Sentiment Spin: Attacking Financial Sentiment with GPT-3

Markus Leippold
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Markus Leippold: University of Zurich; Swiss Finance Institute

No 23-11, Swiss Finance Institute Research Paper Series from 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.

Keywords: sentiment analysis in financial markets; keyword-based approach; FinBERT; GPT-3 (search for similar items in EconPapers)
JEL-codes: C8 G2 G38 M48 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2023-02
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2311

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