Under the belief that good performance will persist, the authors test whether hedge fund portfolios that are formed based on past manager performance have alphas that disappear after accounting for liquidity risk. They form optimal hedge fund portfolios using a Bayesian method to incorporate three predictability assumptions (manager skills, fund risk loadings, and benchmark returns). They then estimate portfolio performance using a previously researched model but add a liquidity risk factor. Lastly, the authors examine the extent to which the supply of liquidity by hedge funds during periods of low liquidity may explain their alphas.

Many investors believe that hedge funds have delivered alpha net of fees. It is important for investors to understand whether their risk-adjusted performance justifies the high fees paid to managers. Previous industry research suggested that predictability in managerial skills (or performance persistence) is a major source of hedge fund portfolios’ superior performance. The authors expand on this research by adding a liquidity risk factor. They observe that for 30% of the equity hedge fund portfolios, the estimated alphas are no longer significant at the 5% level after accounting for liquidity risk. Additionally, within the emerging market hedge funds, the outperformance disappears after liquidity risk is accounted for.

The authors demonstrate that predictability in manager skills is not sufficient to create significant alpha for most equity hedge fund strategies and that part of equity hedge fund performance is compensation for bearing systematic liquidity risk. This liquidity risk is important and should be considered by investors in a performance evaluation of equity hedge fund strategies.

The authors conduct an extensive review of existing literature. Their approach to forming optimal hedge fund portfolios follows previous research and assumes that hedge funds have managerial skills that are predictable. They seek to determine whether predictability of manager skills is an effective way to find hedge fund managers that generate high alpha after accounting for liquidity risk.

The authors consider seven different types of Bayesian-optimizing investors and use a six-factor model to evaluate the performance of the optimal portfolios of each of the investors. They regress each portfolio’s monthly excess return on the following factors: (1) the excess return on the S&P 500 Index, (2) the excess return on the US dollar index, (3) the excess return on the Goldman Sachs Commodity Index, (4) the excess return on the Lehman Corporate AA Intermediate Bond Index, (5) the return spread on the Lehman BAA Corporate Bond Index and the Lehman Treasury Index, and (6) a volatility risk factor.

Hedge fund data are obtained from the Lipper TASS database. The authors begin with TASS data that include “graveyard” hedge funds to avoid survivorship bias and exclude the first 12-month return data to avoid backfilling bias. Hedge funds that did not report net returns, monthly returns, or returns in US currency or had less than two years of performance are also excluded. The authors then calculate summary statistics for both live and defunct hedge funds.

To be able to make a direct comparison with that of the portfolios originally formed by previous researchers, the authors use the Fung and Hsieh (Financial Analysts Journal 2004) model with and without a liquidity risk factor to evaluate the performance of the optimal equity hedge fund portfolio factors. By using this model, the liquidity factor is lower and less significant, but this finding can be explained by the fact that the liquidity risk factor is adjusted by the size factor (the difference between small-cap and large-cap return).

The authors focus only on equity hedge funds. They state that the effect of liquidity risk is weaker for some non-equity (i.e., managed futures) hedge funds. As assets under management continue to increase in these types of funds, more research on the liquidity risk factor would be worth exploring with a broader set of hedge funds.