عنوان مقاله [English]
Technical analysis is a methods to predict price movements which is used widely in financial markets. Theoretical and experimental result shows that investing on different assets as a portfolio cause to risk reduction. One of the deficiencies of technical analysis is lack of attention to make an appropriate diversification on assets. This paper is trying to design an automated trading system which can make an appropriate portfolio and rebalance it whenever needed. This system will be designed by the use of genetic algorithm, technical analysis basis and indicators. In order to assess the efficiency of this expert system twelve stocks were chosen from Tehran securities exchange market and the system has been run for the period of 330 days. Result shows that the return of the expert system is significantly larger than buy and hold strategy of equal weighted portfolio, variance and semi-variance portfolios of Markowitz model and risk free rate of return in Iran.
* Alexander, S. S. (1961). Price movements in speculative markets: Trends or random walks. Industrial Management Review (pre-1986), 2(2), 7.
* Alexander, S. S. (1964). Price Movements in Speculative Markets--Trends or Random Walks, Number 2. IMR; Industrial Management Review (pre-1986), 5(2), 25.
* Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of financial Economics, 51(2), 245-271.
* Allen, F., & Karjalainen, R. (1995). Using genetic algorithms to find technical trading rules. Rodney L. White Center for Financial Research Working Paper, 20-95.
* Bohan, J. (1981). Relative strength: further positive evidence. The Journal of Portfolio Management, 8(1), 36-39.
* Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
* Esfahanipour, A., & Mousavi, S. (2011). A genetic programming model to generate risk-adjusted technical trading rules in stock markets. Expert Systems with Applications, 38(7), 8438-8445.
* Fama, E. F., & Blume, M. E. (1966). Filter rules and stock-market trading.The Journal of Business, 39(1), 226-241.
* Gorgulho, A., Neves, R., & Horta, N. (2011). Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert systems with Applications, 38(11), 14072-14085.
* Lin, X., Yang, Z., & Song, Y. (2011). Intelligent stock trading system based on improved technical analysis and Echo State Network. Expert systems with Applications, 38(9), 11347-11354.
* Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
* Mohammadi, Sh. (2004). Technical analysis in Tehran security exchange market. Jurnal of financial research, (17): 97-129. (in persian)
* Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin.
* Papadamou, S., & Stephanides, G. (2007). Improving technical trading systems by using a new MATLAB-based genetic algorithm procedure.Mathematical and computer modelling, 46(1), 189-197.
* Park, C. H., & Irwin, S. H. (2004). The profitability of technical analysis: A review.
* Potvin, J. Y., Soriano, P., & Vallée, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, 31(7), 1033-1047.
* Radeerom, M. (2014). Building a Trade System by Genetic Algorithm and Technical Analysis for Thai Stock Index. In Intelligent Information and Database Systems (pp. 414-423). Springer International Publishing.
* Shiller, R. J. (1987). Investor behavior in the October 1987 stock market crash: Survey evidence.
* Silva, A., Neves, R., & Horta, N. (2015). A hybrid approach to portfolio composition based on fundamental and technical indicators. Expert Systems with Applications, 42(4), 2036-2048.
* Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. Journal of international Money and Finance, 11(3), 304-314.
Wang, F., Philip, L. H., & Cheung, D. W. (2014). Combining technical trading rules using particle swarm optimization. Expert Systems with Applications, 41(6), 3016-3026.