Reinforcement learning (RL) based investment strategies have been widely adopted in portfolio management (PM) in recent years. Nevertheless, most RL-based approaches may often emphasize on pursuing returns while ignoring the risks of the underlying trading strategies that may potentially lead to great losses especially under high market volatility. Therefore, a risk-manageable PM investment framework integrating both RL and barrier functions (BF) is proposed to carefully balance the needs for high returns and acceptable risk exposure in PM applications. Up to our understanding, this work represents the first attempt to combine BF and RL for financial applications. While the involved RL approach may aggressively search for more profitable trading strategies, the BF-based risk controller will continuously monitor the market states to dynamically adjust the investment portfolio as a controllable measure for avoiding potential losses particularly in downtrend markets. Additionally, two adaptive mechanisms are provided to dynamically adjust the impact of risk controllers such that the proposed framework can be flexibly adapted to uptrend and downtrend markets. The empirical results of our proposed framework clearly reveal such advantages against most well-known RL-based approaches on real-world data sets. More importantly, our proposed framework shed lights on many possible directions for future investigation.

翻译：基于强化学习的投资策略近年来在投资组合管理中广泛应用。然而，大多数基于强化学习的方法往往侧重追求收益，而忽略了交易策略的潜在风险，尤其是在市场高波动性下可能导致巨大损失。为此，提出了一种融合强化学习与障碍函数的风险可控投资组合管理框架，旨在平衡投资组合管理中对高收益与可接受风险暴露的需求。据我们所知，本研究首次将障碍函数与强化学习结合应用于金融领域。其中，强化学习方法可积极寻求更高收益的交易策略，而基于障碍函数的风险控制器将持续监控市场状态，动态调整投资组合，作为避免潜在损失（尤其在市场下行趋势中）的可控措施。此外，框架提供了两种自适应机制以动态调整风险控制器的影响强度，使其能够灵活适应上涨与下跌市场。在真实数据集上的实证结果清晰表明，该框架相较于多数知名强化学习方法具有显著优势。更重要的是，本框架为未来研究开辟了多个探索方向。