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

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  • Yanxin Shen
  • Pulin Kirin Zhang

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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  • Yanxin Shen & Pulin Kirin Zhang, 2024. "Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT," Papers 2410.01987, arXiv.org.
    • Handle: RePEc:arx:papers:2410.01987
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    References listed on IDEAS

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    2. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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