Abstract
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.
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