Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach

TL;DR

This paper introduces a volatility-guided deep reinforcement learning framework for portfolio optimization that personalizes asset selection based on investor risk profiles, improving risk-adjusted returns.

Contribution

It presents a novel integration of GARCH-based volatility categorization with DRL to tailor portfolios to individual investor preferences, enhancing adaptive asset selection.

Findings

Outperforms baseline strategies in risk-adjusted returns

Effectively categorizes stocks by volatility for personalized investment

Demonstrates robustness on Dow 30 stocks

Abstract

Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the careful pre-selection of assets that influence overall portfolio performance. Incorporating the investor's preference in pre-selecting assets for a portfolio is essential in refining their investment strategies. This study proposes a volatility-guided DRL-based portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is utilized for volatility forecasting of stocks and categorizes them based on their volatility as aggressive, moderate, and conservative. The DRL agent is then employed to learn an optimal investment policy by interacting with the historical market data. The efficacy of the proposed methodology is established using stocks from the Dow $30$ index. The proposed investor-specific DRL-based portfolios outperformed the baseline strategies by generating consistent risk-adjusted returns.