We study model-driven statistical arbitrage in U.S. equities. The 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 mean-reverting

processes. This leads naturally to “contrarian” trading signals.

The main contribution of the paper is the construction, back-testing

and comparison of market-neutral PCA- and ETF- based strategies applied

to the broad universe of U.S. stocks. 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

much stronger performances prior to 2003. During 2003-2007, the average

Sharpe ratio of PCA-based strategies was only 0.9. Strategies based

on ETFs achieved a Sharpe ratio of 1.1 from 1997 to 2007, experiencing

a similar degradation after 2002.

We also introduce a method to account for daily trading volume information

in the signals (which is akin to using “trading time” as opposed to

calendar time), and observe significant improvement 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 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