Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent

ArXiv ID: 2407.14486 “View on arXiv”

Authors: Unknown

Abstract

Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set on assumptions that are not supported by data in high volatility markets. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches. In particular, DRL algorithms train an agent by estimating the distribution of the expected reward of every action performed by an agent given any financial state in a simulator. However, these methods rely on Deep Neural Networks model to represent such a distribution, that although they are universal approximator models, they cannot explain its behaviour, given by a set of parameters that are not interpretable. Critically, financial investors policies require predictions to be interpretable, so DRL agents are not suited to follow a particular policy or explain their actions. In this work, we developed a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management, integrating the Proximal Policy Optimization (PPO) with the model agnostic explainable techniques of feature importance, SHAP and LIME to enhance transparency in prediction time. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy or to assess the risk of following the agent suggestions. To the best of our knowledge, our proposed approach is the first explainable post hoc portfolio management financial policy of a DRL agent. We empirically illustrate our methodology by successfully identifying key features influencing investment decisions, which demonstrate the ability to explain the agent actions in prediction time.

Keywords: Explainable Deep Reinforcement Learning (XDRL), Proximal Policy Optimization (PPO), SHAP, LIME, Portfolio Management, Portfolio Management

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 3.5/10
• Quadrant: Lab Rats
• Why: The paper employs advanced deep reinforcement learning (PPO) and XAI techniques (SHAP, LIME), requiring significant mathematical complexity. However, the empirical validation appears theoretical and lacks concrete backtesting metrics, code, or implementation details, placing it in the theoretical research quadrant.

  flowchart TD
    A["Research Goal:<br>Explainable DRL Portfolio Management"] --> B["Data Input:<br>Financial Market Simulator"]
    B --> C["Core Methodology:<br>Explainable XDRL<br>PPO + SHAP/LIME"]
    C --> D["Computational Process:<br>Training Agent &<br>Generating Explanations"]
    D --> E["Key Outcome 1:<br>Identified Key Features<br>for Investment Decisions"]
    D --> F["Key Outcome 2:<br>Post-hoc Interpretability<br>of DRL Actions"]
    E --> G["Conclusion:<br>Validated Transparent &<br>Explainable Financial Policy"]
    F --> G