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Forecasting Stock Market Using Wavelet Transforms and Neural Networks: An integrated system based on Fuzzy Genetic algorithm (Case study of price index of Tehran Stock Exchange) | ||
| International Journal of Finance, Accounting and Economics Studies | ||
| مقاله 6، دوره 2، شماره 3 - شماره پیاپی 7، دی 2012، صفحه 83-94 اصل مقاله (577.69 K) | ||
| نوع مقاله: Research Paper | ||
| نویسندگان | ||
| Ali Asghar Anvary Rostamy* 1؛ Nor addin Mousazadeh Abbasi2؛ Mohammad Ali Aghaei3؛ Mahdi Moradzadeh Fard4 | ||
| 1Professor, Accounting and Finance Department, Faculty of Management and Economics, Tarbiat Modares University (TMU). | ||
| 2Master in Accounting, Faculty of Management and Economics, Tarbiat Modares University (TMU). | ||
| 3Assistant Professor, Accounting and Finance Department, Faculty of Management and Economics, Tarbiat Modares University | ||
| 4Assistant Professor, Accounting and Finance Department, Islamic Azad University, Karaj Branch. | ||
| چکیده | ||
| The jamor purpose of the present research is to predict the total stock market index of Tehran Stock Exchange, using a combined method of Wavelet transforms, Fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers. To do so, first the prediction was made by neural network, then a series of price index was decomposed by wavelet transform and the prediction made by neural network was repeated, finally, the extracted pattern from the neural network was stated through discernible rules using Fuzzy theory. The main focus of this paper is based on a theory in which investors and traders achieve a method for predicting stock market. Concerning the results of previous researches, which confirmed the relative superiority of non-linear models in price index prediction, an appropriate model has been offered in this research by combining the non-linear methods such as Wavelet transforms, Fuzzy genetics, and neural network, The results indicated the superiority of the designed system in predicting price index of Tehran Stock Exchange. | ||
| کلیدواژهها | ||
| Artificial Neural Network؛ Wavelet Transforms؛ Genetic algorithm؛ Fuzzy Theory and Fuzzy Genetic System | ||
| مراجع | ||
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* Abraham,A., & B. Nath, P.K. Mahanti.(2001). Hybrid intelligent systems for stock market analysis, in: V.N. Alexandrov, J. Dongarra, B.A. Julianno, R.S. Renner, C.J.K. Tan (Eds.), Computational Science, Springer-Verlag, Germany, pp. 337–345.
* Abramovich,F. & Besbeas,P.& Sapatinas,T.(2002). Empirical Bayes approach to block wavelet function estimation. Computational Statistics and Data Analysis , 435–451.
* Adam, A,M. & Twenboah, G. (2008). Macroeconomic factors and stock market movement: Evidence Ghana, Monich Personal RePEc Archive.
* Aiken, M., & Bsat, M. (1999). Forecasting market trends with neural networks. Information Systems Management, 16(4), 42–48.
* Anderson J.A. ( 1996). Neural models with cognitive implications. In Basic Processes in Reading Perception and Comprehension Models, pp.27-90.
* Chen, T,L.,& Cheng, C,H.,& Teoh, H, Jong. (2007). Fuzzy time-series basedon Fibonacci sequence for stock price forecasting. Physica A 380, 377–390.
* Chang, P, C., & Liu, C, H. (2008). A TSK type fuzzy rule based system for stock price prediction, Expert Systems with Applications 34, 135–144.
* Chang, P. C., Chen, H.L., & Jun, L.L., & Chin, Y.F,, & Celeste, S.P.Ng.(2009) . A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications,36,6889-6898.
* Chang, P. C., Wang, Y. W., & Yang, W. N. (2004). An investigation of the hybrid forecasting models for stock price variation in Taiwan. Journal of the Chinese Institute of Industrial Engineering, 21(4), 358–368.
* Chi, S. C., Chen, H. P., & Cheng, C. H. (1999). A forecasting approach for stock index future using Grey theory and neural networks. In IEEE international joint conference on neural networks (pp. 3850–3855).
* Gay, R,D.(2008). Effect of macroeconomic variable on stock market returns for four emerging economies: Brazil, Russia, India and China, International Business and Economics Research Journal 7, 8.
* Gan, C., & Lee, M., &Yong, H.H.A, & Zhang, I. (2006). Macroeconomic variables and stock market interactions: New Zealand evidence. Investment management and financial innovations,3.
* Gencay,R. & Selcuk,F. & Whitcher,B.(2002). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press, New York .
* Goldberg,D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning , Addison-Wesley.
* George,S,A., & Kimon,P,V.(2009) .Surveying stock market forecasting techniques – Part II: soft computing methods, Expert Syst. Appl. 36, 5932–5941.
* Hadavandi,E.,& H, Shavandi , A, Ghanbari. (2010). Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowledge based system 23, 800-808.
* Haupt, R,L., & Haupt, S,E. (1980). Practical Genetic Algorithms. Second Edition.
* Hung, J,C.(2009).A fuzzy GARCH model applied to stock market scenario using a genetic algorithm, Expert Syst. Appl. 36, 11710–11717.
* Huang, S. (2002). Extract intelligible and concise fuzzy rules from neural networks. Fuzzy Sets and Systems, 132, 233-243.
* Ibrahim, M.H. & Aziz, H. (2003). Macroeconomic Variable and the Malaysian Equity market: A view through rolling su sample. Journal of Economic studies,30,22.
* Khashei,M., & M. Bijaria, G.A. Ardali.(2009). Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing 72, 956–967.
* Kimoto, T., & Asakawa, K. (1990). Stock market prediction system with modular neural network. IEEE International Joint Conference on Neural Network, 1, 1–6.
* Kuo, R. J., Chen, C. H., & Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118, 21–24.
* Lee, J. W. (2001). Stock price prediction using reinforcement learning. IEEE International Joint Conference on Neural Networks, 1, 690–695.
* Lin, L., & Cao, L., &Wang, J.,& Zhang, Ch. (2007).The Application of Genetic Algorithms in Stock Market Data Mining Optimization, Faculty of Information Technology, University of Technology, Sydney, NSW, Australia.
* Majhi,R.,& Panda,G., & Sahoo, G.(2009). Development and performance evaluation of FLANN based model for forecasting of stock markets, Expert Syst. Appl. 36, 6800–6808.
* Mantas, C. (2006). Extraction of similarity based fuzzy rules from artificial neural networks. International Journal of Approximate Reasoning, 43, 202-221.
* Patterson, D.W.(1996). Artificial Neural Networks: Theory and Applications, Prentice Hall.
* Popoola,A. & Ahmad,K. (2006).Testing the suitability of wavelet preprocessing for TSK fuzzy models. in: Proceeding of FUZZ-IEEE: International Conference Fuzzy System Network , pp.1305–1309.
* Ramsey,J,B. (1999). The contribution of wavelets to the analysis of economic and financial data. Philosophical Transactions of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences ,2593–2606.
* Tang, J, H., & H. F, Hsiao,& W. C, Yeh. (2010). Forecasting Stock Market Using Wavelet Transform and neural networks: An Integrated system based on artificial bee colony alghoritm, Applied soft computing , Gmodel.
* Wang, Y,F.(2002). Predicting stock price using fuzzy grey prediction system, Expert Syst. Appl. 22, 33–39.
* Yao, J., & Poh, H. L. (1995). Forecasting the KLSE index using neural networks. IEEE International Conference on Neural Networks, 2, 1012–1017.
* Yoon, Y., & Swales, J. (1991). Prediction stock price performance: A neural network approach. Proceedings of twenty-fourth annual Hawaii international conference on system science, 156–162. | ||
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