Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT

• URL: http://arxiv.org/abs/2410.01987v1
• Date: Wed, 2 Oct 2024 19:48:17 GMT
• Title: Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT
• Authors: Yanxin Shen, Pulin Kirin Zhang,
• Abstract summary: This paper investigates the application of large language models (LLMs) and FinBERT for financial sentiment analysis. The study emphasizes the advantages of prompt engineering with zero-shot and few-shot strategy to improve sentiment classification accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Financial sentiment analysis (FSA) is crucial for evaluating market sentiment and making well-informed financial decisions. The advent of large language models (LLMs) such as BERT and its financial variant, FinBERT, has notably enhanced sentiment analysis capabilities. This paper investigates the application of LLMs and FinBERT for FSA, comparing their performance on news articles, financial reports and company announcements. The study emphasizes the advantages of prompt engineering with zero-shot and few-shot strategy to improve sentiment classification accuracy. Experimental results indicate that GPT-4o, with few-shot examples of financial texts, can be as competent as a well fine-tuned FinBERT in this specialized field.

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