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Sentiment Spin: Attacking Financial Sentiment with GPT-3

Author

Listed:
  • Markus Leippold

    (University of Zurich; Swiss Finance Institute)

Abstract

The use of dictionaries in financial sentiment analysis and other financial and economic applications remains widespread because keyword-based methods appear more transparent and explainable than more advanced techniques commonly used in computer science. However, this paper demonstrates the vulnerability of using dictionaries by exploiting the eloquence of GPT-3, a sophisticated transformer model, to generate successful adversarial attacks on keyword-based approaches with a success rate close to 99% for negative sentences in the financial phrase base, a well-known human-annotated database for financial sentiment analysis. In contrast, more advanced methods, such as those using context-aware approaches like BERT, remain robust.

Suggested Citation

  • Markus Leippold, 2023. "Sentiment Spin: Attacking Financial Sentiment with GPT-3," Swiss Finance Institute Research Paper Series 23-11, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2311
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4337182
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    Citations

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    Cited by:

    1. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    2. Rick Steinert & Saskia Altmann, 2023. "Linking microblogging sentiments to stock price movement: An application of GPT-4," Papers 2308.16771, arXiv.org.
    3. Li Xian Liu & Zhiyue Sun & Kunpeng Xu & Chao Chen, 2024. "AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges," IJFS, MDPI, vol. 12(3), pages 1-35, June.
    4. Sui, Cong & Wang, Shuhan & Zheng, Wei, 2024. "Sentiment as a shipping market predictor: Testing market-specific language models," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    5. Smales, Lee A., 2023. "Classification of RBA monetary policy announcements using ChatGPT," Finance Research Letters, Elsevier, vol. 58(PC).
    6. Bingler, Julia Anna & Kraus, Mathias & Leippold, Markus & Webersinke, Nicolas, 2024. "How cheap talk in climate disclosures relates to climate initiatives, corporate emissions, and reputation risk," Journal of Banking & Finance, Elsevier, vol. 164(C).
    7. Olamilekan Shobayo & Sidikat Adeyemi-Longe & Olusogo Popoola & Bayode Ogunleye, 2024. "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach," Papers 2412.06837, arXiv.org.
    8. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.

    More about this item

    Keywords

    sentiment analysis in financial markets; keyword-based approach; FinBERT; GPT-3;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation

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