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arXiv Computation and Language By 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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FinLLM-KG is a model‑agnostic framework that enhances financial sentiment analysis by integrating causal large language models with a financial knowledge graph derived from SEC 10‑K filings. It injects temporally grounded, company‑level context at inference time without updating model weights, allowing sentiment predictions to consider broader firm‑level information. The framework also attributes sentiment scores to individual sentences and retrieved knowledge‑graph facts, improving explainability and supporting human‑in‑the‑loop review, and simulations show it consistently outperforms state‑of‑the‑art baselines in portfolio performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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