SD by approximately 0.026-0.040 (Model 16-19) Restrictions on the location of key service providers have lower SD by 0.567 (Model 17) – 1.417(Model 18) Via wrapper have lower SD by 0.766 (Model 17) to 1.477 (Model 18) Private placement show lower SD by 1.608 (Model 19) (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  23. Analyses of Fund Structure Table 8: Fixed Fees and Performance Fees Fixed Fees are higher Marketing via wrapper have fixed fees that are higher by 0.263% (Model 21) to 0.468% (Model 25) Performance Fees lower when: Restriction on the location of key service providers, lower by 3.935% (Model 24) Marketing via wrappers, lower by 3.968%(Model 24) (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

  24. (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions Conclusions

  25. Primary Findings Hedge fund regulations minimum capitalization restrictions on the location of key service providers wrapper distributions associated with bad performance lower fund alphas lower manipulation-proof performance measures lower average returns associated with lower risk lower standard deviation associated with inefficient fund compensation lower performance fees higher management fees (1) Introduction (2) HF Regulation (3) Data (4) Analysis (5) Conclusions

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