Paper: arXiv 2610.36083

Authors: 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. 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.

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; no out-of-sample test; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel framework combining LLMs and KGs for financial sentiment, demonstrating improved portfolio performance through simulations. It emphasizes explainability and contextual understanding, addressing key limitations of existing black-box models. The empirical setup appears robust, though more details on the specific LLM and full backtesting methodology would enhance rigor.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve Financial Sentiment Analysis for Algorithmic Trading with Explainability] --> B{Existing LLM Limitations};
    B --> C[Problem: Isolated Article Sentiment & Black Box Nature];
    C --> D[Proposed Solution: FinLLM-KG Framework];
    D --> E{Methodology: Combine Causal LLMs with Financial Knowledge Graph};
    E --> F[Data/Inputs: Financial Articles, SEC 10-K Filings, Company Profiles, Factual Triples];
    F --> G{Computational Process: Inference-time Knowledge Injection & Sentence/Fact Attribution};
    G --> H[Key Findings: Enhanced Contextual Understanding, Improved Explainability, Superior Portfolio Performance];