Besides pair-trading, “factor model” is the most popular
workhorse of the statistical arbitrageur. In a
previous article, I discussed the most well-known factor model -- the Fama-French
Three-Factor model, with the general market index returns, the market-cap of the
stock, and the book-to-price ratio as the only three factors driving returns.
However, as I explained earlier, this factor model has a very long horizon. For
the quantitative trader who needs to make money every month, the natural
instinct is to look for a more “sophisticated” factor that works in the short
term, or even to develop some kind of model that use different factors every
month in response to “market conditions”. Alas, other than hearsays and
second-hand gossips, I have never witnessed an actual success of this approach
in a hedge fund or proprietary trading group -- at least a success that lasts
for more than a year.
I am of course not privy to the current performance numbers of
factor models run by some of the most successful hedge funds today. However,
there is a class of ETF (called “XTF”) marketed by
PowerShares Capital Management that
uses a similar factor approach for its stock selection criteria. According to
media reports, each stock in these XTF’s is scored by 25 variables such as cash
flow, earnings growth, price momentum, etc. This sounds like a classic factor
model to me. This model is reportedly designed by the quantitative unit at
American Stock Exchange. To find out if they have indeed discovered the holy
grail of factor models, I looked at the performance of these XTF compared to
their benchmarks.
Here I tabulate the XTF’s for each market cap and value
category, their corresponding benchmark market index ETF’s, and finally the YTD
differential returns up to December 13, 2006. (PJG and PJM have too short a
history for this comparison.)
The differential returns are all over the place: some
positive, others negative. To me, this is symptomatic of a factor model that
does not have predictive power. (After all, if the differential returns are
consistently negative, we could have long the ETF, short the XTF, and make
consistent profits!) At the very least, this factor model may have a horizon
much longer than what most traders would be interested in -- in which case, why
not just use the simple Fama-French model?
This is not to say that exotic, proprietary factor models have
no use: they tend to be pretty useful for risk management, as volatilities and
correlations are often easier to predict than returns. But beware every time
your risk management software vendor tries to sell you an alpha generator!
Ernest Chan, Ph.D. is a quantitative trader and consultant who helps his clients
implement automated, statistical trading strategies. He can be reached through
www.epchan.com. Ernie has worked as a
quantitative researcher and trader in various investment banks (Morgan Stanley,
Credit Suisse First Boston, Maple Securities) and hedge funds (Mapleridge
Capital, Millennium Partners, MANE Fund Management) since 1996. He has a Ph.D.
in physics from Cornell University.
