Improving Technical Analysis Predictions: An Application of Genetic Programming

Jin Li and Edward P. K. Tsang, University of Essex

Recent studies in finance domain suggest that technical analysis may have merit to predictability of stock. Technical rules are widely used for market assessment and timing. For example, moving average rules are used to make buy or sell decisions at each day. In this paper, to explore the potential prediction power of technical analysis, we present a genetic programming based system FGP (Financial Genetic Programming), which specialises in taking some well known technical rules and adapting them to prediction problems. FGP uses the power of genetic programming to generate decision trees through efficient combination of technical rules with self-adjusted thresholds. The generated rules are more suitable for the prediction problem at hand. FGP was tested extensively on historical DJIA (Dow Jones Industrial Average) index data through a specific prediction problem. Preliminary results show that it outperforms commonly used, non-adaptive, individual technical rules with respect to prediction accuracy and average annualised rate of return over two different out-of-sample test periods (three and a half year in each period).

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