October 9: Marco Avellaneda, CIMS
Statistical arbitrage in US equity markets
Joint work with Jeong-Hyun Lee
We study model-driven statistical arbitrage strategies in U.S.
equities. Trading signals are generated in two ways: using Principal
Component Analysis 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 quant fund drawndown of August 2007.