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Can ChatGPT Forecast Stock Price Movements?

Return Predictability and Large Language Models

∗

Alejandro Lopez-Lira and Yuehua Tang

University of Florida

First Version: April 6, 2023

This Version May 11, 2023

Abstract

We examine the potential of ChatGPT, and other large language models, in predicting

stock market returns using sentiment analysis of news headlines. We use ChatGPT to indi-

cate whether a given headline is good, bad, or irrelevant news for ﬁrms’ stock prices. We

then compute a numerical score and document a positive correlation between these “ChatGPT

scores” and subsequent daily stock market returns. Further, ChatGPT outperforms traditional

sentiment analysis methods. We ﬁnd that more basic models such as GPT-1, GPT-2, and

BERT cannot accurately forecast returns, indicating return predictability is an emerging ca-

pacity of complex models. ChatGPT-4’s implied Sharpe ratios are larger than ChatGPT-3’s;

however, the latter model has larger total returns. Our results suggest that incorporating ad-

vanced language models into the investment decision-making process can yield more accurate

predictions and enhance the performance of quantitative trading strategies. Predictability is

concentrated on smaller stocks and more prominent on ﬁrms with bad news, consistent with

limits-to-arbitrage arguments rather than market ineﬃciencies.

∗

We are grateful for the comments and feedback from Andrew Chen, Carter Davis, Andy Naranjo,

Nikolai Roussanov, Ben Lee, Holger K. von Jouanne-Diedrich, and ‘@jugglingnumbers.’ Emails: Alejan-

dro Lopez-Lira (corresponding author): alejandro.lopez-lira@warrington.uﬂ.edu, and Yuehua Tang: yue-

hua.tang@warrington.uﬂ.edu.

Electronic copy available at: https://ssrn.com/abstract=4412788

The application of generative artiﬁcial intelligence and large language models (LLMs)

such as ChatGPT in various domains has gained signiﬁcant traction in recent months, with

numerous studies exploring their potential in diverse areas. In ﬁnancial economics, however,

using LLMs remains relatively uncharted territory, especially concerning their ability to pre-

dict stock market returns. On the one hand, as these models are not explicitly trained for

this purpose, one may expect that they oﬀer little value in predicting stock market move-

ments. On the other hand, to 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.

To the best of our knowledge, this paper is among the ﬁrst to address this critical 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. It is well known that stock returns are

predictable at a daily horizon using news and trained algorithms (Tetlock (2007), Tetlock,

Saar-Tsechansky, and Macskassy (2008), and Tetlock (2011) among others), possibly because

combining new information is complicated (Fedyk and Hodson (2023)). Hence, our focus is

to evaluate whether models not trained in predicting returns acquire this capability as they

become better at other tasks.

Our ﬁndings have important implications for the employment landscape in the ﬁnancial

industry. The results could potentially lead to a shift in the methods used for market predic-

tion and investment decision-making. By demonstrating the value of ChatGPT in ﬁnancial

economics, we aim to contribute to the understanding of LLMs’ applications in this ﬁeld and

inspire further research on integrating artiﬁcial intelligence and natural language processing

in ﬁnancial markets. In addition to the implications for employment in the ﬁnancial industry,

our study oﬀers several other signiﬁcant contributions.

Electronic copy available at: https://ssrn.com/abstract=4412788

Firstly, our research can aid regulators and policymakers in understanding the potential

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

the development of best practices for integrating LLMs into market operations.

Secondly, our study can beneﬁt asset managers and institutional investors by providing

empirical evidence on the eﬃcacy of LLMs in predicting stock market returns. This insight

can help these professionals make more informed decisions about incorporating LLMs into

their investment strategies, potentially leading to improved performance and reduced reliance

on traditional, more labor-intensive analysis methods.

Lastly, our research contributes to the broader academic discourse on artiﬁcial intelligence

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.

Our study has far-reaching implications that extend beyond the immediate context of

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.

Related Literature

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

1. See for example Wu et al. (2023).

Electronic copy available at: https://ssrn.com/abstract=4412788

by the Fed to communicate on monetary policy decisions). Cowen and Tabarrok (2023) and

Korinek (2023) demonstrate that ChatGPT is helpful in teaching economics and conducting

economic research. Noy and Zhang (2023) ﬁnd that ChatGPT can enhance productivity in

professional writing jobs. Contemporaneously, Xie et al. (2023) ﬁnd ChatGPT is no better

than simple methods such as linear regression when using numerical data in prediction tasks.

We attribute the diﬀerence in results to their focus on using historical numerical data to

predict, while ChatGPT excels at textual tasks. Ko and Lee (2023) ﬁnds ChatGPT may be

helpful in selecting across asset classes. Furthermore, Yang and Menczer (2023) demonstrates

that ChatGPT successfully identiﬁes credible news outlets. Our study is among the ﬁrst to

study the potential of LLMs in ﬁnancial markets, particularly the investment decision-making

process.

We contribute to the recent strand of the literature that employs text analysis and ma-

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.

Our paper also adds the literature that uses linguistic analyses of news articles to extract

sentiment and predict stock returns. One strand of this literature studies media sentiment

and aggregate stock returns (e.g., Tetlock (2007), Garcia (2013), Calomiris and Mamaysky

(2019)). Another strand of the literature uses the sentiment of ﬁrm news to predict future

individual stock returns (e.g., Tetlock, Saar-Tsechansky, and Macskassy (2008), Tetlock

(2011), Jiang, Li, and Wang (2021)). Diﬀerent from prior studies, we focus on understanding

whether LLMs add value by extracting additional information that predicts stock market

Electronic copy available at: https://ssrn.com/abstract=4412788

reactions.

Finally, our paper also relates to the literature on employment exposures and vulnerability

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

technology as well as the consequences for employment and productivity. With AI being

on a constant rise since its inception, our study focuses on understanding an urgent but

unanswered question – the capabilities of AI, and LLMs in particular, in the ﬁnance domain.

We highlight the potential of LLMs in adding value to market participants in processing

information to predict stock returns.

1 Background

ChatGPT is a large-scale language model developed by OpenAI based on the GPT (Genera-

tive Pre-trained Transformer) architecture. It is one of the most advanced natural language

processing (NLP) models developed to date and trained on a massive corpus of text data

to understand the structure and patterns of natural language. The Generative Pre-trained

Transformer (GPT) architecture is a deep learning algorithm for natural language processing

tasks. It was developed by OpenAI and is based on the Transformer architecture, which was

introduced in Vaswani et al. (2017). The GPT architecture has achieved state-of-the-art

performance in various natural language processing tasks, including language translation,

text summarization, question answering, and text completion.

The GPT architecture uses a multi-layer neural network to model the structure and

patterns of natural language. Using unsupervised learning methods, it is pre-trained on

a large corpus of text data, such as Wikipedia articles or web pages. This pre-training

process allows the model to develop a deep understanding of language syntax and semantics,

which is then ﬁne-tuned for speciﬁc language tasks. One of the unique features of the GPT

Electronic copy available at: https://ssrn.com/abstract=4412788

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