Hedge Fund Conference Kellogg School of Mgmt

1. Hedge Fund ConferenceKellogg School of Mgmt Roundtable Discussion Hedge Fund Performance Evaluation Lawrence R. Glosten Columbia University

2. The academic approach • Portfolio manager provides excess return (over the risk free rate) Xpt in period t • These returns are modeled as: Xpt = α + β1X1t + …+ βnXnt + εt Where the X’s are excess returns that capture the risks that investors care about • α is the “value” provided by the portfolio manager which in general depends upon the factors that are included

3. Performance evaluation problems • There is some suggestion that the excess returns may be measured with error • There is some (though certainly not universal) agreement on what X’s should be used for modeling simple equity returns • Fama/French three factors: market, small minus large, high minus low (book/mkt) • Less agreement on how to model more complicated returns from hedge funds • Worry about performance evaluation affecting investment strategies

4. The returns • Self-reported returns, including back-fill • Back fill data identified as such • Some odd statistical properties • In a market model regression with lags, lagged market returns have significant coefficients (Asness, Krail and Liew) • Hedge fund returns exhibit more serial correlation than one would expect if returns were calculated from market prices (Getmansky, Lo and Makarov)

5. Modifications to standard performance evaluation • Non-linear terms—options on the market (Glosten, Jagannathan) • Merger Arb writing puts (Mitchell, Pulvino) • Trend following buying straddles (Fung and Hsieh) • Lagged market returns to correct for “smoothed” hedge fund returns (Asness, et al) • Other portfolios—bonds, currencies, commodity and security straddles (Fung and Hsieh)

6. Hedge fund alphas and betas

7. Problems • As factors increase in number, run into too few data points per parameter • Things (betas, alphas) change through time (Fung, Hsieh, Naik, Ramadorai) • But, may not need so many factors—for consistent estimation of alpha, need to include only risks that investors care about

8. Finally • Even if we knew all alphas, betas and R2’s , this does not say how much an individual (organization) should invest in a hedge fund—this depends upon “preferences” • At least knowing the betas gives an idea of how hedge funds fit into rest of portfolio and how to manage risk with other securities • Quantitative estimation might be helped by qualitative information from hedge funds

9. Questions I • For Steve Nesbitt • A quantitative analysis of performance provides, in addition to alpha, betas with respect to various risks. How do you use this information to advise clients on hedge fund allocations? Perhaps you could also talk a little about “portable alpha.” • For Don Fehrs • What information do you get from hedge funds in addition to past returns? What are the important qualitative inputs to deciding to carry (or drop) a hedge fund? Has there been much change in your portfolio of funds over time?

10. Questions II • For Gary Brinson • It appears that the contract between investors and hedge funds has been fairly standard, and is based on returns rather than on “performance” as we have been discussing the term today. Do you see this practice as persisting, or might we see “alpha compensation?” • For Girish Reddy • Fund of Funds (FoF) growth has been rather remarkable (Steve Nesbitt reports that FoF’s held 36% of hedge fund assets at year end 05, compared to 17% in 00) Three developments could lead to a reduction in the attractiveness of paying a fee to FoF’s: increased investor sophistication, intermediation by consultants, development of multi-strategy hedge funds. What do you think?