用大模型提升投资决策，让预测更贴合实际操作。
Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization
- 结合大模型注意力机制捕捉资产与宏观关系，直接优化投资组合。
- 在标普100和道琼斯30指数上超越现有深度学习模型。
- 自动识别关键资产，减少预测误差对收益的影响，适合量化金融研究者。
本文针对投资组合优化中预测与决策质量脱节的问题，提出将大语言模型（LLM）与决策导向学习相结合的方法。理论与实证均表明，仅最小化预测误差会导致次优的组合决策。通过注意力机制处理资产间关系、时间依赖性及宏观变量，并直接融入投资组合优化层，模型能捕捉复杂市场动态并使预测与决策目标对齐。在S&P100和DOW30数据集上的大量实验显示，该模型持续优于当前最先进的深度学习模型。梯度分析进一步表明，模型优先关注对决策至关重要的资产，从而缓解预测误差对组合表现的影响。这些发现强调了将决策目标融入预测过程对实现更稳健、情境感知的投资管理具有重要价值。
原文摘要 · Abstract (English)
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.
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