政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/145961

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/145961

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题名:	投資組合管理：Black-Litterman模型結合不同機器學習方法 Portfolio Management: Black-Litterman Portfolios with Different Machine Learning Derived Views
作者:	李宏澤 Li, Hung-Tze
贡献者:	蕭明福廖四郎 Shaw, Ming-fu Liao, Szu-Lang 李宏澤 Li, Hung-Tze
关键词:	Black-Litterman模型共變異數估計機器學習模型 Black-Litterman model Covariance matrix estimation Machine learning
日期:	2023
上传时间:	2023-07-10 11:53:12 (UTC+8)
摘要:	本研究嘗試以不同機器學習方法及不同預測目標，預測資產價格漲跌方向與幅度並結合Black-Litterman模型，建構全球化之投資組合資產配置。以金融資產之價量指標、技術指標及Fama-French三因子為輸入變數，在資料處理上避免使用KNN方式填補遺失值，確保資料的正確性。將機器學習模型預測結果代入Black-Litterman模型中的投資者觀點，結合不同共變異數估計方法，比較在不同投資策略下資產配置的績效表現。實證結果發現，Ledoit-Wolf Shrinkage Variance Estimate為最佳的共變異數估計方法，在分別預測價格漲跌與幅度時，XGBoost有較高的準確率；在直接預測價格漲跌與幅度時， Random Forest有較高的準確率；而在績效表現上，SVM模型於極大化夏普比率與超額報酬-風險值比率時，能有效地分散投資及降低風險；於測試集中，Random Forest直接預測價格漲跌與幅度的績效表現長時間優於其他模型，直到最後三個月，使用分別預測的方式能創造大量報酬，最後以XGBoost分別預測價格漲跌與幅度獲得最高的累積報酬率，並且超越iShares Russell 1000 ETF及直接預測價格漲跌與幅度的模型，造成模型表現差異的原因則源於模型組成與變數選擇。 This research attempts to use different machine learning methods and different forecasting objectives to predict the direction and volatility of asset price. Subsequently, combine the Black-Litterman model to construct a global portfolio asset allocation. Using the price and volume indicators of financial assets, technical indicators and the Fama-French three factors model as input variables. Additionally, avoid using the KNN method to fill in missing values in data processing to ensure the correctness of the data. Substitute the prediction results of the machine learning model into the investor`s point of view in the Black-Litterman model and combine different covariance estimation methods to compare the performance of asset allocation under different investment strategies. The empirical results show that Ledoit-Wolf shrinkage variance estimate is the best covariance estimation method. In addition, XGBoost has a higher accuracy rate in separately predicting the direction and volatility of price; Random Forest has a higher accuracy rate in direction predicting. In terms of performance, SVM model can effectively diversify investments and reduce risk when maximizing the Sharpe ratio and VaR. In test data, using Random Forest to predict the direction and volatility directly outperforms others for a long time. Until last three months, the way of predicting separately can generate large returns. Finally, XGBoost predicts separately has the highest final cumulative return, which even better than the iShares Russell 1000 ETF and the models which predict directly. The reason for the difference in model performance is due to the model composition and variable selection.
參考文獻:	[1] Ai, X. W., Hu, T., Li, X. & Xiong, H. (2010). Clustering High-frequency Stock Data for Trading Volatility Analysis. 2010 Ninth International Conference on Machine Learning and Applications, 333-338. [2] Beach, S.L. & Orlov, A.G. (2007). An application of the Black–Litterman model with EGARCH-M-derived views for international portfolio management. Fin Mkts Portfolio Mgmt, 21, 147–166. [3] Best, M. J. & Grauer, R. R. (1991). On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results. The Review of Financial Studies, 4(2), 315–342. [4] Black, F., & Litterman, R. (1991). Asset allocation: combining investor views with market equilibrium. The Journal of Fixed Income, 1(2), 7-18. [5] Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43. [6] Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. [7] Donthireddy, P. (2018). Black-Litterman portfolio with machine learning derived views. https://doi.org/10.13140/RG.2.2.26727.96160 [8] Fama, E., & French, K. (2004). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives. 18(3), 25-46. [9] Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Syst. Appl., 124, 226–251. [10] Markowitz, H. (1952). Portfolio Selection. Journal of Finance. 7(1), 77-99. [11] Meucci, A. (2010). The Black litterman Approach: Original Model and Extensions. Download from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1117574. [12] Michaud, R. O. (1989). The Markowitz Optimization Enigma: Is Optimized Optimal? Financial Analysts Journal, 31-42. [13] Mossin, J. (1966). Equilibrium in a Capital Asset Market. Econometrical, 34(4), 768–783. [14] Ledoit O. & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. J. Multivariate Anal. 88 (2), 365–4. [15] Ledoit O. & Wolf, M. (2021). Shrinkage estimation of large covariance matrices: Keep it simple, statistician? J. Multivariate Anal. 186. [16] Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19 (3), 425-442. [17] Treynor, J. L. (1961). Market Value, Time, and Risk. Unpublished manuscript. [18] Treynor, J. L. (1962). Toward a Theory of Market Value of Risky Assets. Unpublished manuscript. A final version was published in 1999, in Asset Pricing and Portfolio Performance: Models, Strategy and Performance Metrics. Robert A. Krawczyk (editor) London: Risk Books, 15–22. [19] Zhang, C. & Tang, H. (2022). Empirical Research on Multifactor Quantitative Stock Selection Strategy Based on Machine Learning. 2022 3rd International Conference on Pattern Recognition and Machine Learning (PRML), 380-383. [20] Zhu, Y. (2021). Research on Financial Risk Control Algorithm Based on Machine Learning. 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 16-19.
描述:	碩士國立政治大學經濟學系 110258038
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0110258038
数据类型:	thesis
显示于类别:	[經濟學系] 學位論文

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