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Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/22684
標題: 基於移動平均分析技術實作類神經網路系統於股價預測
A neural network system implementation of stocks price forecasting model based on MA analysis technique
作者: 廖文榮
Liao, Wen-Jung
關鍵字: stocks forecasting;股票預測;technique analysis;neuro-fuzzy network;技術分析;模糊類神經網路
出版社: 電子商務研究所
引用: Armstrong, J.S. and Fildes, R., 1995. Correspondence: On the selection of error measures for comparisons among forecasting methods. Journal of Forecasting, 14(1), 67-71. Bachelier, L. 1900. Théorie de la Spéculation. Paris: Gauthier-Villars. Baxt, W. G., 1990. Use of an artificial neural network for data analysis in clinical decision making: The diagnosis of acute coronary occlusion. Neural Computing, 2(4), 480-489. Chen, S.-R., and Wu, B., 2003. On optimal forecasting with soft computation for nonlinear time series. Fuzzy Optimization and Decision Making, 2(3), 215-228. Chou, Y.-L, 1975. Statistical Analysis. New York: Holt, Rinehart, and Winston. Clements, M. P. and Hendry, D. F., 1993. On the limitations of comparing mean square forecast errors. Journal of Forecasting, 12(8), 617 - 637. Cootner, P., 1964. The Random Character of Stock Market Prices. Cambridge: Massachusetts Institute of Technology Press. Davalo E. and Naim P., 1991. Neural Networks. 1st ed. London: Macmillan Demir, Y., and Ucar, A.., 2003. Modeling and simulation with neural and fuzzy-neural networks of switched circuits. Compel, 22(2), 253-272. De Bondt, W. F. M. and Thaler, R., 1985. Does the stock market overreact. The Journal of Finance, 40(3), 793-805 Dissanaike, G., 1997, Do stock market investors overreact. Journal of Business Fiance & Accounting, 24(1), 27-50 Fama, E., 1965. Random Walks in Stock Market Prices. Financial Analysts Journal, 21(5), 34 - 109. Fama, E., 1970. Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383 - 417. Fausett, L., 1994. Fundamentals of Neural Networks: Architecture, Algorithms and Applications. New Jersey: Prentice Hall. Freisleben, B., 1992, Stock market prediction with backpropagation networks, Industrial and Engineering Applications of Artificial Intelligence and Expert System, Proceedings of the 5th International Conference in Paderborn, Germany, 451-460. Gencay, R. and Stengos, T., 1998. Moving Average Rules, Volume and the Predictability of Security Returns with Feedforward Networks. Journal of Forecasting, 17(5-6), 401 - 414. Güler, I. and Übeyli, E.D., 2003. Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network. Computers in Biology and Medicine, 33(4), 333-343. Haykin, S., 1999. Neural Network: A Comprehensive Foundation. New York: Macmillan College Publishing Company. Hiemstra, Y., 1995. Modeling structured nonlinear knowledge to predict stock market returns. In: Trippi R. R., ed. Chaos and nonlinear dynamics in the financial markets: Theory, Evidence and Applications, Chicago: Irwin. 163-175. Jang, J. S. R., 1993. ANFIS:Adaptive-Network-Based Fuzzy Inference System. IEEE Transaction on System, Man and Cybernetics, 23(3), 665 - 685 Kuo, R. J., 2001. A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm. European Journal of Operational Research, 129(3), 496-517. Makridakis, S., Wheelwright, S.C. and McGee, V.E., 1983. Forecasting: Methods and Applications. 2nd ed. New York: John Wiley & Sons. Malhotra, R. and Malhotra, DK, 2002. Differentiating between good credits and bad credits using neuro-fuzzy systems. European Journal of Operational Research, 136(1), 190-211. McCulloch W. and Pitts W., 1943.A logical calculus of the ideas immanent in nervous activity. Bull. of Math. Biophysics, 5, 115-133. Schöneburg, E., 1990. Stock prediction using neural network: A project report. Neurocomputing, 12(1), 12-27 Sfetsos, A. and Siriopoulos, C., 2002. A hybrid clustering scheme for time series forecasting. Proceedings of the 8th International Conference of Society for Computational Economics, 1-11. Tompkins R., 1994. Options Explained. New York: Macmillan. Tsaih, R., Hsu, Y. and Lai, C. C., 1998. Forecasting S & P500 stock index futures with a hybrid AI system. Decision Support systems, 23(2), 161 - 174. Zadeh, L. A., 1965, Fuzzy sets, Information and Control, 8(3), 338-353. Zhang, W., Cao, Q. and Schniederjans MJ., 2004. Neural network earnings per share forecasting models: a comparative analysis of alternative methods. Decision Sciences, 35(2), 205-236.
摘要: 
本論文目的是基於模糊類神經網路架構實作交易系統，以獲得較佳的指數股票型基金之預測。本研究整合了股票分析系統所需要的各種元件，透過元件的彼此呼叫，建立一個彈性且可以動態調整的模糊類神經網路系統。實作的自動化系統可以達到分析動態的參數。根據過去實證結果之顯示，當透過基於技術分析原則的移動平均指標在指數股票型基金時，提供了一個強烈的非線性證據。因此當效率市場存在時，可以透過充足的資訊處理和策略的學習系統對指數股票型基金做有效的預測，這表示在動態模型上，運用模糊類神經模型方法可以提供投資者賺取更高的投資報酬。

This paper develops a model of a trading system by using neuro-fuzzy framework in order to better predict the stock market index. This research is proposed to integrate every component in the stock analysis system. For this reason, the analysis system is a flexible and adjustable through calling by each component. Dynamic parameters can be automatically analyzed the model developed in the research. The empirical results show strong evidence of nonlinearity in the stock market index by using moving average based technical trading rules. Theoretically our approach shows that the neuro-fuzzy model may allow investors to earn higher returns in the dynamic model when profit making opportunities exist and can be exploited with an efficient information processing and learning system strategically.
URI: http://hdl.handle.net/11455/22684
其他識別: U0005-1308200801301400
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