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研究生: 郭修誠
論文名稱: 日內技術交易系統之獲利性研究
The profitability of intra-day technical trading systems in Taiwan futures market:Taiwan stock exchange capitalization weighted stock index
指導教授: 郭維裕
學位類別: 碩士
Master
系所名稱: 商學院 - 國際經營與貿易學系
Department of International Business
論文出版年: 2009
畢業學年度: 97
語文別: 英文
論文頁數: 40
中文關鍵詞: 日內交易系統
外文關鍵詞: Intraday, Technical trading systems
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  • 這篇文章主要是利用三種交易系統測試 2003 年台灣股價加權指數期
    貨的日內資料:移動平均穿越法、賽塔支撐壓力策略、K-D 隨機指標。
    站在當沖者的觀點測試歷史資料的表現,並分別建立停損與停利點控
    制交易中所發生的損失與利得。研究結果發現,在調整交易成本後,
    順勢系統的表現的確可以獲得顯著的利潤且多頭的利潤多於空頭;而
    逆勢系統則無法獲得顯著的利潤。


    This paper tests three kinds of trading strategies: two of them are momentum strategies-MA, Support and Resistance and the other is contrarian strategy - Stochastic Indicator by utilizing the futures contracts on Taiwan Stock Exchange Capitalization Weighted Stock Index in 2003. We test their historical performances of these three strategies in view of the day traders who must close out their positions before the closing in every single trading day. In addition, we combine each of these rules with the so called stop-loss-point and take-profit-point to control our gains and loss on the positions. For the momentum
    strategies, the results suggest that the returns following buy signals are higher than following sell signals. For contrarian strategy, there is no evidence that the returns are positive across all rules. In sum, our results reveal that it still has the possible to gain significant profits in the futures market for the day traders, even after adjusting the transaction costs.

    Contents
    Introduction………………………………………………………….…………........1
    Methodology and Data……………………………………………………………...5
    Data Sources………………………………………………………………………….5
    Technical Trading Rules……………………………………………………………...5
    Transaction Costs…………………………………………………………………….9
    Performance Measures………………………………………………………………10
    Benchmark Strategies……………………………………………………………......10
    Statistical Test and Assumptions…………………………………………………….12
    Empirical Results………………………………………………………………...... 13
    Sample Statistics……………………………………………………………………...13
    Moving Averages……………………………………………………………………14
    K-D Stochastic Indicator…………………………………………………………………..15
    Saitta’s Support and Resistance Strategy………………………………………......16
    Conclusion…………………………………………………………………………..17
    References…………………………………………………………………………... 18
    Tables………………………………………………………………………………..20

    Carol O., 2000, Support for resistance: Technical analysis and intraday exchange rates,
    FRBNY Economic Policy Review, 53-68.
    Christian L. D., and Jia M., 2004, Optimal trading frequency for active asset
    management: Evidence from technical trading rules, Journal of Asset Management, 5,
    5, 305-326.
    Fama, E. F. and Blume, M. E., 1986, Filter rules and stock market trading. Journal of
    Business, 39, 226-241.
    Fama, E.F., 1970, Efficient capital markets: A review of theory and empirical work.
    Journal of Finance, 25, 383-417.
    Ki Y. K., and Richard J. K., 2002, Technical trading strategies and return
    predictability: NYSE, Applied Finance Economics, 12, 639-653.
    Lars K., 2003, Quantitative trading strategies: Harnessing the power of quantitative
    techniques to create a wining trading program, Copyright by McGraw-Hill, Inc.
    Leuthold, R.M., 1972, Random walk and price trends: The live cattle futures market,
    Journal of Finance, 27, 879-889.
    Norbert F., and Ronald M., 1999, Technical analysis in foreign exchange market: A
    cointegration-based approach, Mutinational Finance Journal, 3, 3, 147-172.
    Robert A.W., Thomas H.M., and Keith O., 1985, An investigation of transactions data
    for NYSE stocks. Journal of Finance, 40, 3.
    Sweeney, R. J., 1998, Beating the foreign exchange market. Journal of Finance, 41,
    163-182.
    Taeksoo S., and Ingoo H., 2000, A hybrid system using multiple cyclic decomposition
    methods and neural network techniques for point forecast decision making, The 33rd
    Hawaii International Conference on System Sciences.
    Wai M.F., and Lawrence H.M.Y., 2004, Chasing trends: recursive moving average trading rules and internet stocks, Journal of Empirical Finance, 12, 43-76.
    Wang, G.H.K., J. Yau, and T. Baptiste, 1997, Trading volume and transaction costs in
    futures markets, Journal of Futures Markets, 17, 757-780.
    William B., Josef L., and Blake L, 1992, Simple technical trading rules and the
    stochastic properties of stock returns. Journal of Finance, 7, 5, 1731-1764.

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