پیش بینی شاخص بورس اوراق بهادار تهران با استفاده از شبکه های عصبی
محورهای موضوعی : مدیریت بازرگانیبیتا دلنواز اصغری 1 , میر فیض فلاح شمس 2
1 - کارمند دانشگاه آزاد اسلامی واحدتبریز و دانش آموخته کارشناسی ارشد مدیریت مالی
2 - استادیارگروه مدیریت مالی ، واحد تهران مرکزی، ،دانشگاه آزاد اسلامی،تهران،ایران
کلید واژه: پیش‌بینی, شبکه‌های عصبی, شاخص سهام, سری‌های زمانی,
چکیده مقاله :
اندازه و روند شاخص‌های قیمت سهام یکی از مهمترین عوامل تاثیرگذار بر تصمیمات سرمایه گذاران در بازارهای مالی می‌باشد. جهت پیش‌بینی بازار از تکنیکهای مختلفی استفاده شده است که معمول‌ترین آنها روشهای رگرسیون و مدل‌های 3ARIMA هستند اما این مدل‌ها در عمل جهت پیش‌بینی بعضی از سریها ناموفق بوده‌اند. در تحقیق حاضر برای پیش‌بینی شاخص کل بورس از مدل شبکه‌های عصبی پیش خور4 با قانون یادگیری پس انتشار خطا5 در سه ساختار شبکه با الگوهای متفاوت ورودی استفاده گردید و نتایج مدل با نتایج مدل‌های رگرسیون چند متغیره و مدل‌های ARIMA مورد مقایسه قرار گرفت. نتایج تحقیق نشان داد که روش شبکه‌های عصبی خطای RMSE به میزان قابل توجهی کمتر از RMSE روشهای دیگر است و در بازار بورس اوراق بهادارتهران پیش‌بینی کوتاه مدت با فاصله زمانی کمتر، مناسب‌تر از پیش‌بینی بلند مدت با فاصله زمانی طولانی تر است. 3. autoregressive integrated moving average 4. Feed Forward Neural network 5. back propagation
The size and process of the stock price indices are among the most important factors affecting the decisions of the investors in the financial markets. In order to predict the market, different techniques have been used, the most common of which are regression methods and ARIMA models. However, these models have been unsuccessful in the practical prediction of some series. In the present research, in order to predict the total index of the stock, the Feed Forward Neural Network model with the law of back propagation was used in three networks with different input models, and the results of the model were compared to the result of multi – variable regression models and ARIMA models. The results indicated that the neural network method showed considerably fewer RMSE errors than RMSE errors in other methods, and that in Tehran stock market short – term prediction within a shorter interval is more suitable than long – term prediction within a longer interval.
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Beaver, W. (1981) Financial Reporting; An Accounting Revolution.
Bishop, M. C. (1997). Neural Networks for Pattern Recognition. Oxford University Press.
Bollereslev, T. (1986). Generalized Autoregressive ConditiQnal Heteroskedasticity. Journal of Ecocnometrics, 31.
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Bot Shekan, M. H. (2003). Recognition of Stock Price Indices in Stock Exchanges and Designing a New Bindex Index in Tehran Stock Exchange. Journal of Accounting Studies, 4, (In Persian).
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Fedaye Nejad, M. E. (2004). Understanding the Dimensions of the Financial System in the United Kingdom. Tehran: Financial Research Magazine, 13 & 14, (In Persian).
Fu, J. (1998). A Neural Network Forecast of Economic Growth and Recession. The Journal of Economics, 1.
Haefke, C., & Helmenstein, C. (1996). Neural Networks in the Capital Markets: An Application to Index Forecasting. Computational Economics, 9.
Hendriksen, E. F., & Vanbreda, M. F. (1994). Accounting Theory. Irwin:Chicago Publication.
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Hiemstra, Y. (1996). Linear Regression versus Backpropagation Networks to Predict Quarterly Stock Market Excess Returns. Computational Economics, 9.
Hill, T., Marques, L., O'Connor, M., & Remus, W. (1996). Artificial Neural Networks Models for Forecasting and Decision Making. International Journal of Forecasting, 10.
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Kuan, C. M., & White, H. (1994). Artificial Neural Networks: An Economics Perspective. Econometrics Reviews, 13.
Kutsurelis, I. E. (1998). Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy. Master Thesis.
Lin, C. S., Alikhan, H., & Huang, C. (2002). Can the Neuro Fuzzy Model Predict Stock Indexes better than its Rivals. University of Denver, Denver.
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Parker, B. B. (1985). Learning Logic: Casting the Cortex of Human Braining in Silicon.
Perron, P. (1989). The Great Crash, the Oil Price Shock and the Unit Root Hypothesis. Econometrica, 57.
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Refenes, A. N., Zapranis, A., & Gavin, F. (1995). Modelling Stock Returns in the Framework of APT: Comparative Study with Regression Models. Neural Networks in the Capital Markets.
Rosenblat, F. (1962). Principles of Neuro Dynamics. Washington DC: Sputan Press.
Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Pshchological Review, 65.
Rumelhart, D. E., Hinton, G. E., & William, R. J. (1988). Learning Representation by Back-Propagation Error. Nature, 323.
Stevenson, W. I., & Hojati, M. (2003). Production and Operation Management.
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Tsibouris, G., & Zeidenberg, M. (1995). Testing the Efficient Markets Hypothesis with Gradient Descent Algoritms. Neural Networks in the Capital Markets.
Verkooijen, W. (1996). A Neural Network Approach to Long-Run Exchange Rate Prediction. Computational Economics, 9.
Werbos, P. J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science. Phd Thesis, Cambridge: Harvard University.
White, H. (1989). Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. Proceeding of IEEE International Conference on Neural NetworksIl.
Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits.
William, F., Gorden, A. J., & Jeffry, B. (1999). Investments. Sharpe Prentice Hall.
Wong, F. S. (1990). Time Series Forecasting Using Backpropagation Neural Networks. Neurocomputing, 2.
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Alborzi, M. (2001). Introduction to Neural Networks. Tehran: Scientific Publication Institute, (In Persian).
Beaver, W. (1981) Financial Reporting; An Accounting Revolution.
Bishop, M. C. (1997). Neural Networks for Pattern Recognition. Oxford University Press.
Bollereslev, T. (1986). Generalized Autoregressive ConditiQnal Heteroskedasticity. Journal of Ecocnometrics, 31.
Bosarge, W. E. (1993). Adaptive Processes to Explain the Nonlinear Structure of Financial Market. Probus Publishing.
Bot Shekan, M. H. (2003). Recognition of Stock Price Indices in Stock Exchanges and Designing a New Bindex Index in Tehran Stock Exchange. Journal of Accounting Studies, 4, (In Persian).
Box, J., & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco.
Brown, K. C., & Reiilly, F. K. (2002). Investment Analysis & Portfolio Management. Dryden Publication.
Chen, A., Leung, M. T., & Dauk, H. (2003). Application of Neural Networks to an Emerging Financial Market; Forecast and Trading the Taiwan Stock Index. Computer and Operation Research, 30.
Davani, Gh. (2003). Exchange, Stock and How to Price. Tehran: Nokhostin Publication, (In Persian).
Ekbatani, M. A. (1994). Stock Price Index. Tehran: Tehran Securities and Exchange Organization, (In Persian).
Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK. Econometrica, 50.
Fadaei Nejad, M. E. (2003). Investigating Capital Market Performance in Tehran Stock Exchange. Phd Thesis, Tehran: Tehran University, (In Persian).
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Emiricial. Journal of Finance, 27.
Fama, E. F. (1990). foundation of Finance. New York: Basic Books Publication.
Fedaye Nejad, M. E. (2004). Understanding the Dimensions of the Financial System in the United Kingdom. Tehran: Financial Research Magazine, 13 & 14, (In Persian).
Fu, J. (1998). A Neural Network Forecast of Economic Growth and Recession. The Journal of Economics, 1.
Haefke, C., & Helmenstein, C. (1996). Neural Networks in the Capital Markets: An Application to Index Forecasting. Computational Economics, 9.
Hendriksen, E. F., & Vanbreda, M. F. (1994). Accounting Theory. Irwin:Chicago Publication.
Heydari, Sh. (2002). Prediction of Stock Index in Tehran Stock Exchange based on the Theory of Inaccurate Collections. Master Thesis, Tehran: Tarbiat Modares University, (In Persian).
Hiemstra, Y. (1996). Linear Regression versus Backpropagation Networks to Predict Quarterly Stock Market Excess Returns. Computational Economics, 9.
Hill, T., Marques, L., O'Connor, M., & Remus, W. (1996). Artificial Neural Networks Models for Forecasting and Decision Making. International Journal of Forecasting, 10.
Hooman, A. (1995). Understanding the Scientific Method in Behavioral Sciences (Haley Research), (In Persian).
Jahangir Ordi, V. (2004). Investigating the Capital Market Performance in Iran. Master Thesis, Tehran: Shahid Beheshti University, (In Persian).
Januskevicius, M. (2003). Testing Stock Market Efficiency Using Neural Networks: Case of Lithuania. Master Thesis, Riga: Stockholm School of Economics.
Jenson, M. C. (1998). Some Anomalous Evidence Regarding Market Efficiency. Journal of Financial Economics, 6.
Johari, H. (2005). Efficiency Evaluation of Tehran Stock Exchange Index. Master's Thesis, Mashhad: University of Mashhad, (In Persian).
Kohzadi, N., Boyd, M. S., Kaastra, I., Kermanshahi, B. S., & Scuse D. (1995). Neural Networks for Forecasting: An Introduction. Canadian Journal of Agricultural Economics, 43.
Kuan, C. M., & White, H. (1994). Artificial Neural Networks: An Economics Perspective. Econometrics Reviews, 13.
Kutsurelis, I. E. (1998). Forecasting Financial Markets Using Neural Networks: An Analysis of Methods and Accuracy. Master Thesis.
Lin, C. S., Alikhan, H., & Huang, C. (2002). Can the Neuro Fuzzy Model Predict Stock Indexes better than its Rivals. University of Denver, Denver.
Modares, A. (2006). Investigating the Application of Multivariate Time Modeling in Forecasting Operating Cash Fows. Phd Thesis, Tehran: Allameh Tabataba'i University, (In Persian).
Momenagh, M. B. (2000). Fundamentals of Neural Networks (Computational Intelligence). Tehran: Amir Kabir University of Technology, (In Persian).
Montgomery, C. D., Lynwood, A. I., & Gardiner, J. S., (1990). Forecasting Time Series Analysis. Mc Grow-Hill.
Moody, I., Levin, U., & Rehfoss, S. (1993). Predicting of U.S. Index of Industrial Production. Neural Network World, 3.
Moshiri, S., & Cameron, N. (2000). Neural Networks versus Econometric Models in Forecasting Inflation. Journal of Forecasting, 19.
Moshiri, S., Cameron, N., & Scuse, D. (1999). Static, Dynamic and Hybrid Neural Networks in Forecasting Inflation. Computational Economics, 14.
Nelson, C., & Plosser, C. (1982). Trends and Random Walks in Macroeconomics Time Series: Some Evidence and Implications. Journal of Monetary Economics, 10.
Parker, B. B. (1985). Learning Logic: Casting the Cortex of Human Braining in Silicon.
Perron, P. (1989). The Great Crash, the Oil Price Shock and the Unit Root Hypothesis. Econometrica, 57.
Pitam, A. R. (2001). Prediction of Stock Prices Using Fuzzy Neural Systems. Master Thesis, Shiraz: Shiraz University, (In Persian).
Refenes, A. N., Zapranis, A., & Gavin, F. (1995). Modelling Stock Returns in the Framework of APT: Comparative Study with Regression Models. Neural Networks in the Capital Markets.
Rosenblat, F. (1962). Principles of Neuro Dynamics. Washington DC: Sputan Press.
Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Pshchological Review, 65.
Rumelhart, D. E., Hinton, G. E., & William, R. J. (1988). Learning Representation by Back-Propagation Error. Nature, 323.
Stevenson, W. I., & Hojati, M. (2003). Production and Operation Management.
Tehran Stock Exchange. (1997). Display in Tehran Stock Exchange, Concepts and Methods, (In Persian).
Tehran Stock Exchange. (1998). A Framework for Measuring 50 more Active Companies in the Tehran Stock Exchange. Journal of Tehran Stock Exchange, 10, (In Persian).
Trippi, R. R., & Turban, E. (1998). Auto Learning Approaches for Building Expert System, Copmuter and Operations Research17.
Trippi, R. R., & Turban, E. (1996). Neural Networks in Finance and Investing.
Tsibouris, G., & Zeidenberg, M. (1995). Testing the Efficient Markets Hypothesis with Gradient Descent Algoritms. Neural Networks in the Capital Markets.
Verkooijen, W. (1996). A Neural Network Approach to Long-Run Exchange Rate Prediction. Computational Economics, 9.
Werbos, P. J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science. Phd Thesis, Cambridge: Harvard University.
White, H. (1989). Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. Proceeding of IEEE International Conference on Neural NetworksIl.
Widrow, B., & Hoff, M. E. (1960). Adaptive Switching Circuits.
William, F., Gorden, A. J., & Jeffry, B. (1999). Investments. Sharpe Prentice Hall.
Wong, F. S. (1990). Time Series Forecasting Using Backpropagation Neural Networks. Neurocomputing, 2.
