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

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 ap...

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Bibliographic Details
Published inIEEE International Conference on Power, Intelligent Computing and Systems (Online) pp. 717 - 721
Main Authors Shen, Yanxin, Zhang, Pulin Kirin
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.07.2024
Subjects
Accuracy
Analytical models
BERT
Computational modeling
Context modeling
FinBERT
Fluctuations
Large language models
NLP
Prompt engineering
Real-time systems
Sentiment analysis
Zero shot learning
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ISSN2834-8567
DOI10.1109/ICPICS62053.2024.10796670

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Summary: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 -40, with few-shot examples of financial texts, can be as competent as a well fine-tuned FinBERT in this specialized field.
ISSN:2834-8567
DOI:10.1109/ICPICS62053.2024.10796670