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| 研究生： |
林雋鈜 Lin, Jyun-Hong |
|---|---|
| 論文名稱： |
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. |
| 指導教授： |
蔡瑞煌
Tsaih, Rua-Huan |
| 口試委員: |
林士貴
Lin, Shih-Kuei 黃介良 Huang, Chai-Liang |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 資訊管理學系 Department of Management Information System |
| 論文出版年： | 2017 |
| 畢業學年度： | 106 |
| 語文別： | 英文 |
| 論文頁數： | 49 |
| 中文關鍵詞： | 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 |
| 外文關鍵詞： | Machine learning, Artificial neural network, GPU, S&P500, Futures market, TensorFlow, VIX index |
| 相關次數： | 點閱：52 下載：6 |
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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.
Chapter 1. Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Objective 3
Chapter 2. Literature Review 5
2.1 Futures Market Background Review 5
1. Commodity Cash Market and Commodity Futures Market 5
2. The Standard &Poor’s 500 (S&P 500) 6
3. The CBOE Volatility Index (VIX) 6
2.2 Decision Support Mechanism 7
1. Hybrid AI System 7
2. Reasoning Neural Network (RN) 7
2.3 Machine Learning 10
1. History and Introduction 10
2. TensorFlow 10
3. GPU-Computing 15
2.4 Moving Window 15
Chapter 3. Experiment Design 17
3.1 Experiment overview 17
3.2 The design of the Variables. 18
1. Data Preprocessing 19
3.3 The design of the System. 24
1. Moving window in our system 24
2. The trigger 25
3. Summarize mechanism. 25
4. The proposed predicting mechanism 26
5. The voting mechanism 29
3.4 Experiment Environment 30
Chapter 4. Experiment result 31
4.1 Result overview 31
4.2 Result of proposed predicting system 35
The training time for every window 39
4.3 Result without VIX variables. 40
Chapter 5. Conclusion and Future work. 45
5.1 Conclusions 45
1. The performance of minute data: 45
2. The use of VIX variable: 45
5.2 Future works 45
Reference 47
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