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    题名: 應用強化學習與卷積神經網路於投資組合配置
    Application of Reinforcement Learning and Convolutional Neural Networks to Portfolio Allocation
    作者: 林冠宇
    Lin, Guan-Yu
    贡献者: 廖四郎
    Liao, Szu-Lang
    林冠宇
    Lin, Guan-Yu
    关键词: 卷積神經網路
    Black-Litterman 模型
    風險趨避參數
    強化學習
    Convolution Neural Network
    Black-Litterman Model
    Risk Aversion
    Reinforcement Learning
    日期: 2022
    上传时间: 2022-08-01 17:27:39 (UTC+8)
    摘要: 本研究嘗試將強化學習方法應用於投資組合資產配置，且利用卷積神經網路(CNN)以金融資產的價量相關資料及技術指標作為輸入資料，進行資產價格漲跌方向及漲跌幅度的預測，並結合Black-Litterman模型建構風險分散的投資組合。將神經網路模型預測的結果作為Black-Litterman模型的投資人觀點，利用強化學習動態調整Black-Litterman模型中的風險趨避參數進行資產配置。實證發現，卷積神經網路在預測資產價格漲跌方向方面有過度配適的情況，使得測試期間準確度不高；而在預測資產價格漲跌幅度方面則有不錯的表現。在績效表現上面，無論是以iShares Russell 1000 ETF作為狀態資料來進行學習的投資組合一或是以S&P 500作為狀態資料來進行學習的投資組合二，皆大幅超越市值加權投資組合、等值加權投資組合，且投資組合一更是優於iShares Russell 1000 ETF且有更小的最大策略虧損，顯示能在控制風險的同時獲取更好的報酬。
    In this thesis, we try to apply reinforcement learning to portfolio allocation. Historical price and volume related data and technical indicators are used as in put data to predict following week’s excess return. We also combine the forecasts with the Black-Litterman model and construct diversified portfolio. We use the forecasts to be investor views in Black-Litterman model and use reinforcement learning to adjust risk aversion.The empirical results show that CNN is overfitting in predicting the sign of asset price, which makes the accuracy of the test period not well; but it has a good performance in predicting the magnitude of excess return. We also find that both portfolio 1 that iShares Russell 1000 ETF is used as the state data and portfolio 2 that S&P 500 is used as the state data significantly outperform the benchmark portfolios. Portfolio 1 is even better than iShares Russell 1000 ETF and has a smaller MDD, showing better returns while controlling risk.
    參考文獻: [1] Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert Systems with Applications, 42(20), 7046-7056.
    [2] Black, F., & Litterman, R. (1991). Asset allocation: combining investor views with market equilibrium. The Journal of Fixed Income, 1(2), 7-18.
    [3] Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
    [4] Donthireddy, P. (2018, July 19). Black-Litterman portfolio with machine learning derived views. ResearchGate. Retrieved September 22, 2019, from https://www.researchgate.net/publication/326489143_Black-Litterman_Portfolios_with_Machine_Learning_derived_Views.
    [5] Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). “Deep direct reinforcement learning for financial signal representation and trading.” IEEE transactions on neural networks and learning systems 28.3: 653-664.
    [6] Hoseinzade, E., & Haratizadeh, S. (2018). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273-285.
    [7] He, G., & Litterman, R. (1999). The intuition behind Black-Litterman model portfolios. New York, NY: Goldman Sachs.
    [8] Idzorek, T. (2004). A step-by-step guide to the Black-Litterman model, incorporating user specified confidence levels. Chicago, IL:Ibbotson Associates.
    [9] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1, 541-551.
    [10] Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
    [11] Moody, J., Wu, L., Liao, Y. & Saffell, M. (1998). Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting, 17(5-6), 441-470.
    [12] Neuneier, R. (1998). Enhancing Q-learning for optimal asset allocation. In Advances in neural information processing systems (pp. 936-942).
    [13] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
    [14] Thawornwong, S., Enke, D., & Dagli, C. (2003). Neural networks as a decision maker for stock trading: a technical analysis approach. International Journal of Smart Engineering System Design, 5(4), 313-325.
    描述: 碩士
    國立政治大學
    金融學系
    108352028
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0108352028
    数据类型: thesis
    DOI: 10.6814/NCCU202201024
    显示于类别:[金融學系] 學位論文

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