Evolving Least Squares Support Vector Machines for Stock.pdf

Evolving Least Squares Support Vector Machines for Stock Market Trend Mining * 1 2 † 3 1 2 Lean Yu , Huanhuan Chen , Shouyang Wang , Kin Keung Lai 1 Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China 2 Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong 3 School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom Abstract — In this study, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, SP 500 Index, Dow Jones Industrial Average Index, and New York Stock Exchange Index, are used as testing targets. Experimental results obtained reveal that the proposed evolving LSSVM can produce some forecasting models that are easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization method