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.
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