Researchers publish paper on FinLLM-KG financial sentiment analysis framework
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Researchers including Giorgos Iacovides have released a paper proposing the model-agnostic FinLLM-KG framework, which combines causal large language models with financial knowledge graphs, injecting time-anchored company-level knowledge from SEC 10-K filings during the inference stage without updating model weights. This framework can provide context in the form of natural language company profiles or a small number of relevance-ranked factual triples, with almost no additional computational cost, while supporting the attribution of sentiment predictions to specific sentences and knowledge graph facts, improving interpretability. Simulation results show that FinLLM-KG consistently improves portfolio performance compared to existing optimal baselines.
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- arXiv q-finFinLLM-KG: Knowledge Graph-Enhanced Large Language Model for Financial Sentiment Analysis in Algorithmic Trading
The study proposes a model-agnostic FinLLM-KG framework that combines causal large language models with financial knowledge graphs, injecting time-anchored company-level knowledge from SEC 10-K filings during inference without updating model weights. The framework can provide context as natural language company profiles or a small number of relevance-ranked factual triples, with almost no additional computational cost, while supporting attribution of sentiment predictions to specific sentences and knowledge graph facts, improving interpretability. Simulation results show that FinLLM-KG consistently improves portfolio performance compared to existing optimal baselines.
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