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FinSentGPT: A universal financial sentiment engine?

Mahdavi Ardekani, A., Bertz, J., Bryce, C. ORCID: 0000-0002-9856-7851 , Dowling, M. & Chen, S. (2024). FinSentGPT: A universal financial sentiment engine?. International Review of Financial Analysis,


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 analyze 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.

Publication Type: Article
Additional Information: © 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license in new tab/window)
Publisher Keywords: ChatGPT; large language models; financial sentiment; monetary policy; fine- tuning
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Bayes Business School
Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
[thumbnail of ChatGPT_as_a_universal_economic_sentiment_engine.pdf] Text - Accepted Version
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