Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?

ArXiv ID: 2503.04873 “View on arXiv”

Authors: Unknown

Abstract

Recently, large language models (LLMs) with hundreds of billions of parameters have demonstrated the emergent ability, surpassing traditional methods in various domains even without fine-tuning over domain-specific data. However, when it comes to financial sentiment analysis (FSA)$\unicode{“x2013”}$a fundamental task in financial AI$\unicode{“x2013”}$these models often encounter various challenges, such as complex financial terminology, subjective human emotions, and ambiguous inclination expressions. In this paper, we aim to answer the fundamental question: whether LLMs are good in-context learners for FSA? Unveiling this question can yield informative insights on whether LLMs can learn to address the challenges by generalizing in-context demonstrations of financial document-sentiment pairs to the sentiment analysis of new documents, given that finetuning these models on finance-specific data is difficult, if not impossible at all. To the best of our knowledge, this is the first paper exploring in-context learning for FSA that covers most modern LLMs (recently released DeepSeek V3 included) and multiple in-context sample selection methods. Comprehensive experiments validate the in-context learning capability of LLMs for FSA.

Keywords: financial sentiment analysis (FSA), in-context learning, large language models (LLMs), natural language processing, finance-specific tasks

Complexity vs Empirical Score

• Math Complexity: 1.0/10
• Empirical Rigor: 9.0/10
• Quadrant: Street Traders
• Why: The paper uses almost no advanced mathematics, relying instead on empirical experiments with multiple real-world datasets, 10 different LLMs, and various sample selection methods to validate in-context learning performance, making it highly practical and implementation-focused.

  flowchart TD
    A["Research Goal: Are LLMs good in-context learners for Financial Sentiment Analysis?"] --> B["Methodology Setup"]
    B --> C{"Prepare Data: Financial Documents + Sentiment Labels"}
    B --> D["Select In-context Samples"]
    
    C --> E["Compute Process: Prompt LLMs with In-context Examples"]
    D --> E
    
    E --> F["LLMs Analyze Sentiment of New Documents<br/>*Without Fine-tuning*"]
    
    F --> G["Key Findings/Outcomes:<br/>- LLMs demonstrate strong in-context learning<br/>- Performance varies by model size & selection method<br/>- Validated across multiple modern LLMs"]