FinLLM-KG: Knowledge Graph-Enhanced LLMs for Financial Sentiment Analysis in Algorithmic Trading

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Computer Science > Computation and Language arXiv:2610.36083 (cs) [Submitted on 26 Oct 2026] Title:FinLLM-KG: Knowledge Graph-Enhanced LLMs for Financial Sentiment Analysis in Algorithmic Trading Authors:Giorgos Iacovides, Oliver Hannibal, Wuyang Zhou, Danilo Mandic.

计算机科学 > 计算与语言 arXiv:2610.36083 (cs) [提交于 2026 年 10 月 26 日] 标题：FinLLM-KG：用于算法交易中金融情感分析的知识图谱增强型大语言模型作者：Giorgos Iacovides, Oliver Hannibal, Wuyang Zhou, Danilo Mandic。

Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). However, existing approaches remain confined to predicting the sentiment of each article in isolation and operate as black boxes, which limits contextual understanding and hinders deployment in high-stakes financial decision-making.

摘要：生成式人工智能的最新进展通过经过训练的金融大语言模型（LLM），显著改善了金融情感分析。然而，现有方法仍局限于孤立地预测每篇文章的情感，且以“黑箱”方式运行，这限制了对上下文的理解，并阻碍了其在高风险金融决策中的部署。

To this end, we introduce FinLLM-KG, a model-agnostic framework that combines causal LLMs with a financial knowledge graph, in order to inject temporally grounded, company-level knowledge from SEC 10-K filings at inference time, without updating model weights.

为此，我们引入了 FinLLM-KG，这是一个与模型无关的框架，它将因果大语言模型与金融知识图谱相结合，以便在推理时注入来自 SEC 10-K 备案文件的、具有时间基础的公司级知识，且无需更新模型权重。

Within FinLLM-KG, context is supplied either as a natural-language company profile or as a small set of relevance-ranked factual triples. This enables sentiment predictions to account for broader firm-level information rather than the article alone, at almost zero computational cost and for any causal LLM.

在 FinLLM-KG 中，上下文以自然语言公司简介或一小组按相关性排序的事实三元组形式提供。这使得情感预测能够考虑更广泛的公司层面信息，而不仅仅是文章本身，且几乎无需计算成本，并适用于任何因果大语言模型。

In addition, attributing each article-level sentiment score to individual sentences and to the retrieved knowledge-graph facts makes the evidence behind the prediction inspectable, thereby enhancing explainability and supporting human-in-the-loop review. Simulations demonstrate that FinLLM-KG consistently improves portfolio performance over the state-of-the-art baseline.

此外，将每篇文章层面的情感得分归因于单个句子和检索到的知识图谱事实，使得预测背后的证据可被审查，从而增强了可解释性并支持“人在回路”的审查。模拟结果表明，FinLLM-KG 在投资组合表现上始终优于最先进的基准模型。