Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks
It addresses the problem of predicting biotech stock prices for investors by incorporating social media sentiment, but it is incremental as it applies existing methods to new data.
This study analyzed the relationship between tweet sentiment across categories like news and CEO opinions and stock market performance for ten major biotech companies, using VADER sentiment analysis and ARIMA/VAR models to forecast stock prices with sentiment covariates, finding a complex interplay that improves predictions.
This study investigates the relationship between tweet sentiment across diverse categories: news, company opinions, CEO opinions, competitor opinions, and stock market behavior in the biotechnology sector, with a focus on understanding the impact of social media discourse on investor sentiment and decision-making processes. We analyzed historical stock market data for ten of the largest and most influential pharmaceutical companies alongside Twitter data related to COVID-19, vaccines, the companies, and their respective CEOs. Using VADER sentiment analysis, we examined the sentiment scores of tweets and assessed their relationships with stock market performance. We employed ARIMA (AutoRegressive Integrated Moving Average) and VAR (Vector AutoRegression) models to forecast stock market performance, incorporating sentiment covariates to improve predictions. Our findings revealed a complex interplay between tweet sentiment, news, biotech companies, their CEOs, and stock market performance, emphasizing the importance of considering diverse factors when modeling and predicting stock prices. This study provides valuable insights into the influence of social media on the financial sector and lays a foundation for future research aimed at refining stock price prediction models.