1. Hedge Funds: Performance, Risk and Capital Formation. Bill Fung (London Business School) David Hsieh (Duke University) Narayan Naik (London Business School) Tarun Ramadorai (Univ. of Oxford and CEPR)

2. Focus of this paper What is the relationship between risk- adjusted performance (alpha), alpha persistence, and capital formation in the hedge fund industry? Testing whether Berk and Green’s (JPE 2004) rational model of active portfolio management is appropriate for the hedge fund industry.

3. The Berk and Green Model Berk and Green (JPE 2004): Rational model of active portfolio management has three features:  Investors competitively provide capital to funds.  Managers have differential ability to generate high returns, but face decreasing returns to scale in deploying their ability.  Investors learn about managerial ability from past performance and direct more capital towards funds with superior risk-adjusted performance. Results in zero alpha and zero alpha persistence in equilibrium.

4. Preview of our main conclusions: Implication 1 of Berk and Green: Funds with higher ability managers should experience greater capital inflows.  We show that funds that generate statistically positive alpha experience far greater and steadier capital inflows than their less fortunate counterparts.

5. Preview of our main conclusions: Implication 2 of Berk and Green: Diminishing returns to scale combined with the inflow of new capital leads to erosion of superior performance over time, even for high ability funds.  We show that capital inflows lead to lower alpha, and lower alpha persistence.  We provide evidence that even alpha-producing funds have experienced a recent, dramatic decline in risk-adjusted performance.

6. A Few Facts About Hedge Fund Returns Individual hedge fund data is subject to biases Fung and Hsieh (2000) and Liang (2000) Hedge funds exhibit different styles/systematic risk. Fung and Hsieh (1997, 2001, 2002, 2004), Brown and Goetzmann (2003), Mitchell and Pulvino (2001), Agarwal and Naik (2004) etc Hedge fund performance measurement is hard because of illiquidity in their holdings. Getmansky, Lo, Makarov (2004), Asness, Krail and Lew (2001) The best (and worst) hedge funds exhibit statistically detectable performance persistence. Kosowski, Naik and Teo (2005), Jagannathan, Malakhov and Novikov (2006) And much more interesting research that I have little time to mention today!

7. The Data Consolidate data from CSFB/Tremont TASS, HFR and CISDM. The most comprehensive set of Funds of Hedge Funds that is publicly available - 1603 in total. Ten-year period (Jan 1995 to Dec 2004). Breakpoints in the data confirmed by the Chow test.  Period I: January 1995 to September 1998.  Period II: October 1998 to March 2000.  Period III: April 2000 to December 2004. We use the Fung-Hsieh seven-factor model to estimate alpha and find…

8. For the Average FoF: No Detectable Alpha Except in Period II (October 1998-March 2000) see Table II

9. Differentiating Funds on Alpha Production For each two-year period, do the Kosowski, Timmerman, White and Wermers (2006) bootstrap. Basic intuition: trying to avoid Type I errors in detecting funds with alpha. OLS t-stat is misleading if errors are non-normally distributed and non i.i.d. This is very likely a problem for FoF returns. Robustness check the results in a variety of ways.  Getmansky-Lo-Makarov (2004) correction.  Politis-Romano (1994) stationary bootstrap.

10. Have Alphas and Have Betas see Table III

11. Have Alphas and Have Betas Transitions see Table III

12. Have Alpha, Have Beta Liquidations Table IV

13. Do Investors Recognize Differences Between Have Alphas and the Rest? Yes! See Figure 1

14. Differences in Flows see Table V

15. Do The Flows Behave Differently? Are our results driven by total return-chasing rather than alpha-perception on the part of investors? Perhaps there are clientele differences between have alphas and the rest? Regress quarterly flows on past quarter’s returns and past quarter’s flows.

16. Do The Flows Behave Differently? s ee Table VI Perhaps there are clientele differences between have alphas and have betas?

17. Do the Flows Generate Declines in Alpha? First, condition two-year transition probabilities, t-stat of alpha and magnitude of alpha on level of flow (above median or below median flow). Second, identify time-variation in alpha for have alphas and have betas, to check if there are corresponding trends.

18. Do Flows Generate Declines in Alpha? Results for Have Alphas: see Table IX Same is true for the t-stat of alpha and magnitude of alpha, see Table XI

19. Do Flows Generate Declines in Alpha? Results for Have Betas: see Table X No detectable difference on t-stat and magnitude of alpha (see Table XII), but recall these are beta-only funds anyway.

20. Has the Alpha Generating Ability of Alpha Producers Changed Over Time? See Table XIII

21. Conclusions Consistent with Berk and Green (JPE 2004), there are significant differences in the ability of managers to deliver alpha. Investors appear to perceive these ability differentials, and direct a steady stream of capital to the managers with higher ability. The persistent inflow of capital is associated with a decline in the alpha produced in the hedge fund industry. The decline is experienced by all managers regardless of ability.

22. Question? Recall that Berk and Green’s model predicts zero alpha and zero alpha persistence in equilibrium… Is the hedge fund industry heading that way?