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.
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- Data Mining for Algorithmic Asset Management
- Book Title
- Data Mining for Business Applications
- Book Part
- pp 283-295
- Print ISBN
- Online ISBN
- Springer US
- Copyright Holder
- Springer US
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- Editor Affiliations
- 1. School of Software Faculty of Engineering and Information Technology, University of Technology
- 2. Department of Computer Science, University of Illinois at Chicago
- 3. Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology, University of Technology
- Author Affiliations
- 4. Department of Mathematics, Imperial College London, 180 Queen's Gate, Caixa Postal: 15064, Porto Alegre, UK, SW7 2AZ, London
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