chatGPT 在股票市场辅助应用.pdf

Roussanov, and ‘@jugglingnumbers.’ Emails: Alejandro Lopez-Lira (corresponding author): alejandro.lopez-

lira@warrington.ufl.edu, and Yuehua Tang: yuehua.tang@warrington.ufl.edu.

the extent that these models are more capable of understanding natural language, one could

argue that they could be a valuable tool for processing textual information to predict stock

returns. Thus, the performance of LLMs in predicting financial market movements is an

open question.

by evaluating the capabilities of ChatGPT in forecasting stock market returns. Through a

novel approach that leverages the model’s sentiment analysis capabilities, we assess the

performance of ChatGPT using news headlines data and compare it to existing sentiment

analysis methods provided by leading vendors.

benefits and risks associated with the increasing adoption of LLMs in financial markets. As

these models become more prevalent, their influence on market behavior, information dissem-

ination, and price formation will become critical areas of concern. Our findings can inform

discussions on regulatory frameworks that govern the use of AI in finance and contribute to

applications in finance. By exploring the capabilities of ChatGPT in predicting stock market

returns, we advance the understanding of LLMs’ potential and limitations within the financial

economics domain. This can inspire future research on developing more sophisticated LLMs

tailored to the financial industry’s needs, paving the way for more efficient and accurate

financial decision-making.

stock market predictions. By shedding light on the potential contributions of ChatGPT to

financial economics, we hope to encourage continued exploration and innovation in AI-driven

finance.

Recent papers that use ChatGPT in the context of economics include Hansen and Kazinnik

(2023), Cowen and Tabarrok (2023), Korinek (2023), and Noy and Zhang (2023). Hansen and

Kazinnik (2023) show that LLMs like ChatGPT can decode Fedspeak (i.e., the language used

chine learning to study a variety of finance research questions (e.g., Jegadeesh and Wu

(2013), Campbell et al. (2014), Hoberg and Phillips (2016), Gaulin (2017), Baker, Bloom,

and Davis (2016), Manela and Moreira (2017), Hansen, McMahon, and Prat (2018), Ke,

Kelly, and Xiu (2019), Ke, Montiel Olea, and Nesbit (2019), Bybee et al. (2019), Gu, Kelly,

and Xiu (2020), Cohen, Malloy, and Nguyen (2020), Freyberger, Neuhierl, and Weber (2020),

Lopez-Lira 2019, Binsbergen et al. (2020), Bybee et al. (2021)). Our paper makes a unique

contribution to this literature as being the first to evaluate the text processing capabilities

of recently developed LLMs such as ChatGPT in forecasting stock market movements.

reactions.

to AI-related technology. Recent works by Agrawal, Gans, and Goldfarb (2019), Webb

(2019), Acemoglu et al. (2022), Acemoglu and Restrepo (2022), Babina et al. (2022), Noy

and Zhang (2023) have examined the extent of job exposure and vulnerability to AI-related