政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/125529
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  全文笔数/总笔数 : 88866/118573 (75%)
造访人次 : 23556728      在线人数 : 178
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/125529


    请使用永久网址来引用或连结此文件: http://nccur.lib.nccu.edu.tw/handle/140.119/125529


    题名: 單層學習神經網路配合多輸出節點應用於期貨預測
    The Single-hidden Layer Feedforward Neural Networks with Multiple Output Nodes for Futures Forecast
    作者: 鄭玉婕
    Jheng, Yu-Jie
    贡献者: 蔡瑞煌
    Tsaih, Rua-Huan
    鄭玉婕
    Jheng, Yu-Jie
    关键词: 人工神經網絡
    強記、 軟化與整合
    混合人工智能
    期貨預測
    決策支持系統
    Artificial Neural Network
    Cramming and Softening and Integrating
    Hybrid Artificial Intelligence
    Futures Forecast
    Decision Support System
    日期: 2019
    上传时间: 2019-09-05 15:44:43 (UTC+8)
    摘要:   蔡,許和賴(1998)提出了一種混合人工智能(AI)方法,該方法集成了基於規則的系統和人工神經網絡(ANN)技術,用以預測標準普爾500指數期貨未來價格變化的方向。他們聲稱混合方法可以促進更可靠的智能係統的開發,以模擬專家思維和支持決策過程。
      這項研究在兩個方面與蔡,許和賴(1998)提出的混合人工智能(AI)有所不同。首先,本研究有兩個新增的狀態變量用於描述市場狀態。其次,我們使用單層前饋式神經網絡(SLFN)和強記、軟化和整合(CSI)學習算法代替推理神經網絡(RN)和反向傳播學習算法。
      實驗結果表明,所提出的具有CSI學習算法的決策支持系統可有效預測2007年至2013年7年測試期間的Non-obvious和Unobserved的資料。決策支持系統為使用者在做決策時提供建議。
      Tsaih, Hsu and Lai (1998) proposed a hybrid artificial intelligence (AI) method that integrates rule-based system techniques and artificial neural network (ANN) techniques to predict the direction of future S&P 500 index futures price changes. They claim that hybrid approaches can facilitate the development of more reliable intelligent systems to simulate expert thinking and support decision-making processes.
      This study differs from Tsaih, Hsu & Lai (1998) in two ways. First, the study has two additional state variables for the research purpose. Secondly, we use the single hidden layer feedforward neural network (SLFN) and the Cramming, Softening and Integrating (CSI) learning algorithm instead of the Reasoning Neural Networks (RN) and the Back Propagation learning algorithm.
      The empirical results show that the proposed decision support system with CSI learning algorithm is effective in predicting Non-obvious and Unobserved data during the 7-year test period from 2007 to 2013. The decision support system provides advice to the user when making decisions.
    參考文獻: [1] C. Ideenlabor, “Kanon der finanziellen Allgemeinbildung – Ein Memorandum”, in Frankfurt/Main: Commerzbank AG, 2003.
    [2] K. Sachse, H. Jungermann and J. Belting, “Investment risk – The perspective of individual investors”, Journal of Economic Psychology, vol. 33, no. 3, pp. 437-447, 2012.
    [3] Nosić and M. Weber, “How Riskily Do I Invest? The Role of Risk Attitudes, Risk Perceptions, and Overconfidence”, Decision Analysis, vol. 7, no. 3, pp. 282-301, 2010.
    [4] J. D. Schwager, “fundamental analysis, technical analysis, trading, spreads, and options”, in A complete guide to the futures markets, John Wiley & Sons, 1984.
    [5] C. Park and S. Irwin, “WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS? ”, Journal of Economic Surveys, vol. 21, no. 4, pp. 786-826, 2007.
    [6] J. Steidlmayer and K. Koy, Markets and market logic. Chicago: Porcupine Press, 1986.
    [7] C. Yeh and C. H. Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients”, Expert Systems with Applications, vol. 36(2), pp. 2473-2480, 2009.
    [8] F. De Roon, T. Nijman and C. Veld, “Hedging Pressure Effects in Futures Markets”, The Journal of Finance, vol. 55, no. 3, pp. 1437-1456, 2000.
    [9] J. de Jesús Rubio, “Stable Kalman filter and neural network for the chaotic systems identification”, Journal of the Franklin Institute, vol. 354, no. 16, pp. 7444-7462, 2017.
    [10] Y. Yoon and G. Swales, “Predicting stock price performance: A neural network approach”, Proceedings of the 24th Annual Hawaii International Conference on System Sciences, Hawaii, vol. 4, pp. 156-162, 1991.
    [11] R. R. Tsaih, “The softening learning procedure”, Mathematical and computer modelling, vol. 18, no. 8, pp. 61-64, 1993.
    [12] R. H. Tsaih and T. C. Cheng, “A resistant learning procedure for coping with outliers”, Annals of Mathematics and Artificial Intelligence, vol. 57, no.2, pp. 161-180, 2009.
    [13] C. C. Chen, Y. C. Kuo, C. H. Huang and A. P. Chen, “Applying market profile theory to forecast Taiwan Index Futures market”, Expert Systems with Applications, vol. 41, no. 10, pp. 4617-4624, 2014.
    [14] T. da Costa, R. Nazário, G. Bergo, V. Sobreiro and H. Kimura, “Trading System based on the use of technical analysis: A computational experiment”, Journal of Behavioral and Experimental Finance, vol. 6, pp. 42-55, 2015.
    [15] P. Heng and S. Niblock, “Trading with Tigers: A Technical Analysis of Southeast Asian Stock Index Futures”, International Economic Journal, vol. 28, no. 4, pp. 679-692, 2014.
    [16] Y. Kara, M. Acar Boyacioglu and Ö. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange”, Expert Systems with Applications, vol. 38, no. 5, pp. 5311-5319, 2011.
    [17] R. Tsaih, Y. Hsu and C. Lai, “Forecasting S&P 500 Stock Index Futures with the Hybrid AI system”, Decision Support Systems, vol. 23, no. 2, pp. 161-174, 1998.
    [18] S. Knerr, L. Personnaz and G. Dreyfus, “Single-layer learning revisited: a stepwise procedure for building and training a neural network”, Neurocomputing. Heidelberg : Springer Berlin Heidelberg, 1990.
    [19] R. R. Tsaih, “An explanation of reasoning neural networks”, Mathematical and Computer Modelling, vol. 28, no. 2, pp. 37-44, 1998.
    [20] S. Y. Huang, F. Yu, R. H. Tsaih and Y. Huang, “Resistant learning on the envelope bulk for identifying anomalous patterns”, 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 3303-3310, 2014.
    [21] C. W. Lin, “A Decision Support Mechanism for Outlier Detection in the Concept Drifting Environment”, Master Thesis, MIS, National Chengchi University, Taipei, Taiwan, Retrieved from https://hdl.handle.net/11296/7q77y6, 2015.
    [22] J. J. Wu, “Application of Machine Learning to Predicting the Returns of Carry Trade ”, Master Thesis, MIS, National Chengchi University, Taipei, Taiwan, Retrieved from https://hdl.handle.net/11296/8m5pu2, 2017.
    [23] J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy and A. Bouchachia, “A survey on concept drift adaptation”, ACM Computing Surveys, vol. 46, no. 4, pp. 1-37, 2014.
    [24] T. S. Chande and S. Kroll, The new technical trader: boost your profit by plugging into the latest indicators. New York: John Wiley & Sons Inc, 1994.
    [25] J. Bollinger, Bollinger on Bollinger bands. New York: McGraw-Hill, 2002.
    [26] J. C. Chen, Y. Zhou and X. Wang, “Profitability of simple stationary technical trading rules with high-frequency data of Chinese Index Futures ”, Physica A: Statistical Mechanics and its Applications, vol. 492, pp.1664-1678, 2018.
    [27] T. Lubnau and N. Todorova, “Trading on mean-reversion in energy futures markets ”, Energy Economics, vol. 51, pp. 312-319, 2015.
    [28] A. C. Atkinson and T. C. Cheng, “Computing least trimmed squares regression with the forward search ”, Statistics and Computing, vol. 9, no. 4, pp. 251-263, 1999.
    [29] S. V. Stehman, “Selecting and interpreting measures of thematic classification accuracy ”, Remote sensing of Environment, vol. 62, no. 1, pp. 77-89, 1997.
    描述: 碩士
    國立政治大學
    資訊管理學系
    106356019
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0106356019
    数据类型: thesis
    DOI: 10.6814/NCCU201900839
    显示于类别:[資訊管理學系] 學位論文

    文件中的档案:

    档案 大小格式浏览次数
    601901.pdf1405KbAdobe PDF0检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈