FinSentGPT: A universal financial sentiment engine?

Aref Mahdavi Ardekani, Julie Bertz, Cormac Bryce, Michael Dowling and Long, Suwan(Cheng)

International Review of Financial Analysis, 2024, vol. 94, issue C

Abstract: We present FinSentGPT, a financial sentiment prediction model based on a fine-tuned version of the artificial intelligence language model, ChatGPT. To assess the model’s effectiveness, we analyse a sample of US media news and a multi-language dataset of European Central Bank Monetary Policy Decisions. Our findings demonstrate that FinSentGPT’s sentiment classification ability aligns well with a prominent English-language finance sentiment model, surpasses an established alternative machine learning model, and is capable of predicting sentiment across various languages. Consequently, we offer preliminary evidence that advanced large-language AI models can facilitate flexible and contextual financial sentiment determination, transcending language barriers.

Keywords: ChatGPT; Large language models; Financial sentiment; Monetary policy; Fine-tuning (search for similar items in EconPapers)
JEL-codes: C22 F47 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:94:y:2024:i:c:s1057521924002230

DOI: 10.1016/j.irfa.2024.103291

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