English  |  正體中文  |  简体中文  |  Post-Print筆數 : 20 |  Items with full text/Total items : 90029/119959 (75%)
Visitors : 24035769      Online Users : 210
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/125529
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/125529

    Title: 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
    Authors: 鄭玉婕
    Jheng, Yu-Jie
    Contributors: 蔡瑞煌
    Tsaih, Rua-Huan
    Jheng, Yu-Jie
    Keywords: 人工神經網絡
    強記、 軟化與整合
    Artificial Neural Network
    Cramming and Softening and Integrating
    Hybrid Artificial Intelligence
    Futures Forecast
    Decision Support System
    Date: 2019
    Issue Date: 2019-09-05 15:44:43 (UTC+8)
    Abstract:   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。
      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.
    Reference: [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.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106356019
    Data Type: thesis
    DOI: 10.6814/NCCU201900839
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File SizeFormat
    601901.pdf1405KbAdobe PDF0View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback