IDEAS home Printed from https://ideas.repec.org/p/bdi/opques/qef_814_23.html
   My bibliography  Save this paper

Loquacity and visible emotion: ChatGPT as a policy advisor

Author

Listed:
  • Claudia Biancotti

    (Bank of Italy)

  • Carolina Camassa

    (Bank of Italy)

Abstract

ChatGPT, a software seeking to simulate human conversational abilities, is attracting increasing attention. It is sometimes portrayed as a groundbreaking productivity aid, including for creative work. In this paper, we run an experiment to assess its potential in complex writing tasks. We ask the software to compose a policy brief for the Board of the Bank of Italy. We find that ChatGPT can accelerate workflows by providing well-structured content suggestions, and by producing extensive, linguistically correct text in a matter of seconds. It does, however, require a significant amount of expert supervision, which partially offsets productivity gains. If the app is used naively, output can be incorrect, superficial, or irrelevant. Superficiality is an especially problematic limitation in the context of policy advice intended for high-level audiences.

Suggested Citation

  • Claudia Biancotti & Carolina Camassa, 2023. "Loquacity and visible emotion: ChatGPT as a policy advisor," Questioni di Economia e Finanza (Occasional Papers) 814, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_814_23
    as

    Download full text from publisher

    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2023-0814/QEF_814_23.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2023.
    2. Andrea L. Eisfeldt & Gregor Schubert & Miao Ben Zhang, 2023. "Generative AI and Firm Values," NBER Working Papers 31222, National Bureau of Economic Research, Inc.
    3. Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI," Papers 2310.17721, arXiv.org.
    2. Dat Mai, 2024. "StockGPT: A GenAI Model for Stock Prediction and Trading," Papers 2404.05101, arXiv.org, revised Apr 2024.
    3. Lezhi Li & Ting-Yu Chang & Hai Wang, 2023. "Multimodal Gen-AI for Fundamental Investment Research," Papers 2401.06164, arXiv.org.
    4. Thanos Konstantinidis & Giorgos Iacovides & Mingxue Xu & Tony G. Constantinides & Danilo Mandic, 2024. "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications," Papers 2403.12285, arXiv.org.
    5. Frank Xing, 2024. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis," Papers 2401.05799, arXiv.org.
    6. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    7. Seppälä, Timo & Mucha, Tomasz & Mattila, Juri, 2023. "Beyond AI, Blockchain Systems, and Digital Platforms: Digitalization Unlocks Mass Hyper-Personalization and Mass Servitization," ETLA Working Papers 106, The Research Institute of the Finnish Economy.
    8. Samir Huseynov, 2023. "ChatGPT and the Labor Market: Unraveling the Effect of AI Discussions on Students' Earnings Expectations," Papers 2305.11900, arXiv.org, revised Aug 2023.
    9. Zhaofeng Zhang & Banghao Chen & Shengxin Zhu & Nicolas Langren'e, 2024. "From attention to profit: quantitative trading strategy based on transformer," Papers 2404.00424, arXiv.org.
    10. Hanshuang Tong & Jun Li & Ning Wu & Ming Gong & Dongmei Zhang & Qi Zhang, 2024. "Ploutos: Towards interpretable stock movement prediction with financial large language model," Papers 2403.00782, arXiv.org.
    11. Udit Gupta, 2023. "GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models," Papers 2309.03079, arXiv.org.
    12. Haohan Zhang & Fengrui Hua & Chengjin Xu & Jian Guo & Hao Kong & Ruiting Zuo, 2023. "Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?," Papers 2306.14222, arXiv.org.
    13. Ummara Mumtaz & Summaya Mumtaz, 2023. "Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis," Papers 2311.06273, arXiv.org.
    14. Georgios Fatouros & Konstantinos Metaxas & John Soldatos & Dimosthenis Kyriazis, 2024. "Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection," Papers 2401.03737, arXiv.org, revised Apr 2024.
    15. Wentao Zhang & Lingxuan Zhao & Haochong Xia & Shuo Sun & Jiaze Sun & Molei Qin & Xinyi Li & Yuqing Zhao & Yilei Zhao & Xinyu Cai & Longtao Zheng & Xinrun Wang & Bo An, 2024. "A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist," Papers 2402.18485, arXiv.org, revised Feb 2024.
    16. Marius Hofert, 2023. "Correlation Pitfalls with ChatGPT: Would You Fall for Them?," Risks, MDPI, vol. 11(7), pages 1-17, June.
    17. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org.
    18. Zihan Chen & Lei Nico Zheng & Cheng Lu & Jialu Yuan & Di Zhu, 2023. "ChatGPT Informed Graph Neural Network for Stock Movement Prediction," Papers 2306.03763, arXiv.org, revised Sep 2023.
    19. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    20. Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez & Thomas Jacquot, 2024. "Stress index strategy enhanced with financial news sentiment analysis for the equity markets," Papers 2404.00012, arXiv.org.

    More about this item

    Keywords

    Large language models; generative artificial intelligence; ChatGPT;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:opques:qef_814_23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.