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題名 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貢獻者 蔡瑞煌
Tsaih, Rua-Huan
鄭玉婕
Jheng, Yu-Jie關鍵詞 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 上傳時間 5-Sep-2019 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 資料類型 thesis dc.contributor.advisor 蔡瑞煌 zh_TW dc.contributor.advisor Tsaih, Rua-Huan en_US dc.contributor.author (Authors) 鄭玉婕 zh_TW dc.contributor.author (Authors) Jheng, Yu-Jie en_US dc.creator (作者) 鄭玉婕 zh_TW dc.creator (作者) Jheng, Yu-Jie en_US dc.date (日期) 2019 en_US dc.date.accessioned 5-Sep-2019 15:44:43 (UTC+8) - dc.date.available 5-Sep-2019 15:44:43 (UTC+8) - dc.date.issued (上傳時間) 5-Sep-2019 15:44:43 (UTC+8) - dc.identifier (Other Identifiers) G0106356019 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125529 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 106356019 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents CHAPTER 1 INTRODUCTION 1CHAPTER 2 LITERATURE REVIEW 72.1 The Adaptive Single-hidden Layer Feed-forward Neural Networks (ASLFN) with Multiple Output Nodes 72.2 The Back-Propagation Learning Algorithm Associated with SLFN with Multiple-Class Categorization Learning Goal 92.3 Moving Window 122.4 Technical Indicators 132.4.1 Forecast Oscillator 152.4.2 Forecast Moving Average 152.4.3 Stochastic Relative Strength Index 162.4.4 Bollinger Bands 172.5 Least Trimmed Squares (LTS) 192.6 The Rule-based Systems 202.7 The Condition L 23CHAPTER 3 THE PROPOSED LEARNING ALGORITHM 24CHAPTER 4 THE EXPERIMENT DESIGN OF S&P 500 INDEX FORECASTING 344.1 Variable Description 354.3 The Description of Collected Data 384.4 The Proposed Learning Algorithm and Implementation 42CHAPTER 5 THE EXPERIMENT RESULT OF S&P 500 INDEX FORECASTING 44CHAPTER 6 CONCLUSION AND FUTURE WORK 48REFERENCE 50APPENDIX 53 zh_TW dc.format.extent 1439101 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106356019 en_US dc.subject (關鍵詞) 人工神經網絡 zh_TW dc.subject (關鍵詞) 強記、 軟化與整合 zh_TW dc.subject (關鍵詞) 混合人工智能 zh_TW dc.subject (關鍵詞) 期貨預測 zh_TW dc.subject (關鍵詞) 決策支持系統 zh_TW dc.subject (關鍵詞) Artificial Neural Network en_US dc.subject (關鍵詞) Cramming and Softening and Integrating en_US dc.subject (關鍵詞) Hybrid Artificial Intelligence en_US dc.subject (關鍵詞) Futures Forecast en_US dc.subject (關鍵詞) Decision Support System en_US dc.title (題名) 單層學習神經網路配合多輸出節點應用於期貨預測 zh_TW dc.title (題名) The Single-hidden Layer Feedforward Neural Networks with Multiple Output Nodes for Futures Forecast en_US dc.type (資料類型) thesis en_US dc.relation.reference (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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900839 en_US
