Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization

ArXiv ID: 2502.00828 “View on arXiv”

Authors: Unknown

Abstract

This paper addresses the critical disconnect between prediction and decision quality in portfolio optimization by integrating Large Language Models (LLMs) with decision-focused learning. We demonstrate both theoretically and empirically that minimizing the prediction error alone leads to suboptimal portfolio decisions. We aim to exploit the representational power of LLMs for investment decisions. An attention mechanism processes asset relationships, temporal dependencies, and macro variables, which are then directly integrated into a portfolio optimization layer. This enables the model to capture complex market dynamics and align predictions with the decision objectives. Extensive experiments on S&P100 and DOW30 datasets show that our model consistently outperforms state-of-the-art deep learning models. In addition, gradient-based analyses show that our model prioritizes the assets most crucial to decision making, thus mitigating the effects of prediction errors on portfolio performance. These findings underscore the value of integrating decision objectives into predictions for more robust and context-aware portfolio management.

Keywords: Decision-focused Learning, Portfolio Optimization, Attention Mechanism, LLM Integration, Gradient-based Analysis, Stocks

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including attention mechanisms, gradient-based analysis for differentiating through convex optimization, and hybrid loss functions combining statistical and decision objectives. It demonstrates empirical rigor through extensive experiments on S&P100 and DOW30 datasets, comparing against state-of-the-art models, and includes performance metrics and gradient-based interpretability analyses.

  flowchart TD
    A["Research Goal: Improve Portfolio Optimization by aligning predictions with decisions"] --> B{"Data & Inputs"};
    B --> B1["S&P100 & DOW30<br>Price Data"];
    B --> B2["Asset Relationships<br>Macro Variables"];
    B --> B3["Historical Outcomes"];
    
    B --> C["Methodology: LLM-Decision Focused Learning"];
    C --> D["Attention Mechanism<br>Processes temporal & relational data"];
    D --> E["Decision Layer<br>Portfolio Optimization Layer"];
    
    E --> F{"Computational Process"};
    F --> F1["Predict Asset Returns"];
    F --> F2["Optimize Portfolio Allocation"];
    F --> F3["Compute Decision Loss"];
    
    F --> G["Key Findings & Outcomes"];
    G --> G1["Outperforms SOTA Models"];
    G --> G2["Gradient Analysis shows focus on crucial assets"];
    G --> G3["Minimizes prediction error impact on decisions"];
    
    style A fill:#e1f5fe
    style C fill:#fff3e0
    style G fill:#e8f5e8