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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
Chang, Tain-Tzu
Keywords: 深度強化學習
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.
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Data Type: thesis
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