Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization

Understand

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.