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FinSentGPT: A universal financial sentiment engine?

Author

Listed:
  • Ardekani, Aref Mahdavi
  • Bertz, Julie
  • Bryce, Cormac
  • Dowling, Michael
  • Long, Suwan(Cheng)

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.

Suggested Citation

  • Ardekani, Aref Mahdavi & Bertz, Julie & Bryce, Cormac & Dowling, Michael & Long, Suwan(Cheng), 2024. "FinSentGPT: A universal financial sentiment engine?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:finana:v:94:y:2024:i:c:s1057521924002230
    DOI: 10.1016/j.irfa.2024.103291
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    References listed on IDEAS

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    1. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    2. Li, Xiao, 2020. "When financial literacy meets textual analysis: A conceptual review," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    3. Picault, Matthieu & Pinter, Julien & Renault, Thomas, 2022. "Media sentiment on monetary policy: Determinants and relevance for inflation expectations," Journal of International Money and Finance, Elsevier, vol. 124(C).
    4. Anton Korinek, 2023. "Language Models and Cognitive Automation for Economic Research," NBER Working Papers 30957, National Bureau of Economic Research, Inc.
    5. Boyu Zhang & Hongyang Yang & Tianyu Zhou & Ali Babar & Xiao-Yang Liu, 2023. "Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models," Papers 2310.04027, arXiv.org, revised Nov 2023.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    ChatGPT; Large language models; Financial sentiment; Monetary policy; Fine-tuning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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