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

Summary

FinLLM-KG is a model-agnostic framework that combines causal LLMs with a financial knowledge graph to inject temporally grounded, company-level knowledge from SEC 10-K filings at inference time without updating model weights. It supplies context via natural-language company profiles or relevance-ranked factual triples, enabling sentiment predictions that account for broader firm-level information and supports explainability, with simulations showing improved portfolio performance over the state-of-the-art baseline.

SourcearXiv Computational LinguisticsAuthor: Giorgos Iacovides, Oliver Hannibal, Wuyang Zhou, Danilo Mandic
FinLLM-KG: Knowledge Graph-Enhanced LLMs for Financial Sentiment Analysis in Algorithmic Trading
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[Submitted on 26 Oct 2026]

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

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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. 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. 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. 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.

Subjects:

Computation and Language (cs.CL); Machine Learning (cs.LG); Statistical Finance (q-fin.ST); Trading and Market Microstructure (q-fin.TR)

Cite as: arXiv:2610.36083 [cs.CL]

(or arXiv:2610.36083v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.36083

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Giorgos Iacovides [view email] [v1] Mon, 26 Oct 2026 18:08:20 UTC (851 KB)

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Key points and analysis

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Key points

  • Model-agnostic framework applicable to any causal LLM, injecting knowledge graph context at inference with near-zero computational cost.
  • Leverages a financial knowledge graph built from SEC 10-K filings to provide company-level, temporally grounded knowledge.
  • Enhances explainability by attributing sentiment scores to individual sentences and retrieved knowledge graph facts, supporting human-in-the-loop review.
  • Simulations demonstrate consistent improvement in portfolio performance over the state-of-the-art baseline.

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