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系統識別號 U0002-2106201922362700
中文論文名稱 應用人工智慧於ETF市場預測與投資組合最佳化
英文論文名稱 Artificial Intelligence for ETF Market Prediction and Portfolio Optimization
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 107
學期 2
出版年 108
研究生中文姓名 林建廷
研究生英文姓名 Jian-Ting Lin
學號 606630274
學位類別 碩士
語文別 英文
口試日期 2019-06-01
論文頁數 60頁
口試委員 指導教授-戴敏育
委員-張昭憲
委員-楊錦生
委員-戴敏育
中文關鍵字 人工智慧  ETF  深度學習  投資組合最佳化  機器學習  金融市場預測 
英文關鍵字 Artificial Intelligent (AI)  ETF (Exchange Traded Funds)  Deep Learning  Portfolio Optimization  Machine Learning  Financial Market Prediction 
學科別分類
中文摘要 本研究旨在開發將機器學習和深度學習算法應用於投資回報預測和投資組合優化管理的系統,用於推薦投資者適當的短期和長期投資策略。我們設計的核心算法是一個人工智能嵌入式系統,用於在金融市場中進行時間序列預測,並專注於ETF交易。有許多研究側重於算法交易,傳統的時間序列預測和不同形式的各種應用的投資組合管理;然而,關注使用各種機器學習算法來預測市場趨勢的應用的文獻量是有限的。在這項研究中,我們使用了五種機器學習算法和兩種深度學習方法,長期短期記憶(LSTM)和閘循環單元(GRU)來開發我們的系統並構建短期和長期項目組合管理的應用程序。我們開發預測模塊並利用結果構建日間交易策略並執行投資組合優化以舉例說明機器學習算法真正增加價值,我們比較不同的方法並評估哪些算法在我們的任務中表現更好。我們構建的系統和模塊是可擴展和可移植的,它可以在不同的時間間隔內開發交易推薦系統時用作框架和子模塊。
英文摘要 There are many studies focus on algorithmic trading, traditional time series forecasting and portfolio management in different forms of various applications; however, the amount of literatures focusing on the applications which use various machine learning algorithm to forecast market trends are limited. In this research, we used five machine learning algorithms and two deep learning approaches, Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU), to develop our system and build up the applications for short-term and long term portfolio management. We develop a forecasting module and exploit the result to construct a day trading strategy and to perform portfolio optimization to exemplify the machine learning algorithms truly add values, and we compare different methodologies and evaluate which algorithms perform better in our task. The system and module we built is expandable and portable, it can be used as a framework and submodule when developing trading recommendation system in different time intervals.
論文目次 1. Introduction 1
1.1. Background and Motivation 1
1.2. Research Purpose 3
1.3. Research Method 3
1.4. Conclusion and Findings 4
1.5. Research Contribution 5
2. Related Works 6
2.1. Artificial Intelligence in Finance 6
2.2. Machine Learning 8
2.2.1. SVM 9
2.2.2. Decision Tree 11
2.2.3. eXtreme Gradient Boosting 12
2.2.4. Random Forest 14
2.2.5. Naïve Bayes 15
2.2.6. Logistic Regression 16
2.3. Neural Network and Deep Learning 17
2.3.1. Recurrent Neuron Net (RNN) 19
2.3.2. Long Short Term Memory (LSTM) 20
2.3.3. Gate Recurrent Unit (GRU) 22
2.4. Portfolio Optimization 24
2.4.1. Markowitz Mean-Variance Model 25
2.4.2. Black-Litterman model 25
2.4.3. Summary of Related works on Portfolio Optimization 26
3. Research Methods and System Framework 27
3.1. Research Design 27
3.1.1 Construct a Conceptual Framework 27
3.1.2 Develop a System Architecture 27
3.1.3 Analyze & Design the System 28
3.1.4 Build the System 28
3.1.5 Observe & Evaluate the System 28
3.2. System Architecture 29
3.2.1. Data Collection Module 29
3.2.2. Data Preprocessing Module 29
3.2.3. Machine Learning Training Module 29
3.2.4. Deep Learning Training Module 30
3.2.5. Forecasting Module 30
3.2.6. Data Visualization Module 30
3.2.7. Portfolio Optimization Module 31
3.3. Subjects and Data Collection 32
3.4. Increase complexity of time series data and normalization 32
3.5. Training and Validation 33
3.6. Hyper parameter and Environment Setting 33
3.7. The Black-Litterman Formula 34
4. Experimental Result and Discussion 36
4.1.1. Result and Analysis 37
4.1.2. Comparison with Longer Time Horizon 40
4.1.3. Portfolio Optimization With Model Based Investor Views 42
4.1.4. Portfolio Assets Selection 43
5. Conclusion and Recommendations 53
5.1. Reviews and research finding 53
5.2. Research contribution 54
5.3. Practitioners implication 55
5.4. Limitations of the Study 56
5.5. Recommendations for Future Research 56
References 58






List of Figures
Figure 1. SVM classification 10
Figure 2. Classification Tree 11
Figure 3. Tree Ensemble Model 13
Figure 4. Random Forests 14
Figure 5. Logistic Regression compare to Linear Regression 16
Figure 6. Frank Rosenblatt’s Perceptron 18
Figure 7. A recurrent neural network and the unfolding in time. 19
Figure 8. A Long Short-Term Memory (LSTM) unit 21
Figure 9. The structure of GRU 22
Figure 10. System Development Research Method Flow Chart 28
Figure 11. System Architecture 31
Figure 12. Confusion Matrix for 0050 TW prediction in 2018 38
Figure 13. Volatility for 0050 TW prediction in 2018 40
Figure 14. Fluctuation for 0050 TW prediction in 2018 41
Figure 15. Correlation Matrix for whole 20 ETFs 43
Figure 16. Cumulative Return of 6 stocks (2018) 47
Figure 17. Cumulated return of portfolio optimization strategies(From Predict Model) 48
Figure 18. Cumulated return of portfolio optimization strategies (Return from Actual return) 49
Figure 19. Cumulated return (By Naïve Bayes) of portfolio optimization strategies 49
Figure 20. Cumulated return (By Logistic Regression) of portfolio optimization strategies 51
List of Tables
Table 1. Four approaches of AI 8
Table 2. A comparison of related works on stock market prediction 23
Table 3. A comparison of related works on portfolio optimization 26
Table 4. Structure of our deep learning model 37
Table 5. Predicted Result of 0050.TW by SVM 38
Table 6. Result and Performance of Each Predict Model 42
Table 7. Weight Rebalancing for Each Quarter 44
Table 8. List of Selected ETFs Date of Portfolio 44
Table 9. Weight of 6 ETFs of The Black-Litterman Model 45
Table 10. Correlation Matrix for the selected 6 ETFs 45
Table 11. Predicted Investors’ opinion matrix (Q1 and Q2 for instance) 46
Table 12. Correlation Matrix for the selected 6 ETFs 52




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