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

ArXiv ID: 2505.03760 “View on arXiv”

Authors: Unknown

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.

Keywords: Deep Reinforcement Learning, Portfolio Optimization, GARCH, Risk Profiling, Volatility Forecasting, Stocks (Equities)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical frameworks like GARCH models and Markov Decision Processes with complex LaTeX formulas, while also demonstrating strong empirical rigor through extensive backtesting on real market data with risk-adjusted metrics and benchmark comparisons.

  flowchart TD
    A["Research Goal<br>Investor-Specific Portfolio Optimization"] --> B["Data Acquisition<br>Dow 30 Stocks"]
    B --> C["Volatility Modeling<br>GARCH Forecasting"]
    C --> D["Asset Categorization<br>Aggressive, Moderate, Conservative"]
    D --> E["DRL Agent Training<br>Deep Reinforcement Learning"]
    E --> F["Simulation & Evaluation<br>Risk-Adjusted Returns"]
    F --> G["Outcome<br>Outperforms Baseline Strategies"]