Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/137166


Title: 具可調整風險偏好之深度強化學習資產配置系統
A Deep Reinforcement Learning Portfolio Management System with Adjustable Risk Preference
Authors: 張天慈
Chang, Tain-Tzu
Contributors: 胡毓忠
Hu, Yuh Jong
張天慈
Chang, Tain-Tzu
Keywords: 深度強化學習
強化學習
資產配置系統
風險偏好
Deep Reinforcement Learning
Reinforcement Learning
Portfolio Management System
Risk Preference
Asset Allocation
OpenAI gym
Date: 2021
Issue Date: 2021-09-02 18:17:46 (UTC+8)
Abstract: 我們導入了一個具可調整風險偏好之深度強化學習資產配置系統。透過變更門檻參數,此系統可提供適合不同風險容忍度的投資者合適的投資組合。實驗結果顯示此系統在多數情況最大跌幅和年化報酬上皆優於固定比重投資組合。相同的做法也可用於其他投資者偏好,例如BlackLitterman模型中的投資者觀點。
We introduced a DRL-based portfolio management system with adjustable risk preference. The system can produce portfolio s that meet different investors’risk preference by adjusting the threshold parameter.The experiment results show that for most cases, our system outperformed the constant rebalanced portfolio (CRP) in terms of maximum drawdown (MDD) and Compound annual growth rate (CAGR). The same approach has the potential to apply to different investors’ preferences, like the opinion of the investor used in the Black–Litterman model.
Reference: [1] Black, F., and Litterman, R. Global portfolio optimization. Financial analysts journal
48, 5 (1992), 28–43.
[2] Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and
Zaremba, W. Openai gym. arXiv preprint arXiv:1606.01540 (2016).
[3] Chang, E. Why you likely have too many mutual funds or etfs, Sep 2016.
[4] Cogneau, P., and Hübner, G. The 101 ways to measure portfolio performance. Available
at SSRN 1326076 (2009).
[5] Fischer, T., and Krauss, C. Deep learning with long shortterm
memory networks
for financial market predictions. European Journal of Operational Research 270, 2
(2018), 654 – 669.
[6] Fujimoto, S., Hoof, H., and Meger, D. Addressing function approximation error
in actorcritic
methods. In International Conference on Machine Learning (2018),
PMLR, pp. 1587–1596.
[7] Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. Soft actorcritic:
Offpolicy
maximum entropy deep reinforcement learning with a stochastic actor, 2018.
[8] Johansen, A., and Sornette, D. Stock market crashes are outliers. The European
Physical Journal BCondensed
Matter and Complex Systems 1, 2 (1998), 141–143.
[9] Johansen, A., and Sornette, D. Large stock market price drawdowns are outliers.
Journal of Risk 4 (2002), 69–110.
[10] Kahneman, D., and Tversky, A. An analysis of decision under risk. Econometrica
36 (2000).
[11] Krauss, C., Do, X. A., and Huck, N. Deep neural networks, gradientboosted
trees,
random forests: Statistical arbitrage on the s&p 500. European Journal of Operational
Research 259, 2 (2017), 689 – 702.
[12] Levine, S., Finn, C., Darrell, T., and Abbeel, P. Endtoend
training of deep visuomotor
policies. The Journal of Machine Learning Research 17, 1 (2016), 1334–1373.
[13] MagdonIsmail,
M., and Atiya, A. F. Maximum drawdown. Risk Magazine 17, 10
(2004), 99–102.
[14] Markowitz, H. Portfolio selection. The Journal of Finance 7, 1 (1952), 77–91.
[15] Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D.,
and Riedmiller, M. Playing atari with deep reinforcement learning. arXiv preprint
arXiv:1312.5602 (2013).
[16] Moody, J., and Lizhong Wu. Optimization of trading systems and portfolios. In
Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering
(CIFEr) (1997), pp. 300–307.
[17] Moody, J., and Saffell, M. Learning to trade via direct reinforcement. IEEE Transactions
on Neural Networks 12, 4 (2001), 875–889.
[18] Moody, J., Wu, L., Liao, Y., and Saffell, M. Performance functions and reinforcement
learning for trading systems and portfolios. Journal of Forecasting 17, 56
(1998), 441–470.
[19] Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. Trust region policy
optimization. In International conference on machine learning (2015), PMLR,
pp. 1889–1897.
[20] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. Proximal policy
optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).
[21] Sharpe, W. F. The sharpe ratio. The Journal of Portfolio Management 21, 1 (1994),
49–58.
[22] Silver, D., Lever, G.,Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. Deterministic
policy gradient algorithms. In International conference on machine learning
(2014), PMLR, pp. 387–395.
[23] Statman, M. How many stocks make a diversified portfolio? Journal of financial
and quantitative analysis (1987), 353–363.
[24] Tversky, A., and Kahneman, D. Advances in prospect theory: Cumulative representation
of uncertainty. Journal of Risk and Uncertainty 5, 4 (Oct 1992), 297–323.
[25] Willenbrock, S. Diversification return, portfolio rebalancing, and the commodity
return puzzle. Financial Analysts Journal 67, 4 (2011), 42–49.
Description: 碩士
國立政治大學
資訊科學系碩士在職專班
108971001
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108971001
Data Type: thesis
Appears in Collections:[資訊科學系碩士在職專班] 學位論文

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