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

張天慈(Chang Tain-Tzu)

指導教授 ： 胡毓忠

國立政治大學/理學院/資訊科學系碩士在職專班/碩士(2021年)

https://doi.org/10.6814/NCCU202101177

摘要

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。

關鍵字

深度強化學習 ； 強化學習 ； 資產配置系統 ； 風險偏好

並列摘要

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.

並列關鍵字

Deep Reinforcement Learning ； Reinforcement Learning ； Portfolio Management System ； Risk Preference ； Asset Allocation ； OpenAI gym

參考文獻

[1] Black, F., and Litterman, R. Global portfolio optimization. Financial analysts journal

48, 5 (1992), 28–43.

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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.