Publication Type
Journal Article
Version
acceptedVersion
Publication Date
5-2026
Abstract
The advent of generative artificial intelligence (AI) has heightened the proliferation of fake news. A key challenge is the limited real-world data to investigate the societal impact of fake news produced by generative AI. In this paper, we examine stock market reactions to financial news articles that exhibit stylometric similarity to human-crafted and AI-crafted fake financial news. Grounded in language expectancy theory, we employ a style-based transfer learning model, pre-trained to recognizing deceptive language employed in various types of fake news intricacies. We then apply this model to a comprehensive dataset of financial news, assigning a “veracity style score” to each article. This score quantifies the extent to which an article’s language and stylistic features align with patterns typically observed in human-crafted or AI-crafted fake news, indicating the risk of deception. By analyzing these scores of financial news articles on S&P 500 stocks, we find differential market reactions. Deceptive language in news articles stylometrically similar to human-crafted fake news is negatively associated with abnormal trading volume and absolute abnormal returns, whereas deceptive language in news articles stylometrically similar to AI-crafted fake news is positively associated with both metrics, highlighting the risk of market inefficiency in identifying AI-crafted deceptions.
Keywords
fake news, generative AI, large language model, trading volume, stock volatility
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Information Systems and Management
Areas of Excellence
Digital transformation
Publication
ACM Transactions on Management Information Systems
First Page
1
Last Page
29
ISSN
2158-656X
Identifier
doi/10.1145/3816728
Publisher
Association for Computing Machinery (ACM)
Citation
NG, Ka Chung; KE, Ping Fan; Ping Fan; SO, Mike; TAM; and Kar Yan.
Market reactions to deceptive language in fake news: implications from language expectancy theory and transfer learning. (2026). ACM Transactions on Management Information Systems. 1-29.
Available at: https://ink.library.smu.edu.sg/sis_research/11115
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://dl.acm.org/doi/10.1145/3816728