簡易檢索 / 詳目顯示

研究生: 蔡晨瑩
Tsai, Chen-Ying
論文名稱: 臺灣50指數ETF整合型分類預測之研究
A Study of the Integrated Classification and Forecasting in Taiwan Top50 Tracker Fund
指導教授: 吳宗正
Wu, Chung-Cheng
學位類別: 碩士
Master
系所名稱: 管理學院 - 統計學系
Department of Statistics
論文出版年: 2005
畢業學年度: 93
語文別: 中文
論文頁數: 92
中文關鍵詞: 適應性網路模糊推論系統類神經網路臺灣50指數ETF統計方法
外文關鍵詞: Statistical method, Artificial Neural Network, Taiwan Top50 Tracker Fund, Adaptive Network-based Fuzzy Inference System
相關次數: 點閱:106下載:4
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  •   本研究係針對臺灣50指數ETF,運用統計方法、類神經網路及適應性網路模糊推論系統等方法,架構八種整合型分類預測模式,探討各模式對於臺灣50指數ETF之分類及預測之績效表現。本研究之整合型分類預測模式可分為二部份,一為前半部-分類模式,一為後半部-預測模式。前半部-分類模式係透過量化技術指標對臺灣50指數ETF價格進行分類判別;後半部-預測模式係由分類變數、當日收盤價、三大法人買賣超、券資比及NASDAQ科技類股指數等變數,對臺灣50指數ETF隔日收盤價進行預測。進而針對所建立之整合型分類預測模式,將其預測結果運用於本研究所建構之交易策略,以模擬該交易策略之報酬。

      本研究之研究範圍為民國92年6月30日至民國94年2月25日。實證結果顯示,在預測誤差方面,模式六(自我組織映射網路、學習向量量化網路及動態倒傳遞神經網路)優於其他模式。在模擬交易報酬率方面,則以模式一(因素分析、集群分析、區別分析及迴歸分析)優於其他模式。

     The study is in connection with Taiwan Top50 Tracker Fund and to establish eight integrated classification and forecasting models by statistical method, artificial neural network, adaptive network-based fuzzy inference system, and so on. It is to probe into the result of these models for Taiwan Top50 Tracker Fund in classind and forecasting. The integrated classification and forecasting model can be divided to two parts, one is for classification, and the other one is for forecasting. For classification, the price of Taiwan Top50 Tracker Fund are classified based on technical indexes; for forecasting, the price of Taiwan Top50 Tracker Fund are forecasted based on the previous closing price, net buy or sell of the institutional investor, short-to-long ratio, NASDAQ index, and so on. Besides, a speculative trading strategy is applied to evaluate the return of theses integrated classification and forecasting models.

     The empirical interval is June 30, 2003 to February 25, 2005. The empirical result shows that: in point of the forecast error, the model I (Self-Organizing Map, Learning Vector Quantization Network, and moving Back-Propagation Network) is better than the other models; in point of the return, the model VI (Factor Analysis, Cluster Analysis, Discriminant Analysis, and Regression Analysis) is better than the other models.

    目錄 I 圖目錄 III 表目錄 IV 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 3 第三節 研究範圍與對象 3 第四節 研究流程 3 第二章 文獻探討 5 第一節 股票價格相關理論 5 第二節 臺灣50指數商品簡介 10 第三節 文獻探討 13 第三章 研究方法 20 第一節 研究架構 20 第二節 統計方法 25 第三節 類神經網路 31 第四節 模糊理論 39 第五節 適應性網路模糊推論系統 42 第四章 實證研究 43 第一節 各模式之建構 43 第二節 模式績效評比 67 第五章 結論與建議 72 第一節 研究結論 72 第二節 研究限制 73 第三節 後續研究建議 73 參考文獻 74 附錄 79

    中文部份
    【01】 王文俊(2001),認識Fuzzy,二版,全華科技圖書。
    【02】 王盈傑(2004),台灣股市動量策略與反向策略之整合研究,國立中正大學國際經濟研究所。
    【03】 王冠閔、黃柏農(2004),臺灣股、匯市與美國股市關聯性探討,臺灣經濟預測與政策,第34卷,第2期,31-72。
    【04】 陳順宇(2004),多變量分析,三版,華泰書局。
    【05】 陳順宇(2004),迴歸分析,三版,華泰書局。
    【06】 張斐章(2003)、張麗秋、黃浩倫,類神經網路:理論與實務,東華書局。
    【07】 張雅雯(2003),以正、逆價差檢驗台灣股票市場效率性,私立逢甲大學企業管理研究所。
    【08】 游淑禎(1998),類神經網路應用於臺灣股市預測:統合基本面與技術面資訊,證券市場發展,第10卷,第3期,97-134。
    【09】 葉怡成(2003),類神經網路模式應用與實作,儒林書局,4-16。
    【10】 葛思惠(2003),指數股票型基金之制度規劃,證券暨期貨管理,第21卷,第6期,15-51。
    【11】 歐宏杰(2003),國際投資月刊。
    【12】 歐宏杰(2004),臺灣ETF市場如何與國際接軌(上),集保月刊,第127期,20-36。
    【13】 蔡依玲(2001),台灣股票市場報酬率之研究,國立成功大學統計學研究所。
    【14】 蘇木春、張孝德(2004),機器學習:類神經網路、模糊系統以及基因演算法則,三版,全華科技圖書。

    英文部份
    【01】 Afifi, A. A., Clark, V. and May, S. (2004), “Computer-Aided Multivariate Analysis,” 4th ed., New York, Chapman & Hall, 285.
    【02】 Balachandher, K. G., Fauzias, M. N. and Lai, M. M. (2002), “An Examination of the Random Walk Model and Technical Trading Rules in the Malaysian Stock Market,” Quarterly Journal of Business and Economics, Vol. 41, Iss. 1/2, 81-104.
    【03】 Bessembiner, H. and Chan, C. (1995), “The profitability of technical trading rules in the Asian stock markets,” Pacific-Basin Finance Journal, Vol. 3, Iss. 2-3, 257-284.
    【04】 Dash, P. K., Liew, A. C. and Rahman, S. (1995), “Peak Load Forecasting Using A Fuzzy Neural Network,” Electric Power Systems Research, Vol. 32, Iss. 1, 19-23.
    【05】 Dawson, E. R. and Steely, J. M. (2003), “On the Existence of Visual Technical Patterns in the UK Stock Market,” Journal of Business Finance & Accounting, Vol. 30, Iss. 1/2, 263-294.
    【06】 Edwards, Robert D. and Magee, John (1992), “Technical analysis of stock trends,” 6th ed., Boston, J. Magee Inc.
    【07】 Eun, C. S. and Shim S. (1989), “International Transmission of Stock Market Movements,” Journal of Financial and Quantitative Analysis, Vol. 24, Iss. 2, 241-256.
    【08】 Fama, E. F. (1970), “Efficient capital market: a view of theory and empirical work,” Journal of Finance, Vol. 25, No. 2, 383-417.
    【09】 Fernandez-Rodriguez, F., Gonzalez-Martel, C. and Sosvilla-Rivero S. (2000), “On The Profitability of Technical Trading Rules Based on Artificial Neural Networks: Evidence from The Madrid Stock Market,” Economic Letters, Vol. 69, Iss. 1, 89-94.
    【10】 Foster, George (1986), “Financial Statement Analysis,” 2nd ed., New York, Englewood Cliffs N.J. Prentices Hall.
    【11】 Gencay, R. (1996), “Non-linear prediction of security returns with moving average rules,” Journal of Forecasting, Vol. 15, Iss. 3, 165-174.
    【12】 Gencay, R. (1998), “The Predictability of Security Returns with Simple Technical Trading Rules,” Journal of Empirical Finance, Vol. 5, Iss. 4, 347-359.
    【13】 Hung, Shin-Yuan, Liang, Ting-Peng and Liu, Victor Wei-Chi. (1996), “Integrating arbitrage pricing theory and artificial neural networks to support portfolio management,” Decision Support Systems, Vol. 18, Iss. 3/4, 301-316.
    【14】 Kaiser, H. F. (1970), “A second generation little Jiffy,” Psychometrika, Vol. 35, Iss. 4, 401-415.
    【15】 Kanas, A. and Yannopoulos, A. (2001), “Comparing linear and nonlinear forecasts for stock return,” International Review of Economics and Finance, Vol. 10, Iss. 4, 383-398.
    【16】 Klir, George J. and Yuan, Bo (1995), “Fuzzy sets and fuzzy logic :/theory and applications,” Upper Saddle River, N.J. :/Prentice Hall PTR.
    【17】 Kosko, Bart (1992), “Neural networks and fuzzy systems :/a dynamical systems approach to machine intelligence,” Englewood Cliffs, NJ :/Prentice Hall.
    【18】 Kuo, R. J., Chen, C. H. and 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, Vol. 118, Iss. 1, 21-45.
    【19】 Kwon, Ki-Yeol and Kish, J. R. (2002), “Technical trading strategies and return predictability: NYSE,” Applied Financial Economics, Vol. 12, Iss. 9, 639-653.
    【20】 Lachtermacher, G. and Fuller, J. D. (1995), “Backpropagation in Time-Series Forecasting,” Journal of Forecasting, Vol. 14, Iss. 4, 381-393.
    【21】 Mills, T. C. (1997), “Technical analysis and the London Stock Exchange: testing trading rules using the FT30,” International Journal of Finance and Economics, Vol. 2, Iss. 4, 319-331.
    【22】 Mizuno, H., Kosaka, M., Yajima, H. and Komoda, N. (1998), “Appliction Of Neural Network To Technical Analysis Of Stock Market Prediction,” Studies in Informatics and Control, Vol. 7, No. 2, 111-120.
    【23】 Palit, A. K. and Popovic, D. (2000), “Nonlinear Combination of Forecasts Using Artificial Neural Network, Fuzzy Logic and Neuro-Fuzzy Approaches,” IEEE International Conference on Fuzzy Systems, 2, 566-571.
    【24】 Pruitt, Stephen W., Tse, K. S. Maurice and White, Richard E. (1992), “The CRISMA Trading System: The Next Five Years,” Journal of Portfolio Management, Vol. 18, Iss. 3, 22-25.
    【25】 Pruitt, Stephen W. and White, Richard (1988), “The CRISMA Trading System: Who Says Technical Analysis Can't Beat the Market?” Journal of Portfolio Management, Vol. 14, Iss. 3, 55-58.
    【26】 Smirlock, M. and Starks, L. (1985), “A Further Examination of Stock Price Changes and Transaction Volume,” Journal of Finance Research, Vol. 8, 217-226.
    【27】 Swales, G. S. and Yoon Y. (1992), “Applying Artificial Neural Networks toInvestment Analysis,” Financial Analysts Journal, Vol. 48, Iss. 5, 78-80.
    【28】 Tam, Kar Yan and Kiang, Melody Y. (1992), “Managerial Applications of Neural Networks: The Case of Bank Failure Predictions,” Management Science, Vol. 38, Iss. 7, 926-947.
    【29】 Tamimi, M. and Egbert, R. (2000), “Short Term Electric Load Forecasting Via Fuzzy Neural Collaboration,” Electric Power System Research, Vol. 56, Iss. 3, 243-248.
    【30】 Wood, D. and Dasgupta, B. (1996), “Classfying Trend Movements in the MCSI U.S.A. Capital Index-A Comparison of Regression, ARIMA and Neural Network Methods,” Computers & Operations Research, Vol. 23, Iss. 6, 611-622.
    【31】 Zemke, S. (1999), “Nonlinear index prediction,” Physica, Vol. 269, Iss. 1, 177-183.

    下載圖示 校內:2006-06-20公開
    校外:2006-06-20公開
    QR CODE