AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI

ArXiv ID: ssrn-3554486 “View on arXiv”

Authors: Unknown

Abstract

We directly optimize the objectives of portfolio management via deep reinforcement learning—an alternative to conventional supervised-learning paradigms that

Keywords: Deep Reinforcement Learning, Portfolio Optimization, Artificial Intelligence, Asset Allocation, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep reinforcement learning (RL) with attention-based neural networks (Transformers/LSTMs) and polynomial sensitivity analysis, which involves high mathematical complexity; it also provides out-of-sample performance metrics (Sharpe ratios, alphas) and robustness checks across market conditions, indicating strong empirical backing for implementation.

  flowchart TD
    A["Research Goal: Direct Portfolio Optimization via DRL"] --> B["Data: Historical Market Data & Indicators"]
    B --> C["Methodology: Deep Reinforcement Learning Framework"]
    C --> D["Process: Policy Network & Reward Function"]
    D --> E["Key Finding: End-to-End Optimization"]
    E --> F["Outcome: Superior Risk-Adjusted Returns"]