Market Reactions to Deceptive Language in Fake News: Implications from Language Expectancy Theory and Transfer Learning

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.