基于波动率筛选资产，用强化学习为不同风险偏好的投资者定制投资组合。
Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
- 用GARCH模型按波动率分类股票，指导DRL Agent选资产。
- 在道琼斯30指数上，新方法显著提升风险调整后收益。
- 适合个性化投资策略研究者或量化交易开发者参考。
投资组合优化需在动态市场中权衡风险与回报，实现资金的动态配置。随着人工智能发展，深度强化学习（DRL）在提供自适应、可扩展的投资策略方面日益重要。然而，这些策略的成功不仅依赖于对市场动态的适应能力，还取决于对影响整体表现的关键资产进行精心预筛选。将投资者偏好融入资产预筛选过程，对于优化投资策略至关重要。本文提出一种基于波动率引导的DRL投资组合优化框架，根据投资者的风险偏好动态构建投资组合。利用广义自回归条件异方差（GARCH）模型对股票波动率进行预测，并据此将股票分为激进型、稳健型和保守型三类。随后，通过与历史市场数据交互，训练DRL智能体学习最优投资策略。实验采用道琼斯30指数中的股票验证该方法的有效性。结果表明，所提出的投资者定制化DRL投资组合相比基线策略，持续实现了更高的风险调整后收益。
原文摘要 · Abstract (English)
Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the careful pre-selection of assets that influence overall portfolio performance. Incorporating the investor's preference in pre-selecting assets for a portfolio is essential in refining their investment strategies. This study proposes a volatility-guided DRL-based portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is utilized for volatility forecasting of stocks and categorizes them based on their volatility as aggressive, moderate, and conservative. The DRL agent is then employed to learn an optimal investment policy by interacting with the historical market data. The efficacy of the proposed methodology is established using stocks from the Dow $30$ index. The proposed investor-specific DRL-based portfolios outperformed the baseline strategies by generating consistent risk-adjusted returns.
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