Guest Post: Galleon Technology Fund: A Clipper Or A Barge?
Submitted by Michael Markov of MPI Blog
The goal of this week’s post is to explore the factors driving
Galleon Technology fund’s performance a bit deeper. The fund was widely
known for its high turnover, rapid-fire trading and extensive use of
options to leverage short-term bets. Therefore, it seems unlikely that
this quintessential hedge fund could resemble a typical technology
sector mutual fund. But, as we’ve already learned from our previous analysis of Renaissance RIEF, such massive trading may inadvertently result in performance that can be explained by a handful of directional bets.
First, we expanded the time period from our previous post. The chart
below now includes excess return information for five full years from
2004 where we’ve highlighted three months: July ’07 and ’08, which have
come under heavy scrutiny for alleged insider trading and July ’06,
where outperformance was notably higher than both July ’07 and July’08.
Over the five year period (2004-2008) these three months were the only
months when the Galleon fund significantly outperformed its peers—an
average technology hedge fund in the index.
Clearly, July performance numbers for each of the three years are
deemed as statistical outliers (regardless of the legal connotation)
which potentially could distort any further analysis of Galleon
returns. In addition, the complaint mentions July 2007 $4M gain from
trading Hilton—not a technology stock—which could also “contaminate”
our analysis. Given the facts above we decided to remove all three July
observaions as outliers.
Next, we proceed with a dynamic forensic analysis of the Galleon Technology fund’s returns similar to the one performed in our previous post using MPI Stylus™ and its DSA engine. The results of this analysis are presented in an exposure chart below. Note
that this chart does not show actual holdings, but allocations to
different factors that best explain the returns of the fund.
We note quite stable long exposure to several Dow Jones technology
sectors. The exposure to foreign technology companies is represented by
the MSCI All Country ex. US Tech index, indicating positions in ADRs,
foreign stocks, or simply sensitivity to foreign markets through
investing in U.S. stocks. Short exposure to PowerShares QQQ ETF is
supported by the fund’s SEC filings according to which the fund
maintained at times a significant position in QQQ put options. Both the
cash exposure (about 60%, an indication of net 40% market exposure) and
the size of the short position (30-40%) are similar to our earlier
results. At the same time, these results are slightly different from
the ones in the previous post, which is expected given that we removed
three very large outliers.
It should be noted that because of the removal of outliers the
statistical quality of the analysis improved significantly. The
R-squared statistic determining the quality of fit is 89% and the Predicted R-squared,
MPI’s proprietary cross-validation statistic, is 79%. Such a high
quality regression is more typical for a large, diversified mutual
fund. The quality of fit is also illustrated in the performance chart
below where an exposure-weighted portfolio made of factor indexes
(called a “Style” or “Tracking” portfolio) closely tracks the fund’s
actual performance in-sample (“Total”).
Note that the tracking is exceptionally good through the middle of
2006 where the fund and the tracking portfolio lines begin to deviate
slightly despite the removal of outliers. Nevertheless, both the
pattern of performance and its magnitude are captured very well
throughout the entire five-year history.
While the results of this analysis are very intriguing and somewhat
unexpected, the study itself carries very important lessons for
investors. First, it shows that analysis of hedge fund returns is a
delicate, iterative process requiring careful examination of residuals.
If outliers cannot be explained by any available portfolio information
they could warrant removal or winzorisation.
More importantly, removal of several large “alpha” outliers allowed us
to show that in the remaining periods this quintessential high-turnover
arbitrageur behaved more like a diversified mutual fund, with returns
mimicked by a few long-term directional bets. And while massive
computer-generated trading of Renaissance RIEF resulted in such an
immediately apparent pattern, in the case of Galleon, the long-term
directional pattern became visible only after identification and
removal of several exceptional returns.
Daniel Li, PhD contributed to this research.