Data Mining for Algorithmic Asset Management

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Statistical arbitrage refers to a class of algorithmic trading systems implementing data mining strategies. In this chapter we describe a computational framework for statistical arbitrage based on support vector regression. The algorithm learns the fair price of the security under management by minimining a regularized ε-insensitive loss function in an on-line fashion, using the most recent market information acquired by means of streaming financial data. The difficult issue of adaptive learning in non-stationary environments is addressed by adopting an ensemble learning approach, where a meta-algorithm strategically combines the opinion of a pool of experts. Experimental results based on nearly seven years of historical data for the iShare S&P 500 ETF demonstrate that satisfactory risk-adjusted returns can be achieved by the data mining system even after transaction costs.