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Risk Averse Reinforcement Learning for Portfolio Optimization
Bayaraa Enkhsaikhan,Ohyun Jo(조오현) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
In this paper, we investigate investment portfolio optimization using Reinforcement Learning (RL) with risk assessment. Due to market friction, the reaction of other market participants and uncertainties, it is challenging to trade and optimize investment portfolios dynamically. The financial market is sophisticated and complex to model. Moreover, regulatory requirement and internal risk policy require investors to make risk-averse decisions for preventing catastrophic results, which is hard to recover later. One way to solve the problem is to set a high enough penalty to reward for the risk. As the experiment result suggests, the proposed Value at Risk(VaR) technique using Actor-Critic reinforcement learning could benefit faster learning and reward.
Risk-averse Reinforcement Learning for Portfolio Optimization
Enkhsaikhan Bayaraa,Jo Ohyun 한국통신학회 2024 ICT Express Vol.10 No.4
This investigation explores Reinforcement Learning (RL) for dynamic portfolio optimization with risk assessment. The challenges include market complexity, uncertain reactions, and regulatory requirements for risk-averse decisions. Our solution leverages Bayesian Neural Network (BNN) to capture uncertainties. We successfully implemented a risk-averse Reinforcement Learning algorithm, achieving 18 percent lower risk. Reinforcement Learning with risk-aversion shows promise for optimizing portfolios for risk-averse investors.