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FinSentGPT: A universal financial sentiment engine?

Ardekani, Aref Mahdavi, Bertz, Julie, Bryce, Cormac, Dowling, Michael orcid logoORCID: 0000-0002-3539-8843 and Long, Suwan (Cheng) (2024) FinSentGPT: A universal financial sentiment engine? International Review of Financial Analysis, 94 . ISSN 1057-5219

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.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:ChatGPT; Large language models; Financial sentiment; Monetary policy; Fine-tuning
Subjects:Business > Economics
Business > Finance
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
Publisher:Elsevier
Official URL:https://www.sciencedirect.com/science/article/pii/...
Copyright Information:Authors
ID Code:32682
Deposited On:21 May 2026 16:47 by Tam Nguyen . Last Modified 21 May 2026 16:47
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