Social Media, Traditional News and Stock Returns: A Causal Mediation Analysis

Increasing computing power and access to the internet have amplified the role of social media and online news media on financial market outcomes. However, these two sources of information are intertwined in such a way that information flows between them. As a result, sentiment expressed in one source can affect stock market outcomes through the other source. This study examines this interplay between news media sentiment, social media sentiment and stock returns within the Dow Jones constituent companies from 2016 to 2023. Leveraging an extensive dataset, we adopt an approach that combines causal mediation models with robust statistical techniques to establish the mediation effects of one sentiment proxy on the relationship between the other proxy and stock returns. We also use a range of other methods like path analysis, panel vector autoregression and causal forests for robustness. The study finds that news sentiment is more influential in directly affecting stock returns than Twitter sentiment while the latter is more influential indirectly when mediated by news sentiment.