A genetic programming model to generate risk-adjusted technical trading rules in stock markets |
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| Authors: | Akbar Esfahanipour Somayeh Mousavi |
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| Affiliation: | 1. Faculty of Engineering and the Environment, Lanchester Building, University of Southampton, SO17 1BJ, UK;2. Electronics and Computer Science, Building 32, University of Southampton, SO17 1BJ, UK;1. Faculty of Technology, Engineering and the Environment School of Computing, Telecommunications and Networks Birmingham City University, UK;2. Dept de Lienguatges I Sistemes Informatics, Universitat Politécnica de Catalunya, Spain;1. Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi 755-8611, Japan;2. Information, Production and Systems Research Center, Waseda University, Hibikino 2-2, Kitakyushu, Fukuoka 808-0135, Japan;1. Faculty of Business Administration and Management, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain;2. Faculty of Business & Social Sciences, Hamburg University of Applied Sciences, Berliner Tor 5, 20099 Hamburg, Germany |
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| Abstract: | Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks. Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules. Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis. |
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