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

Paper: arXiv 2502.00828

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.

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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

Cited References (69)

1. Portfolio Selection (1952) — link
2. The Markowitz optimization enigma: Is ‘optimized’optimal? (1989)
3. Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? (2009)
4. The effect of errors in means, variances, and covariances on optimal portfolio choice (1993)
5. A well-conditioned estimator for large-dimensional covariance matrices (2004)
6. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection (2003)
7. Empirical asset pricing via machine learning (2020)
8. Smart “predict, then optimize” (2022)

3 of 69 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.