On Wednesday, the Wall Street Journal ran a blog on replicating hedge fund returns. The technique proposed by Tim Edwards, an analyst with S&P Dow Jones Indices, could not be simpler. He constructed a model consisting 50% of the returns on the S&P U.S. Aggregate Bond Index and 50% on those of the S&P Global 1200 Index, rebalanced monthly, charging a 1.5% annual management and a 15% performance fee. He compared it with the HFRI Fund-Weighted Composite Index, which is constructed from the reported returns of hedge funds worldwide. The blog provided this graph of the model’s monthly returns against the benchmark:
No analysis was provided, but the graph is all that is needed to show that the series are tightly correlated: from the look of it, probably >0.9. Note that the replication was consistently and significantly more volatile than the index it allegedly replicates.
Mr. Edwards is quoted saying “The average hedge fund looks like a fixed blend of cheap investments, at high cost.” He has not demonstrated this. What he has shown is that an unweighted index aggregating >2,000 managers’ returns will, over short periods, resemble broad market indices. It does not analyze the funds’ performance over periods meaningful to investors, and over which the funds’ cumulative returns and volatility would differentiate them from an index. Mr. Edwards’s model replicates the mean fund and chain-links its return to that of the next month’s mean return: it does not replicate any particular fund, so there is no sense in which it replicates an “average” hedge fund. Volatility lowers compounded returns, so his model underperforms even that notional fund on both absolute and risk-adjusted bases over periods of a year or more.
That an average of many funds’ returns resembles such a model over short periods is not surprising. The World Federation of Exchanges’ June report counted 42,759 companies worldwide. The overwhelming majority of them would not attract a fund’s attention, so hedge funds’ portfolios inevitably overlap. In a given month, returns on the Russell 1000 are similar to the average of all large cap U.S. equity funds, too. But hedge funds’ returns disperse widely around the mean and are negatively skewed: the return on the median fund (the one outperformed by as many funds as it outperforms) is higher than the mean return of all funds. Based on the graphic with which we are provided, Mr. Edwards’s technique creates a roughly sixtieth percentile hedge fund clone, in a period of consistently rising markets that favor a long-only comparison. This is hardly impressive.
There are many issues regarding indices that purport to measure hedge fund returns. They include survivorship bias, whether the “best” funds report their returns at all, and whether funds that do report are honest (it is in their interest to report returns which flatter their actual performance). And quite apart from issues with indices, there are plenty of reasons to be skeptical about hedge fund. The extent to which leverage parades itself as alpha is the most important one.
I am skeptical about “hedge fund beta,” and consequently doubtful about any approach to hedge fund replication based on aggregate fund returns rather than the returns of a specific fund. The high dispersion of hedge fund returns indicates that the mean of those returns has limited value as a series to replicate: any data can be correlated with sunspots, Elvis sightings, etc., and data-mining will always find a series it resembles. A specific fund at least provides a meaningful basis for comparison with other financial series, and one that might be worth the trouble of using factor analysis or replicated trading strategies, to replicate. Strategies based on an algorithmic model of a fund's trading strategy may also hold some promise. I am nevertheless skeptical. But I am not skeptical about crude strategies such as that proposed by Mr. Edwards: it is not a replication at all.