Ardekani, Aref Mahdavi, Bertz, Julie, Bryce, Cormac, Dowling, Michael
ORCID: 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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