Title: Genetic programming application to generate technical trading rules in stock markets

Authors: Akbar Esfahanipour, Somaye Mousavi

Addresses: Industrial Engineering Department, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran. ' Industrial Engineering Department, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran

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.

Keywords: genetic programming; technical trading rules; stock markets; Tehran Stock Exchange; TSE; Iran; decision making; stock trading.

DOI: 10.1504/IJRIS.2010.036870

International Journal of Reasoning-based Intelligent Systems, 2010 Vol.2 No.3/4, pp.244 - 250

Published online: 12 Nov 2010 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article