Marco Avellaneda
Statistical Arbitrage in the U.S. Equities Market
We study model-driven statistical arbitrage strategies in U.S.
equities. Trading signals are generated in two ways: using Principal
Component Analysis (PCA) and using sector ETFs. In both cases, we
consider the residuals, or idiosyncratic components of stock returns,
and model them as a mean-reverting process, which leads naturally to
"contrarian'' trading signals. The main contribution of the paper is
the back-testing and comparison of market-neutral PCA- and ETF- based
strategies over the broad universe of U.S. equities. Back-testing shows
that, after accounting for transaction costs, PCA-based strategies have
an average annual Sharpe ratio of 1.44 over the period 1997 to 2007,
with a much stronger performances prior to 2003: during 2003-2007, the
average Sharpe ratio of PCA-based strategies was only 0.9. On the other
hand, strategies based on ETFs achieved a Sharpe ratio of 1.1 from 1997
to 2007, but experience a similar degradation of performance after
2002. We introduce a method to take into account daily trading volume
information in the signals (using "trading time'' as opposed to
calendar time), and observe significant improvements in performance in
the case of ETF-based signals. ETF strategies which use volume
information achieve a Sharpe ratio of 1.51 from 2003 to 2007. The paper
also relates the performance of mean-reversion statistical arbitrage
strategies with the stock market cycle. In particular, we study in some
detail the performance of the strategies during the liquidity crisis of
the summer of 2007. We obtain results which are consistent with
Khandani and Lo (2007) and validate their "unwinding'' theory for the
quantitative fund drawdown of August 2007. Joint work with Jeonghyun
Lee, Courant Institute, NYU.