系統識別號 | U0002-2302201813271400 |
---|---|
DOI | 10.6846/TKU.2018.00678 |
論文名稱(中文) | 應用深度學習於機器人理財之投資組合最佳化 |
論文名稱(英文) | Deep Learning for Robo-Advisors in Portfolio Optimization |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 經濟學系碩士班 |
系所名稱(英文) | Department of Economics |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 106 |
學期 | 1 |
出版年 | 107 |
研究生(中文) | 李政剛 |
研究生(英文) | Jheng-Gang Li |
學號 | 604570118 |
學位類別 | 碩士 |
語言別 | 英文 |
第二語言別 | |
口試日期 | 2018-01-19 |
論文頁數 | 53頁 |
口試委員 |
指導教授
-
鄭東光
共同指導教授 - 戴敏育 委員 - 陳柏儒 委員 - 林文修 |
關鍵字(中) |
人工智慧 機器人理財 深度學習 投資組合最佳化 時間序列分析 金融市場預測 |
關鍵字(英) |
Artificial Intelligent (AI) Robo-Advisors Deep Learning Portfolio Optimization Black-Litterman Time-Series Forecasting Financial Market Prediction |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
本研究旨在開發執行自動化投資管理、時間序列分析以及投資組合最佳化的理財機器人系統。本研究開發的核心演算法,利用人工智慧執行金融資產價格預測,並作為理財機器人的子系統。演算法交易以及投資組合最佳化的研究中已有許多實務上的應用,然而只有少部分文獻專注於理財機器人的應用開發。本研究利用深度學習中的長短期記憶 (Long Short-Term Memory) 方法,來解決時間序列分析中序列相依的複雜問題,並進一步開發自動化系統來執行時間序列預測於當沖交易策略以及投資組合最佳化的應用。本研究開發的可擴充系統可做為自動化投資管理中理財機器人系統的開發基礎。 |
英文摘要 |
This research aims to develop the system for Robo-Advisor to perform time-series forecasting and portfolio optimization in automated investment management. We designed the core algorithm, artificial intelligence to perform time-series forecasting in the financial market, as part of the system for Robo-Advisor in automated investment. There are many studies in algorithmic trading and portfolio management in various forms of applications; however, there is a paucity of literature focusing on the applications which are designed for Robo-Advisors. In this research, we used a deep learning approach, Long Short-Term Memory (LSTM), to develop our algorithm to solve the complexity of sequence dependence in time-series forecasting. We developed an automated system to perform time-series forecasting and used the result to construct a day trading strategy and to perform portfolio optimization to show that LSTM based algorithm added value. The system we built is expandable and can be used as a framework when developing Robo-Advisors for automated investment management. |
第三語言摘要 | |
論文目次 |
Table of Contents 1. Introduction 1 1.1. Objective 1 1.2. Background and Motivation 2 1.3. Importance of the Study 2 1.4. Robo-Advisors 3 1.5. The Black-Litterman Model 4 1.6. Deep Learning 5 1.7. Long Short-Term Memories for Time Series Forecasting 6 2. Research Methods and System Framework 7 2.1. Research Design 7 2.1.1. Construct a Conceptual Framework 7 2.1.2. Develop a System Architecture 7 2.1.3. Analyze & Design the System 7 2.1.4. Build the System 7 2.1.5. Observe & Evaluate the System 7 2.2. System Architecture 8 2.2.1. Data Collection Module 9 2.2.2. Data Preprocessing Module 9 2.2.3. LSTM Module 9 2.2.4. Forecasting Module 9 2.2.5. Data Visualization Module 10 2.2.6. Portfolio Optimization Module 10 2.3. Subjects and Data Collection 11 2.4. Time-Series to Supervised Learning 11 2.5. Training and Validation 13 2.6. LSTM Model Configuration 13 2.7. Forecasting and Labeling 16 2.8. The Black-Litterman Formula 16 3. Application and Results 18 3.1. An LSTM Based Day Trading Strategy 18 3.1.1. Result and Analysis 19 3.1.2. Comparison with Longer Time Horizon 22 3.2. Portfolio Optimization Using the LSTM Based Investor Views 27 3.2.1. The LSTM Investor Views 27 3.2.2. The Black-Litterman Model 31 3.2.3. Portfolio Performance in 2016 31 4. Conclusion and Recommendations 38 References 40 Appendix A.2 Comparison of Cumulative Return 41 List of Figures Figure 1. How LSTM Works 5 Figure 2. System Development Research Method Flow Chart 8 Figure 3. System Architecture 10 Figure 4. Model Training Losses 14 Figure 5. Distribution of the Cumulative Returns 20 Figure 6. 275_OIL Cumulative Return Under LSTM Investing Strategy in 2015 23 Figure 7. 275_OIL Cumulative Return Under LSTM Investing Strategy in 2016 23 Figure 8. Price Movement of 275_OIL in 2015 24 Figure 9. Price Movement of 275_OIL in 2016 24 Figure 10. Fluctuation in 2015 25 Figure 11. Fluctuation in 2016 25 Figure 12. Confusion Matrix for 275_OIL in 2015 26 Figure 13. Confusion Matrix for 275_OIL in 2016 26 Figure 14. Convert Forecasted Daily Returns into Quarterly Return 28 Figure 15. Pick Matrix and View Matrix for each Quarter 30 Figure 16. Portfolio Cumulative Returns 37 Figure 17. Assets Cumulative Returns 37 List of Tables Table 1. Selected ETFs Categories 12 Table 2. Model Configurations 15 Table 3. Model Summary 15 Table 4. Summary of Cumulative Return Comparison 19 Table 5. Performance of the LSTM Based Strategy in Each Class 21 Table 6. Six Selected Assets Portfolio 27 Table 7. Date of Portfolio Weight Rebalancing for Each Quarter 27 Table 8. Forecasted Quarterly Cumulative Returns and Prices 29 Table 9. Predicted Quarterly Returns 30 Table 10. Quarterly Performance of each Portfolio 31 Table 11. Historical Covariance Matrix for each Quarter 32 Table 12. Posterior Covariance Matrix for each Quarter 33 Table 13. Return Vectors and Resulting Portfolio Weights for each Quarter 34 Table 14. Annual Portfolio Statistics 36 |
參考文獻 |
Bicksler, J. L. (1984). Modern Portfolio Theory and the Capital Asset Pricing Model: A User's Guide (Book Review) (Vol. 39, pp. 568-569). Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial analysts journal, 48(5), 28-43. Chishti, S., & Barberis, J. (2016). The FINTECH Book The Financial Technology Handbook for Investors, Entrepreneurs and Visionaries. New York: New York : Wiley. Grauer, R. R., & Janmaat, J. A. (2010). Cross-sectional tests of the CAPM and Fama– French three-factor model. Journal of Banking and Finance, 34(2), 457-470. doi:10.1016/j.jbankfin.2009.08.011 Gruber, M. J., & Ross, S. A. (1978). THE CURRENT STATUS OF THE CAPITAL ASSET PRICING MODEL (CAPM. Journal of Finance, 33(3), 885-901. doi:10.1111/j.1540-6261.1978.tb02029.x Idzorek, T. M. (2002). A step-by-step guide to the Black-Litterman model. Forecasting expected returns in the financial markets, 17. Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42, 11-24. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539 Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. Nunamaker, J. F., Chen, M., & Purdin, T. D. M. (1990). Systems Development in Information Systems Research. Journal of Management Information Systems, 7(3), 89-106. doi:10.1080/07421222.1990.11517898 Rättyä, J. (2016). March of the Robo-advisors: The potential for global expansion of digital asset management platforms. Sironi, P. (2016). FinTech Innovation From Robo-Advisors to Goal Based Investing and Gamification. Somerset: Somerset : Wiley. Walters, C. (2014). The Black-Litterman model in detail. The Black-Litterman Model in Detail (June 20, 2014). Zhou, G. (2009). Beyond Black-Litterman: letting the data speak. Journal of Portfolio Management, 36(1), 36. |
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