Hedge Fund Market Neutral Strategies Distinguishing Financial and

Hedge Fund Market Neutral Strategies: Distinguishing Financial and Operational Risk Factors Stephen J. Brown NYU Stern The Joint 14 th Annual PBFEA and 2006 Annual Fe. AT Conference 第十四屆亞太財務經濟及會計會議暨 2006台灣財務 程學會聯合研討會

Distinguishing operational and financial risk ¯Historical perspective ¯Operational risk ¯Characterized by conflicts of interest ¯Financial risk ¯The myth of market neutrality ¯Robust measure of tail risk neutrality ¯Conclusion

The History of Hedge Funds ¯ The first hedge fund: Alfred Winslow Jones (1949) ¯Limited Partnership (exempt from ’ 40 Act) ¯Long-short strategy ¯ 20% of profit, no fixed fee ¯Used short positions and leverage ¯ “Hedge Fund” (Fortune magazine 1966) ¯ Tiger Fund (Institutional Investor 1986) ¯ George Soros $3. 2 Billion raid on the ERM (1992) ¯ Cal. PERS (2000)

Institutional concern about risk ¯Fiduciary guidelines imply concern for risk ¯Financial risk ¯Operational risk ¯Institutional demand ¯Growing popularity of market neutral styles ¯Explosive growth of funds ¯Demand for “market neutral” funds of

Operational Risk Hedge fund failure is highly predictable … Source: Tremont TASS (Europe) Limited

Measuring operational risk ¯SEC registration requirement (Feb 2006) ¯ 2270 of TASS Funds that registered ¯Had better past performance ¯Had larger assets under management ¯ 15. 8% had prior legal/regulatory problems

Correlates of operational risk “Problem” Funds “Non-Problem” Funds N Mea n Median N Mean Media n Diff Avg Return 356 0. 89 0. 80 1898 0. 84 -0. 09* Std Dev 354 2. 60 1. 79 1897 2. 74 2. 08 -0. 14 Sharpe Ratio 354 0. 33 0. 29 1897 0. 39 0. 30 -0. 06* AUM ($mm) 325 218. 2 58. 74 1647 180. 2 54. 00 38. 00 Age (Years) 358 5. 65 4. 50 1912 4. 99 3. 92 0. 66** Management Fee (%) 358 1. 37 1. 25 1912 1. 38 1. 50 -0. 01 Incentive Fee (%) 358 15. 2 3 20. 00 1912 17. 52 20. 00 -2. 29**

External conflicts Problem funds Non problem funds With: N % Yes Broker/Dealer 359 73. 8 1912 24. 8 Investment Comp 359 50. 4 1912 16. 0 Investment Advisor 359 74. 7 1912 41. 3 Commodities Broker 359 53. 5 1912 20. 3 Bank 359 40. 4 1912 9. 8 Insurance 359 39. 8 1912 9. 4 Sponsor of LLP 359 56. 8 1912 22. 2

Internal conflicts Problem funds With: N % Yes Non problem funds N % Yes Trade securities with clients 359 30. 1 1912 8. 4 Allow trading on own account 359 85. 2 1912 69. 6 Recommend own securities 359 74. 9 1912 50. 8 In-house broker dealer 359 31. 2 1912 2. 3 Recommends own underwriting service 359 69. 4 1912 46. 8 Recommends commission fee items 359 22. 6 1912 15. 7 Recommends brokers 359 45. 7 1912 38. 4 Use broker provided external research 359 81. 3 1912 69. 9

Towards a univariate index of operational risk TASS Variables SEC Variables Previous Returns -0. 27 In-house broker dealer 0. 06 Previous Std. Dev. -0. 36 Associated with broker dealer 0. 24 Fund Age -0. 10 Investment company association 0. 25 Log of Assets 0. 09 Investment advisor association 0. 24 Reports Assets 0. 07 Commodity trader association 0. 44 Incentive Fee -0. 89 Associated with bank or thrift 0. 39 Margin -0. 29 Associated with insurance co 0. 42 Audited -0. 21 Associated with ltd. partner syndicator 0. 27 Personal Capital -0. 26 Trade securities with clients 0. 06 Onshore -0. 11 Allow trading on own account Open to Inv. Accepts Managed Accts 0. 04 -0. 13 -0. 12 Recommend own securities 0. 32 Recommends own underwriting service 0. 24 Recommends commission fee items 0. 28 Recommends brokers -0. 35 Use broker provided external research -0. 69

Financial Risk Source: Elton and Gruber 1995. Risk is measured relative to the standard deviation of the average stock

Financial Risk

Caught by the tail ¯“S&P 500 returns at Treasury Bill risk” ¯Most new funds claim to be “market neutral” ¯Zero correlation with benchmark ¯Zero correlation is not a strategy ¯Zero correlation is an outcome of a strategy ¯These strategies fail in liquidity crises ¯Risk is considerably understated ¯New concept: “tail risk neutrality”

A market neutral strategy

Data ¯TASS hedge funds – both dead and alive ¯US funds with at least 10 returns, average of 40 max of 120. ¯Not a lot of data per fund, but plenty when the universe is combined – nearly 50, 000 fund-month observations.

An example of ‘market neutrality’ Fund Returns 0. 8 1. 5% 0. 6 1. 1% 0. 4 0. 8% 0. 2 0. 4 0. 6 0. 8 Market Returns Assuming MVN returns Beta =. 28, rho =. 24

Market neutrality in the ‘real world’ Fund Returns 0. 8 2. 5% 0. 6 1. 9% 0. 4 1. 3% 0. 2 0. 6% 0. 2 0. 4 0. 6 0. 8 Using TASS data S&P 500 Returns Beta =. 28, rho =. 24

Market neutrality in the ‘real world’ Fund Returns 0. 8 2. 5% 0. 6 1. 9% 0. 4 1. 3% 0. 2 0. 6% 0. 2 0. 4 0. 6 0. 8 S&P 500 Returns Beta =. 28, rho =. 24

Long Short Equity Funds Fund Returns 0. 8 2. 9% 0. 6 2. 2% 0. 4 1. 3% 0. 2 0. 6% 0. 2 0. 4 0. 6 0. 8 S&P 500 Returns Beta =. 50, rho =. 37

Event driven style Fund Returns 0. 8 3. 1% 0. 6 2. 3% 0. 4 1. 5% 0. 2 0. 8% 0. 2 0. 4 0. 6 0. 8 S&P 500 Returns Beta =. 20, rho =. 23

Dedicated Short Sellers Fund Returns 0. 8 4. 5% 0. 6 3. 4% 0. 4 2. 3% 0. 2 1. 1% 0. 2 0. 4 0. 6 0. 8 S&P 500 Returns Beta = -. 91, rho = -. 61

Fixed income arbitrage Fund Returns 0. 8 1. 5% 0. 6 1. 1% 0. 4 0. 8% 0. 2 0. 4 0. 6 0. 8 S&P 500 Returns Beta = 0. 01, rho = 0. 02

Funds of Hedge Funds

Funds of Hedge Funds ¯ Provides

Funds of Hedge Funds ¯ Provides ¯Diversification – lower value at risk

Funds of Hedge Funds ¯ Provides ¯Diversification – lower value at risk ¯Smaller unit size of investment

Funds of Hedge Funds ¯ Provides ¯Diversification – lower value at risk ¯Smaller unit size of investment ¯Professional management / Due diligence

Funds of Hedge Funds ¯ Provides ¯Diversification – lower value at risk ¯Smaller unit size of investment ¯Professional management / Due diligence ¯Access to otherwise closed funds

Institutions love Fo. F ¯ Spectacular growth of Funds 2000: 2003: 2005: 15% of all Hedge funds were Fo. F 18% of all Hedge funds were Fo. F 27% of all Hedge funds were Fo. F ¯ Institutional attraction of Funds ¯ Risk management ¯ Due diligence

Funds of Funds Fund Returns 0. 8 2. 9% 0. 6 2. 2% 0. 4 1. 3% 0. 2 0. 6% 0. 2 0. 4 0. 6 0. 8 S&P 500 Returns Beta =. 14, rho =. 22

Relationship to LIBOR Fund Returns 0. 8 1. 0% 0. 6 0. 8% 0. 4 0. 5% 0. 2 0. 3% 0. 2 0. 4 0. 6 0. 8 LIBOR return Beta = 0. 0, rho = 0. 0

Fixed income arbitrage Fund Returns 0. 8 2. 0% 0. 6 1. 5% 0. 4 1. 0% 0. 2 0. 5% 0. 2 0. 4 0. 6 0. 8 LIBOR return Beta = -. 02, rho = -. 05

Simple measures of tail risk exposure ¯ Independence an unrealistic benchmark ¯ Consider ¯MV Normal with the sample correlation ¯MV Student with 3 df

Simple measures of tail risk exposure ¯ Independence an unrealistic benchmark ¯ Consider 0. 0188 0. 24 ¯MV Normal with the sample correlation ¯MV Student with 3 df

An example of ‘market neutrality’ Fund Returns 0. 8 1. 5% 0. 6 1. 1% 0. 4 0. 8% 0. 2 0. 4 0. 6 0. 8 Market Returns Assuming MVN returns Beta =. 28, rho =. 24

An example of ‘market neutrality’ Fund Returns 0. 8 1. 5% 0. 6 1. 1% LW WW 0. 4 0. 8% 0. 2 0. 4% LL WL 0. 2 0. 4 0. 6 0. 8 LL should be 1. 88% of Market Returns sample assuming MVN Beta =. 28, rho =. 24

Comparison with S&P 500 Benchmark Correlation with benchmark Binomial Crash p-value (ind) p-value (N) p-value (t) All Funds 0. 28** 0 0 0 Funds of Funds 0. 14** 0 0 0 Convertible Arbitrage 0. 09** 0 0. 033 0. 840 Dedicated Short Bias -0. 91** 0. 997 0. 112 0. 838 Emerging Markets 0. 66** 0 0. 031 0. 394 Equity Market Neutral 0. 02 0. 001 0. 006 0. 893 Event Driven 0. 20** 0 0 0 Fixed Income Arbitrage 0. 01 0. 395 0. 480 0. 995 Global Macro 0. 08 0. 004 0. 034 0. 752

Comparison with LIBOR Benchmark Correlation with benchmark Binomial Crash p-value (ind) p-value (N) p-value (t) All Funds 0. 00 1 1 1 Funds of Funds 0. 01 1 Convertible Arbitrage 0. 00 0 0 0. 074 Dedicated Short Bias 0. 07 0. 006 0. 031 0. 432 Emerging Markets -0. 17** 0. 995 0. 823 1 Equity Market Neutral 0. 07** 0. 148 0. 567 1 Event Driven -0. 04** 1 1 1 Fixed Income Arbitrage -0. 05 0 0 0. 007 Global Macro -0. 03 0. 849 0. 756 0. 999

Logit Specification Boyson, Stahel and Stulz [2006] suggest running logit regressions of whether a fund index crashes in a month upon the market return and a dummy for market crashes. A positive coefficient on the dummy indicates additional dependence during crashes. Lacks power when run on a single index. We run the regressions on the cross-section.

Conclusions ¯Operational risk ¯Important role for due diligence ¯Characterized by internal and external conflicts of interest ¯Financial risk ¯Undiversifiable crash risk lurks in hedge fund returns, despite their seemingly light dependence in normal times.