2024/10/02 by Shen, Yanxin, Pulin Kirin Zhang, Zhang, Pulin Kirin · 3 citations
Computer Science · Decision Sciences · Social Sciences · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #FOS: Economics and business #General Finance (q-fin.GN) #Information Retrieval (cs.IR) #Social and Information Networks (cs.SI) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2410.01987
openalex publication_date 2024/10/02 · openalex created_date 2024/10/30 · openalex updated_date 2026/07/28
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.