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Statistical Arbitrage in the U.S. Equities Market Marco Avellaneda
 

Summary: Statistical Arbitrage in the U.S. Equities Market
Marco Avellaneda
and Jeong-Hyun Lee
July 11, 2008
Abstract
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"

  

Source: Avellaneda, Marco - Department of Mathematics, Courant Institute of Mathematical Sciences, New York University

 

Collections: Mathematics