2023/06/12 by Li, Zhenglong, Huang, Hejun, Tam, Vincent
Decision Sciences · Economics, Econometrics and Finance · #Advanced Bandit Algorithms Research #Artificial intelligence #Business #Computational Engineering #Computer science #Economics #FOS: Computer and information sciences #FOS: Economics and business #Finance #Financial Markets and Investment Strategies #Financial market #Investment (military) #Investment strategy #Market liquidity #Portfolio #Portfolio Management (q-fin.PM) #Portfolio optimization #Project management #Project portfolio management #Reinforcement learning #Risk Management (q-fin.RM) #Risk analysis (engineering) #Risk management #Stock Market Forecasting Methods #Volatility (finance) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2306.07013
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.