Genetic programming application to generate technical trading rules in stock markets Online publication date: Fri, 12-Nov-2010
by Akbar Esfahanipour, Somaye Mousavi
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 2, No. 3/4, 2010
Abstract: Technical trading rules can be generated from historical data for decision making in stock trading. In this study, genetic programming (GP) as an evolutionary algorithm has been applied to automatically generate such technical trading rules on individual stocks. In order to obtain more realistic trading rules, we have included transaction costs, dividends and splits in our GP model. Our model has been applied for nine Iranian companies listed on different activity sectors of Tehran Stock Exchange (TSE). Our results show that this model could generate profitable trading rules in comparison with buy and hold strategy for companies having frequent trading in the market. Also, the effect of the above mentioned parameters on trading rule's profitability are evaluated using three separate models.
Online publication date: Fri, 12-Nov-2010
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