:::

詳目顯示

回上一頁
題名:以群體學習為基礎的知識再強化智慧型財務決策支援系統建置—以台灣加權指數內含行為的知識萃取為例
作者:李鍾斌
作者(外文):LI, Jung-Bin
校院名稱:國立交通大學
系所名稱:資訊管理研究所
指導教授:陳安斌
學位類別:博士
出版日期:2006
主題關鍵詞:分類元系統類神經網路多重代理人classifier systemneural networkmulti-agent
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:70
人工智慧歷經多年發展,已有為數眾多的研究將其應用在股市趨勢的預測。然而股市所具有如莫名波動、非線性或近似渾沌的特質長久以來一直是股市投資者及財務研究學者最有興趣的課題。由於過去多數研究所採用的人工智慧模型均以試誤法為基礎進行學習,學習結果不僅效率與準確性不盡理想外,也只能針對變異性不大的封閉型環境進行應用。隨著電腦運算能力不斷的大幅提昇,人工智慧的學習模式應該重新檢視,以充分應用現有的電腦科技、財工數學和計量經濟等理論基礎與相關資源,俾產生最適宜的財務投資決策,來降低人為錯誤、提高學習與決策效率、並擴大獲利機率。
本研究嘗試由傳統學習理論的角度出發,以人類學習行為中群組學習的概念與精神建構一個智慧型的群組學習模型,對台灣股價指數的漲跌趨勢進行分析預測。在資訊科技的應用上本研究採用類神經網路(neural network, NN)以及分類元系統(eXtended Classifier System, XCS)之動態知識學習。由於XCS系統是一個以規則為基準的主動式機械學習(machine learning)系統,並具適應動態環境學習的特性,故可以更貼近人類學習、決策方式及瞭解股票脈動。配合類神經網路進行強化式學習,將系統所發掘的知識規則予以強化確認。此外,研究中還採用多重代理人機制,結合群體學習理論中知識分享的概念提升決策準確率。
本研究在系統效能評估上,以未採用類神經網路強化學習的XCS單一技術指標、單一分類元系統、Buy & Hold,以及銀行6年期定存等操作策略做為對照組進行分析比較,實證結果顯示,本研究所提出的系統不論在預測的準確率、投資累積報酬率等模擬績效均優於對照組的績效表現。
Artificial intelligence has been applied in numerous studies to predict stock trends after years of development. The fluctuating and chaotic nature of the stock market has long been a topic of interest for investors and financial researchers. As most learning models form the past studies were based on trial and error, they produced unsatisfactory performances in terms of efficiency and accuracy, or could only be applied in a closed form environment. Since the power of computers has been improved tremendously in recent years, the learning model of artificial intelligence should also be re-examined to improve decision quality.
This study built an intelligent group learning model based on the group learning concept in human behavior in traditional learning theories. Cooperative learning is widely defined as the process through which a group of individuals interact to achieve their goal. In the fluctuating stock market, investors often have various decision making approaches. This work integrates eXtended Classifier System (XCS) and neural network modules incorporating features such as dynamic learning and group decision making. An empirical study is conducted by comparing the profitability of the proposed system with that of investment strategies based on simple rules with single technical indices, individual learning XCS, buy and hold, and six-year term deposits based on the Taiwan Index. The proposed system demonstrates superior performance in terms of accuracy and the rate of cumulative return.
[1] Alavi, M., “Computer-Mediated Collaborative Learning: An Empirical Evaluation”, MIS Quarterly, June, pp.159-174, 1994.
[2] Beltrametti, L., Fiorentimi, R., Tamborini, R., “A Learning-to-Forcast Experiment on the Foreign Exchange Market with a Classifier System”, Journal of Economic Dynamics and Control, 21, pp.1543-1575, 1997.
[3] Brenner, T., “Can evolutionary algorithms describe learning processes?” Evolutionary Economics, 8, 271-283, 1998.
[4] Butz, M.V., Wilson, S.W., “An algorithmic description of XCS”, Soft Computing – A Fusion of Foundations, Methodologies and Applications, Springer-Verlag GmbH, 6(3-4), pp.144-153, 2002.
[5] Chen, A-P, Chen, Y-C, Tseng, W-C, “Applying Extending Classifier System to Develop an Option-Operation Suggestion Model of Intraday Trading-An Example of Taiwan Index Option”, Lecture Notes in AI, Vol. 3681, pp27-33, 2005.
[6] Chen, A-P, Chen, Y-C, Huang, Y-H, “Applying Two-Stage XCS Model on Global Overnight Effect for Local Stock Prediction“, Lecture Notes in AI, Vol. 3681, pp.34-40, 2005. {4}
[7] Deboeck, G.J., Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, Wiley, 1994.
[8] Erev, I. and Roth, A. E., “On the Need for Low Rationality, Cognitive Game Theory: Reinforcement Learning in Experimental Games With Unique, Mixed Strategy Euilibria,” Mimeo, University of Pittsburgh, 1996.
[9] Fama, E. F., “Efficient Capital Market:A View of Theory and Empirical Work”, Journal of Finance, Vol.25, pp.383-417, 1970.
[10] Farivar, S. H., Developing a Cooperative Learning Program in a Elementary Classroom: Cooperative Study of Innovative and Tradition Middle Teaching and Learning Strategies. University of California, Los-Angeles, 1985.
[11] Granville, Joseph E., Granville’s New Strategy of Daily Stock Market Timing for Maximum Profit , Englewood Cliffs, Prentice Hall Inc, New Jersey, 1976.
[12] Hashemi, R.R., Le Blanc, L.A., Rucks, C.T., Rajaratnam, A., “A Hybrid Intelligent System for Predicting Bank Holding Structures”, European Journal of Operational Research, Vol.109, pp.390-402, 1998.
[13] Holland, J. H., “Processing and processors for Schemata.” Associative information processing, pp.127-146. New York: American Elsevier, 1971.
[14] Holland, J. H., Reitman, J. S. “Cognitive Systems Based on Adaptive Algorithms.” Pattern-directed inference systems. New York: Academic Press, 1978.
[15] Holland, J. H., “Adaptive Algorithms for Discovering and using General Patterns in Growing Knowledge Bases.” International Journal of Policy Analysis and Information Systems, 4(3), pp.245-268, 1980.
[16] Holland, J. H., “A Mathematical Framework for Studying Learning in Classifier Systems.”, Physica D, 2(1-3), pp.307-317, 1986.
[17] Holland, J. H., Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press, 1975.
[18] Huefner, R.L. “Sensitivity Analysis and Risk Valuation”, Decision Science, 1972.
[19] Johnson, D.W., and Johnson, R.T., Cirles of Learning. Alexandria,VA: Association for Supervision and Curriculum Development, 1988.
[20] Johonson, D.W., and Johnson, R.T., What makes cooperative learning work, ERIC Document Reproduction Services No. ED437841, 1999.
[21] Johnson, D. W., Maruyama, G., Johnson, R., Nelson, D., & Skon, L., “Effects of cooperative, competitive, and individualistic goal structures on achievement: A meta-analysis”, Psychology Bulletin, 89(1), 47-62, 1981.
[22] Johnson, R.T., Johnson, D.W., “An Overview of Cooperative Learning”, url: http://www.co-operation.org/pages/overviewpaper.html
[23] Johnson, R. T., & Johnson, D. W., Learning together and alone: Cooperative, competitive, and individualistic learning (4th Ed.). Boston: Allyn & Bacon, 1994.
[24] Kahneman, Daniel, and Tversky, Amos, “Prospect theory:An analysis of decision under risk”, Econometrica, Vol.47(2), pp.263-291, March 1979.
[25] Kovalerchuk, B., Vityaev, E., Data Mining in Finance, Kluwer, 2000.
[26] Lanzi, P. L., Riolo, R. L., “A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999)”, Learning Classifier Systems:from Foundations to Applications, 1813, pp.33-62, Springer-Verlag, Berlin, 2000.
[27] Li, J.-B., Yu, Y.-T., and Chen, A.-P., “Integration of Group Decisions and XCS on Intelligent Financial Decision Support System — an Example of Taiwan Index”, IEEE World Congress on Computational Intelligence (forthcoming), 2006.
[28] Liao, P-Y, Chen, J-S, “Dynamic Trading Strategy Learning Model Using Learning Classifier Systems”, Proceedings of the 2001 Congress on Evolutionary Computation, pp.783-789, 2001.
[29] Mitl�仡ner, J., “Classifier Systems and Economic Modeling”, Proceeding of 1996 Conference on Designing the Future, pp.77-86, 1996.
[30] Openshaw, S., Openshaw, C., Artificial intelligence in Geography, John Wiley and Sons, 1997.
[31] Samuelson, P.A., “Proof that Properly Anticipated Prices Fluctuate Randomly”, Industrial Management Review, 6, pp.41-49, 1965.
[32] Slavin, R. E., Cooperative Learning: Theory, Research, and Practice, pp.1-4, Needhan Heights, MA:Allyn & Bacon, 1995.
[33] Slavin, R.E., Rogers, C., The Social Psychology of the Primary School, N.Y.: KKY, 1990.
[34] Slavin, R. E., Cooperative learning and student achievement. In R. E. Slavin (Ed.) School and Classroom Organization. Hillsdale, N. J.: Lawrence Erlbaum Associates, 1989.
[35] Slavin, R. E., “Synthesis of research on cooperative learning”, Educational Leadership, 48, 71-82, 1991.
[36] Udvari-Solner, A., “A Decision-Making Model for Curricular Adaptations in Cooperative Groups”, Creativity and Collaborative Learning, Baltimore Maryland: Paul H. Brookes Publishing, pp.59-77, 1994.
[37] Wilson, S.W., Goldberg, D.E. “A Critical Review of Classifier Systems.”, Proceedings of the 3rd International Conference on Genetic Algorithms, p.244-255, Morgan Kauffman, 1989.
[38] Wilson, S.W., “ZCS: A zeroth level classifier system.”, Evolutionary Computation, 2, 1, pp.1-18, 1994.
[39] Wilson, S.W., “Classifier Fitness Based on Accuracy.”, Evolutionary Computation, 3,2, pp.149-175, 1995.
[40] Wong, B., Bodnovich, T.A., Selvi, Y., “Neural Network Applications in Business: A Review and Analysis of the Literature (1988-95)”, Decision Support Systems, Vol.19, pp.301-320, 1997.
[41] 林生傳,“群育取向的創新教學”,群育教學與輔導學術研討會,1993。
[42] 施良方,學習理論,麗文文化事業公司,1998。
[43] 陳怡璋,“以認知學習修正XCS建構具知識教育與機械學習之雙智慧型機制—以財務資料預測之知識學習為例”,國立交通大學資訊管理研究所博士論文,2005。new window
[44] 黃政傑、林佩璇,合作學習,五南圖書出版公司,1996。
[45] 葉怡成,類神經網路模式應用與實作,儒林圖書公司,2004。
[46] 劉錫麒,“合作反省思考的數學解題教學模式及其實證研究”,國立台灣師範大學教育研究所博士論文,1991。new window
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
:::
無相關著作
 
QR Code
QRCODE