Deep Reinforcement Learning for Portfolio Allocation

ArXiv ID: ssrn-3886804 “View on arXiv”

Authors: Unknown

Abstract

In 2013, a paper by Google DeepMind kicked off an explosion in Deep Reinforcement Learning (DRL), for games. In this talk, we show that DRL can also be applied

Keywords: Deep Reinforcement Learning, Algorithmic Trading, Artificial Intelligence, Financial Markets

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics (reinforcement learning, optimization, Shapley values) and demonstrates strong empirical rigor with detailed backtesting methodology, specific datasets, performance metrics, and sensitivity analysis for real-world implementation.

  flowchart TD
    Goal["Research Goal: Apply DRL to Portfolio Allocation"] --> Method["Methodology: Deep Q-Network (DQN) Algorithm"]
    Method --> Input["Data Inputs: Historical Price Data & Market Indicators"]
    Input --> Proc["Computational Process: Training Agent on Simulated Market"]
    Proc --> Find1["Outcome 1: Dynamic Asset Weighting"]
    Proc --> Find2["Outcome 2: Risk-Adjusted Return Optimization"]
    Find1 --> End["Conclusion: DRL Viable for Financial Markets"]
    Find2 --> End