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CORN: Correlation-driven Non parametric Learning Approach for Portfolio Selection

Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this paper, we propose a novel learning to trade algorithm termed the CORrelation-driven Non parametric learning strategy (CORN) for activelytrading stocks, which effectively exploits statistical relations between stock market windows via a non parametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, in which the encouraging results show that the proposed new algorithm can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpasses avariety of state-of-the-art techniques significantly.

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