Social mood and M&A performance: An empirical investigation enhanced by multimodal analytics

Abstract

This study, grounded in the emotions as social information theory, investigates the relationship between social mood, extracted from multimodal social media posts, and the mergers and acquisitions (M&A) performance of acquirer firms. Integrating a deep learning-based multimodal analytics methodology into econometric models, our empirical analysis reveals that the social moods of surprise and fear, extracted from texts and images on Twitter, negatively influence the M&A performance of acquirer firms. Our study also reveals the incremental effect of these moods captured in images in addition to those captured in text. The study further demonstrates that information uncertainty regarding acquirers amplifies the effect of fear mood on M&A performance. We also discover that the effects of surprise and fear are more significant for smaller acquirers. These findings have important theoretical and practical implications.