政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136360

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136360

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政大機構典藏 > 商學院 > 金融學系 > 學位論文 > Item 140.119/136360

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题名:	強化學習下動態調整風險偏好之投資組合配置:以台灣50指數為例 Portfolio Allocation with Dynamic Risk Aversion via Reinforcement Learning: Evidence from Taiwan 50 Index
作者:	陳昱成 Chen, Yu-Cheng
贡献者:	林士貴陳昱成 Chen, Yu-Cheng
关键词:	均數-變異數模型風險趨避強化學習近端策略優化 Mean-Variance model Risk Aversion Reinforcement Learning Proximal Policy Optimization
日期:	2021
上传时间:	2021-08-04 14:51:05 (UTC+8)
摘要:	Markowitz(1952)提出現代投資組合理論，透過均數-變異數模型(Mean-Variance Model) 為投資人進行資產配置，並設風險趨避參數調整報酬率和風險之間的比例，但在實務中，此風險趨避參數難以動態調整。本研究使用強化學習(Reinforcement Learning) 中的近端策略優化(Proximal Policy Optimization,PPO)，依據不同市場變化，動態調整每一天風險趨避參數，當市場情況好時，投資人偏好承擔較高風險，獲得更高報酬，當市場情況壞時，投資人風險偏好趨於保守。本研究以台灣 50 指數當作整體市場走勢，比較強化學習輸入過去不同時間週期資訊之結果，研究結果顯示，不論輸入時間週期長短，強化學習績效皆能贏過固定風險趨避參數下均數-變異數模型，說明利用強化學習，能解決實務上風險趨避參數難以動態調整之問題。 Markowitz (1952) proposed Modern Portfolio Theory, which used the Mean-Variance Model to allocate assets for investors, and set the risk aversion parameter to adjust the ratio between return and the risk. But in practice, this risk aversion parameter is difficult to adjust dynamically. In our paper, we use Proximal Policy Optimization in reinforcement learning to dynamically adjust daily risk aversion parameters according to different market changes. In a bull market, investors prefer to take higher risks and get higher returns. On the other hand, in a bear market, investors` risk appetite tends to be conservative. This study uses the Taiwan 50 Index as the overall market trend, and compares the results of inputting different time periods of information into the model. The results show that regardless of the length of the input time period, the performance of the model can outperform the mean-variance model under fixed risk aversion parameters. Explain that the use of reinforcement learning can solve the problem of difficulty in dynamic adjustment of risk aversion parameters in practice.
參考文獻:	劉上瑋 (2017)。深度增強學習在動態資產配置上之應用 : 以美國 ETF 為例。國立政治大學金融研究所碩士論文。 Basak, S., & Chabakauri, G. (2010). Dynamic mean-variance asset allocation. The Review of Financial Studies, 23(8), 2970-3016. Björk, T., Murgoci, A., & Zhou, X. Y. (2014). Mean–variance portfolio optimization with state‐dependent risk aversion. Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics, 24(1), 1-24. Díaz, A., & Esparcia, C. (2019). Assessing risk aversion from the investor’s point of view. Frontiers in psychology, 10, 1490. Gold, C. (2003, March). FX trading via recurrent reinforcement learning. In 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. (pp. 363-370). IEEE. Jiang, Z., & Liang, J. (2017, September). Cryptocurrency portfolio management with deep reinforcement learning. In 2017 Intelligent Systems Conference (IntelliSys) (pp. 905-913). IEEE. Li, Y., & Li, Z. (2013). Optimal time-consistent investment and reinsurance strategies for mean–variance insurers with state dependent risk aversion. Insurance: Mathematics and Economics, 53(1), 86-97. Markowitz, H. (1959). Portfolio selection. Journal of Finance, 7, 77–98. Moody, J., & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks, 12(4), 875-889. Neuneier, R. (1998). Enhancing Q-learning for optimal asset allocation. Advances in neural information processing systems (pp. 936-942). Rosenberg, J. V., & Engle, R. F. (2002). Empirical pricing kernels. Journal of Financial Economics, 64(3), 341-372. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. Zhang, Y., Wu, Y., Li, S., & Wiwatanapataphee, B. (2017). Mean-variance asset liability management with state-dependent risk aversion. North American Actuarial Journal, 21(1), 87-106. Zhang, Y., Zhao, P., Li, B., Wu, Q., Huang, J., & Tan, M. (2020). Cost-sensitive portfolio selection via deep reinforcement learning. IEEE Transactions on Knowledge and Data Engineering.
描述:	碩士國立政治大學金融學系 108352019
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0108352019
数据类型:	thesis
DOI:	10.6814/NCCU202100814
显示于类别:	[金融學系] 學位論文

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