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Classification of RBA monetary policy announcements using ChatGPT

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  • Smales, Lee A.

Abstract

ChatGPT is a tool that has gained much attention and has accelerated the adoption of Artificial Intelligence across many applications. Using monetary policy decisions made by the Reserve Bank of Australia (RBA), we test whether ChatGPT's classifications of monetary policy announcements are consistent with market-observed characteristics, whether this has changed with the update from GPT-3.5 to GPT-4, and whether this provides additional informativeness for changes in the yield implied by interest rate futures. Our results indicate that, regardless of statement readability, ChatGPT provides a classification of hawkish / dovish tone that is consistent with “Target Surprises” and “Path Surprises” and although it seems to improve in the latest version has more difficulty with “sentiment” classification. However, this information does not appear to help to explain changes in interest rate markets on the day of the policy announcement.

Suggested Citation

  • Smales, Lee A., 2023. "Classification of RBA monetary policy announcements using ChatGPT," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008863
    DOI: 10.1016/j.frl.2023.104514
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    References listed on IDEAS

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    Cited by:

    1. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    2. Zifeng Feng & Gangqing Hu & Bingxin Li & Jinge Wang, 2025. "Unleashing the power of ChatGPT in finance research: opportunities and challenges," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-26, December.
    3. Arata ITO & Masahiro SATO & Rui OTA, 2024. "Content-based Metric on Monetary Policy Uncertainty by Using Large Language Models," Discussion papers 24080, Research Institute of Economy, Trade and Industry (RIETI).
    4. Can Celebi & Stefan Penczynski, 2024. "Using Large Language Models for Text Classification in Experimental Economics," Working Paper series, University of East Anglia, Centre for Behavioural and Experimental Social Science (CBESS) 24-01, School of Economics, University of East Anglia, Norwich, UK..
    5. Pelster, Matthias & Val, Joel, 2024. "Can ChatGPT assist in picking stocks?," Finance Research Letters, Elsevier, vol. 59(C).
    6. Julian Junyan Wang & Victor Xiaoqi Wang, 2025. "Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks," Papers 2503.16974, arXiv.org, revised Mar 2025.
    7. Beckmann, Lars & Hark, Paul F., 2024. "ChatGPT and the banking business: Insights from the US stock market on potential implications for banks," Finance Research Letters, Elsevier, vol. 63(C).

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    More about this item

    Keywords

    Artificial Intelligence (AI); ChatGPT; Monetary Policy Communication; Reserve Bank of Australia (RBA);
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G1 - Financial Economics - - General Financial Markets

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