Reddit Sentiment Analysis and Its Effect on Stock Return: A Case Study of Lundbeck A/S Using Reddit-Derived Sentiment Data

Abstract

This thesis investigates the relationship between investor sentiment on Reddit and the daily stock returns of H. Lundbeck A/S, a pharmaceutical company operating in the emotionally sensitive domain of mental health treatment. Drawing from behavioral finance, the study explores whether user-generated discussions—especially those expressing trust, fear, or overall sentiment polarity—can meaningfully predict or explain stock market behavior. Reddit comments mentioning Lundbeck and its key products were collected and analyzed using a lexicon-based sentiment pipeline. Natural language processing techniques such as lemmatization and negation handling were applied to ensure semantic accuracy. Two sentiment lexicons were used: AFINN for scalar valence scoring, and NRC for emotional categories. Daily sentiment measures were aligned with Lundbeck’s stock return data and tested using Vector Autoregression (VAR) and Vector Error Correction Models (VECM). Results show that sentiment polarity—especially the NRC lexicon’s positive and negative labels—provides the most reliable predictive signals, outperforming trust and fear metrics in short-term forecasting. AFINN scores showed limited interaction with return dynamics. These findings suggest that generalized sentiment polarity captures financial relevance more effectively than specific emotional cues. This study contributes to the literature on behavioral finance and financial text analytics by demonstrating that sentiment derived from social media can exert a measurable influence on stock performance, even within sectors not traditionally associated with retail-driven speculation.