The ripple effect: How fake news shapes stock market behavior

International Review of Economics and Finance. 2026.

Fedorova E., Ledyaeva S., Stepanov V.

This study investigates the influence of low-quality information, specifically fake news, on short-

term stock returns, contributing to the discourse on signal theory and behavioral economics.

Employing a machine learning approach, the authors developed a robust fake news dataset from

publicly available sources. Sentiment analysis was conducted using a FinBERT model fine-tuned

for stock market contexts, alongside extensive dataset cleaning through the NER-BERT model and

conventional methods. Fixed-effects regression analysis was applied to assess the relationship

between news volume and stock returns. The findings reveal that negative fake news significantly

impacts stock returns, with its effects varying across firms and economic conditions. Notably, fake

news has a stronger influence on relatively illiquid stocks and becomes more pronounced during

periods of market uncertainty. These results underscore the importance of addressing misinformation

in financial markets. This study offers originality in its approach by introducing a novel

definition of fake news, addressing the lack of consensus in economic literature. It also identifies

new categories of fake news previously unexplored in the field and provides the first large-scale

econometric analysis of fake news effects on stock market performance. The findings hold critical

implications for investors, regulators, and corporate management, emphasizing the necessity of

countering misinformation to safeguard market stability and asset valuation integrity.

Keywords: текстовый анализ, behavioral economics, fake news, фейковые новости, applications of sentiment analysis