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    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
    Lin, Jyun-Hong
    Keywords: 機器學習
    Machine learning
    Artificial neural network
    Futures market
    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。
    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.
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    Description: 碩士
    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

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