English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88531/118073 (75%)
Visitors : 23459062      Online Users : 179
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 140.119/113286
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/113286


    Title: 應用機器學習於標準普爾指數期貨
    An application of machine learning to Standard & Poor's 500 index future.
    Authors: 林雋鈜
    Lin, Jyun-Hong
    Contributors: 蔡瑞煌
    Tsaih, Rua-Huan
    林雋鈜
    Lin, Jyun-Hong
    Keywords: 機器學習
    類神經網路
    圖形處理器
    標準普爾500指數
    期貨市場
    張量流
    VIX指數
    Machine learning
    Artificial neural network
    GPU
    S&P500
    Futures market
    TensorFlow
    VIX index
    Date: 2017
    Issue Date: 2017-10-02 10:15:01 (UTC+8)
    Abstract: 本系統係藉由分析歷史交易資料來預測S&P500期貨市場之漲幅。 我們改進了Tsaih et al. (1998)提出的混和式AI系統。 該系統結合了Rule Base 系統以及類神經網路作為其預測之機制。我們針對該系統在以下幾點進行改善:(1) 將原本的日期資料改為使用分鐘資料作為輸入。(2) 本研究採用了“移動視窗”的技術,在移動視窗的概念下,每一個視窗我們希望能夠在60分鐘內訓練完成。(3)在擴增了額外的變數 – VIX價格做為系統的輸入。(4) 由於運算量上升,因此本研究利用TensorFlow 以及GPU運算來改進系統之運作效能。
    我們發現VIX變數確實可以改善系統之預測精準度,但訓練的時間雖然平均低於60分鐘,但仍有部分視窗的時間會小幅超過60分鐘。
    The system is made to predict the Futures’ trend through analyzing the transaction data in the past, and gives advices to the investors who are hesitating to make decisions. We improved the system proposed by Tsaih et al. (1998), which was called hybrid AI system. It was combined with rule-based system and artificial neural network system, which can give suggestions depends on the past data. We improved the hybrid system with the following aspects: (1) The index data are changed from daily-based in into the minute-based in this study. (2) The “moving-window” mechanism is adopted in this study. For each window, we hope we can finish training in 60 minutes. (3) There is one extra variable VIX, which is calculated by the VIX in this study. (4) Due to the more computation demand, TensorFlow and GPU computing is applied in our system.
    We discover that the VIX can obviously has positively influence of the predicting performance of our proposed system. The average training time is lower than 60 minutes, however, some of the windows still cost more than 60 minutes to train.
    Reference: 1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. “TensorFlow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467, 2016.
    2. Arner, D. W., Barberis, J., & Buckley, R. P., “The Evolution of Fintech: A New Post-Crisis Paradigm?”, 2015.
    3. Babcock, B., Datar, M., & Motwani, R. “Sampling from a moving window over streaming data,” Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, January 2002, pp. 633-634.
    4. Bartlett, M. S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., & Movellan, J., “Recognizing facial expression: machine learning and application to spontaneous behavior,” Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference, Vol. 2, June 2005, pp. 568-573.
    5. Catanzaro, B., Sundaram, N., & Keutzer, K., “Fast support vector machine training and classification on graphics processors,” Proceedings of the 25th international conference on Machine learning. ACM, July 2008, pp. 104-111.
    6. Chen, A. S., Leung, M. T., & Daouk, H., “Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index.” Computers & Operations Research 30(6), 2003, pp. 901-923.
    7. Clark, J, “Google Turning Its Lucrative Web Search Over to AI Machines,” Bloomberg Technology, August 2015 (available online at https://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines).
    8. Cohen, W. W., Machine Learning Proceedings 1994: Proceedings of the Eighth, International Conference. Morgan Kaufmann., 2017.
    9. Google Brain, “TensorFlow,” Google Brain, 2017, available online at https://www.TensorFlow.org/.
    10. Hull, J. C., Options, futures, and other derivatives. Pearson Education India, 2006.
    11. Heakal R., “Futures Fundamentals: Characteristics”, Investopedia (available online at http://www.investopedia.com/university/futures/futures4.asp).
    12. Hornik, K., Stinchcombe, M., & White, H., “Multilayer feedforward networks are universal approximators,” Neural networks, 2(5), 1989, pp359-366.
    13. Kashani, M. N., Aminian, J., Shahhosseini, S., & Farrokhi, M., “Dynamic crude oil fouling prediction in industrial preheaters using optimized ANN based moving window technique,” Chemical Engineering Research and Design, 90(7), 2012, pp. 938-949.
    14. Metz C., “TensorFlow, Google’s Open Source AI, Signals Big Changes in Hardware Too,” Wired.com, November 2015 (available online at https://www.wired.com/2015/11/googles-open-source-ai-TensorFlow-signals-fast-changing-hardware-world/).
    15. Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. “GPU computing,” Proceedings of the IEEE, 96(5), 2008, pp. 879-899.
    16. Rampasek, L., & Goldenberg, A., “TensorFlow: Biology’s gateway to deep learning?,” Cell systems, 2(1), 2016, pp. 12-14.
    17. Scherer, K. R., “Studying the emotion-antecedent appraisal process: An expert system approach,” Cognition & Emotion, 7(3-4), 1993, pp. 325-355.
    18. Stoll, H. R., & Whaley, R. E., “Commodity index investing and commodity futures prices,” 2015.
    19. Thomson Reuters, “Google's AI beats human champion at Go,” CBC News, January 2016 (available online at http://www.cbc.ca/news/technology/alphago-ai-1.3422347).
    20. Tsaih, R. R., “The softening learning procedure,” Mathematical and computer modelling, 18(8), 1993, pp. 61-64.
    21. Tsaih, R. R., “Reasoning neural networks,”. Mathematics of Neural Networks, 1997, pp. 366-371.
    22. Tsaih, R., Hsu, Y., & Lai, C. C., “Forecasting S&P 500 stock index futures with a hybrid AI system,” Decision Support Systems, 23(2), 1998, pp. 161-174.
    23. Whaley, R. E., “Understanding the VIX,” The Journal of Portfolio Management, 35(3), 2009, pp. 98-105.
    24. Yadan, O., Adams, K., Taigman, Y., & Ranzato, M. A., “Multi-gpu training of convnets,” arXiv preprint arXiv:1312.5853, 2013.
    25. ZhaoZhi-Ming, Overview of Futures,Winson Taipei, 1993.
    Description: 碩士
    國立政治大學
    資訊管理學系
    104356036
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104356036
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File SizeFormat
    603601.pdf2890KbAdobe PDF0View/Open


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


    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 ©   - Feedback