本研究利用資料探勘及人工智慧技術，藉由股票市場已過的資料來建立分析模型，以協助投資決策。影響股市的關鍵因素主要可分為心理面、基本面與技術面。本論文將以技術分析為主，藉由對台股大盤歷史資料的學習，運用過濾法則將其原始趨勢修正為可信度較佳的趨勢—稱為PARK。根據PARK趨勢之價格波動與存續時間來分析其統計量，並結合模糊聚類與灰色預測等方法，將所有PARK多空區段中交易時點的風險等級決定出來。最後再搭配不同的操作策略執行部位控管，發展出一系列低風險、高報酬之可行方案來擊敗市場。
經實驗結果顯示出三項重點：第一，藉由過濾法則所產生PARK趨勢的績效遠比原始趨勢資料的績效佳；第二，經由GMM風險評估後的短期策略部位比重愈重，則績效愈好；第三，在交易訊號可信度高的情況下，積極型的操作策略比保守型的更能獲得高利潤。

This research adopts techniques of data mining and artificial intelligence to build up analysis models from the historical data of stock market to assist investment decisions. The effective factors of stock market can be identified with Psychologicals, Fundamentals, and Technicals. This thesis primarily focuses on the technical analysis and refines the raw trend as the more reliable one — called as “PARK” by using filter rules learned from the past Taiex data. The risk level of each trading point in the PARK bull/bear sectors would be determined according to the statistics of price fluctuation and time duration, as well as the methods of fuzzy clustering and grey prediction. Finally, a series of feasible solutions with low risk and high profit would be developed by integrating various strategies for position control and then beat the market.
The experimental results show three points: First of all, the performance based on PARK trend would be better than that on raw trend. Secondly, the more percentage the GMM-based short-term strategies occupy, the better performance the total strategies would obtain. Thirdly, the aggressive strategies would be better than that of the conservative ones if the trading signals are trusty.

[1] John J. Murphy, “Technical Analysis of the Futures Markets – A Comprehensive Guide to Trading Methods and Applications” , New York Institute of Finance, 1986.
[2] Martin J. Pring , “Technical Analysis Explained – The Successful Investor’s Guide to Spotting Investment Trends and Turning Points” 2nd Edition, 1985, McGraw-Hill.
[3] Woody Dorsey, “Behavioral Trading : Methods for Measuring Investor Confidence, Expectations and Market Trends”, 2003.
[4] L.A. Zadeh, “From Circuit Theory to System Theory”, Proceedings of the Institute of Radio Engineers 50, pp. 856-865, 1962.
[5] L.A. Zadeh, “Fuzzy Sets”, Information and Control 8, pp. 338-353, 1965.
[6] R.E. Bellman and L.A. Zadeh, “Decision-making in a Fuzzy Environment”, Management Science 17, pp. B141-B164, 1970.
[7] L.A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes”, IEEE Transactions on Systems, Man & Cybernetics SMC-3 ,pp. 28-44, 1973.
[8] E.H. Mamdani, “Applications of Fuzzy Alogorithms for Control of a Simple Dynamic Plant”, Proceedings of the Institute of Electrical Engineers 121, pp. 1585-1588, 1974.
[9] L.A. Zadeh, “The concept of linguistic variable and its application to approximate reasoning”, Information Science, 1975.
[10] L.A. Zadeh, “PRUF – A Meaning Representation Language for Natural Languages”, in: E.H. Mamdani and B.R. Gaines, eds., Fuzzy Reasoning and its Applications, pp. 1-66, 1981, (Academic Press, London).
[11] Deng Julong, “Control problems of Grey System, System and Control Letters”, vol.5, pp. 288-294, 1982.
[12] H.J. Zimmermann and P.Zysno, “Latent Connectives in Human Decision Making”, Fuzzy Sets and Systems 4 , pp. 37-51, 1980.
[13] H.J.Zimmermann,L.A.Zadeh,B.R.Gaines “Fuzzy Sets and Decision Analysis” 1983, North Holland.
[14] Bernadette Bouchon-Meunier,Ronald R.Yager,Lotfi A.Zadeh “Fuzzy Logic and Soft Computing” 1995, World Scientific.
[15] A.A. Markov, “Extension of the limit theorems of probability theory to a sum of variables connected in a chain”, Notes of The Imperial Academy of Sciences of St Petersburg, VII Series, Classe Physico-Mathematique, vol. XXII, no. 9, 1907.
[16] A. Kolmogorov, “Űber die analytischen Methoden in der Wahrscheinlich Keitsrechnung”, Math. Ann., vol. 104, pp. 415-458, 1931.
[17] George Wright, Peter Ayton “Judgmental Forecasting” 1987, JohnWiley & Sons.
[18] Louise Yamada “Market Magic – Riding the Greatest Bull Market of the Century” 1998, JohnWiley & Sons.
[19] Justine Gregory-Williams, Bill M. Williams “Trading Chaos -- 2nd Edition” 2004, JohnWiley & Sons.
[20] Price Headley “Big Trends in Trading – Strategies to Master Major Market Moves” 2002, JohnWiley & Sons.
[21] Richard W. Arms “Trading Without Fear” 1996, JohnWiley & Sons.
[22] Jon Leizman,Ph.D. “Short-Term Trading, Long-Term Profits” 2001, McGraw-Hill.
[23] Robert Pardo “Design, Testing, and Optimization of Trading Systems” 1992, JohnWiley & Sons.
[24] Lars N. Kestner “Quantitative Trading Strategies” 2003,McGraw-Hill.
[25] E.Y. Kang, ” Grey Modeling of Taiwan Stock Market”, the master thesis, Department of Electric and Electronic Engineering, National Taiwan University of Science and Technology, 1996.
[26] C.B. Lin, S.F. Su, and Y.T. Hsu, “High Precision Forecast using Grey Models,” International Journal of Systems Science, vol. 32, no. 5, pp. 609-619, 2001.
[27] Y.T. Hsu, H.C. Cheng and C.B. Lin, “A Long-Term Prediction using GMs,” The Journal of Grey System, vol. 12, no. 1, pp. 41-54, 2000.
[28] Y.T. Hsu, C.B. Lin, and H.C. Cheng, “Grey Model Selections based on Fuzzy Trend Prediction,” Journal of Fuzzy Mathematics, vol. 7, no.4, pp. 897-905, 1999.
[29] Y.T. Hsu and E.Y. Kang, “Grey Modeling of Moving Average Index in Taiwan Stock Market,” The Journal of Grey System, vol. 10, no. 3, pp. 215-223, 1998.
[30] Y.T. Hsu, G.G. Lin and Y.M. Chou “Predictions of Tops and Bottoms of Stock Market based on Grey Clustering Method” The Joint Conference on AI, Fuzzy System, and Grey System, 2003.
[31] Y.T. Hsu , M.C. Liu, H.C. Chao, “Prediction of Taiex based on Grey Hidden Markov Model”, The 5th Conference on Artificial Intelligence and Applications, 2004.
[32] C.C. Lee, ” Applying Learning Fuzzy Classifier System in Stock Technical Analysis”, the master thesis, Institute of Information Management, National Chiao Tung University, 2003.
[33] E.Y. Kang, ” Grey Modeling of Taiwan Stock Market”, the master thesis, Department of Electric and Electronic Engineering, National Taiwan University of Science and Technology, 1996.