Paper: arXiv 2412.10823

Abstract

Financial sentiment analysis is crucial for understanding the influence of news on stock prices. Recently, large language models (LLMs) have been widely adopted for this purpose due to their advanced text analysis capabilities. However, these models often only consider the news content itself, ignoring its dissemination, which hampers accurate prediction of short-term stock movements. Additionally, current methods often lack sufficient contextual data and explicit instructions in their prompts, limiting LLMs’ ability to interpret news. In this paper, we propose a data-driven approach that enhances LLM-powered sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions. We cluster recent company-related news to assess its reach and influence, enriching prompts with more specific data and precise instructions. This data is used to construct an instruction tuning dataset to fine-tune an LLM for predicting short-term stock price movements. Our experimental results show that our approach improves prediction accuracy by 8% compared to existing methods.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper’s methodology centers on data engineering (clustering, fine-tuning) and empirical evaluation with an 8% improvement over baselines, which is highly practical and implementation-heavy. The mathematics is relatively simple, relying on standard NLP techniques and statistical metrics rather than advanced theoretical derivations.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict Short-Term Stock Movements"] --> B["Input Data<br>Financial News & Company Data"]
  B --> C["Methodology: Dissemination & Context"]
  C --> D["Step 1: News Clustering & Reach Analysis"]
  C --> E["Step 2: Enrich Prompts with Context & Instructions"]
  D & E --> F["Step 3: Instruction Tuning Dataset Construction"]
  F --> G["Step 4: Fine-Tune LLM"]
  G --> H["Outcome: Enhanced Sentiment Prediction Model"]
  H --> I["Findings<br>8% Improvement in Accuracy"]

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7 of 44 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.