Paper: arXiv 2502.02619

Abstract

This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional portfolio construction, our approach aims to improve an already high-performing strategy through dynamic rebalancing driven by PPO and Oracle agents. Our target is to enhance the traditional 60/40 benchmark (60% stocks, 40% bonds) by employing the Regret-based Sharpe reward function. To address the impact of transaction fee frictions and prevent signal loss, we develop a transaction cost scheduler. We introduce a future-looking reward function and employ synthetic data training through a circular block bootstrap method to facilitate the learning of generalizable allocation strategies. We focus on two key evaluation measures: return and maximum drawdown. Given the high stochasticity of financial markets, we train 20 independent agents each period and evaluate their average performance against the benchmark. Our method not only enhances the performance of the existing portfolio strategy through strategic rebalancing but also demonstrates strong results compared to other baselines.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced deep reinforcement learning techniques like PPO with custom regret-based reward functions and synthetic data generation, resulting in high mathematical complexity. It demonstrates strong empirical rigor through specific backtest metrics, transaction cost modeling, and robust evaluation across training, validation, and test sets with ablation studies.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Enhance 60/40 Portfolio using<br>Deep Reinforcement Learning"] --> B["Data Input<br>Synthetic Data via<br>Circular Block Bootstrap"]
  
  B --> C["Methodology<br>Proximal Policy Optimization<br>with Regret-based Sharpe Reward"]
  
  C --> D["Key Innovation<br>Future-Looking Reward &<br>Transaction Cost Scheduler"]
  
  D --> E["Computation<br>Train 20 Independent Agents<br>Average Stochastic Performance"]
  
  E --> F["Outcomes<br>Enhanced Returns & Reduced<br>Max Drawdown vs. Baselines"]
  
  F --> G["Conclusion<br>Effective Dynamic Rebalancing<br>with Strong Generalization"]

Cited References (50)

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  5. High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning — link
  6. Deep Direct Reinforcement Learning for Financial Signal Representation and Trading — link
  7. Practical Deep Reinforcement Learning Approach for Stock Trading — link
  8. Deep Reinforcement Learning for Asset Allocation in US Equities — link
and 42 more
  1. A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem — link
  2. Embedded draw-down constraint reward function for deep reinforcement learning — link
  3. Headline-Driven Classification and Local Interpretation for Market Outperformance and Low-Risk Stock Prediction — link
  4. Combining deep reinforcement learning with technical analysis and trend monitoring on cryptocurrency markets (2023)
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2 of 50 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.