Paper: arXiv 2505.03760

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (16)

  1. Foundations of portfolio theory (1991)
  2. Performance functions and reinforcement learning for trading systems and portfolios (1998)
  3. A deep reinforcement learning framework for the financial portfolio management problem (2017) — link
  4. Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory (2023)
  5. Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization (2020)
  6. Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach (2024)
  7. DeepTrader: a deep reinforcement learning approach for risk-return balanced portfolio management with market conditions Embedding (2021)
  8. Recurrent ensemble random vector functional link neural network for financial time series forecasting (2024)
and 8 more
  1. Generalized autoregressive conditional heteroskedasticity (1986)
  2. Portfolio formation with preselection using deep learning from long-term financial data (2020)
  3. A multiobjective multiperiod portfolio selection approach with different investor attitudes under an uncertain environment (2024)
  4. Reinforcement learning: An introduction (2018)
  5. Proximal policy optimization algorithms (2017) — link
  6. FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance (2020) — link
  7. Dynamic Reinforced Ensemble using Bayesian Optimization for Stock Trading (2024)
  8. Forecasting volatility in the financial markets (2011)

0 of 16 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.