Attention Factors for Statistical Arbitrage
This work addresses the challenge of profitable trading for investors by identifying mispricing in financial markets, though it is incremental in improving factor-based arbitrage methods.
The paper tackles the problem of statistical arbitrage by developing a framework that jointly learns conditional latent factors and a trading policy to maximize risk-adjusted returns after costs, achieving an out-of-sample Sharpe ratio above 4 and a net ratio of 2.3 on U.S. equities over 24 years.
Statistical arbitrage exploits temporal price differences between similar assets. We develop a framework to jointly identify similar assets through factors, identify mispricing and form a trading policy that maximizes risk-adjusted performance after trading costs. Our Attention Factors are conditional latent factors that are the most useful for arbitrage trading. They are learned from firm characteristic embeddings that allow for complex interactions. We identify time-series signals from the residual portfolios of our factors with a general sequence model. Estimating factors and the arbitrage trading strategy jointly is crucial to maximize profitability after trading costs. In a comprehensive empirical study we show that our Attention Factor model achieves an out-of-sample Sharpe ratio above 4 on the largest U.S. equities over a 24-year period. Our one-step solution yields an unprecedented Sharpe ratio of 2.3 net of transaction costs. We show that weak factors are important for arbitrage trading.