Analyzing the impact of user-generated content on B2B Firms' stock performance: Big data analysis with machine learning methods

Marketing scholars are interested in the big data of user-generated content (UGC) from social media platforms. However, the majority of current UGC studies have been conducted in the business-to-consumer (B2C) context. To fill the knowledge gap in business-to-business (B2B) research, we investigate whether UGC has differential impacts on stock performance for B2B and B2C firms by using big data. We collect a large dataset of 84 million tweets from 20.3 million Twitter accounts and 8 years of stock data for 407 companies from the S&P500 index. The results from machine learning methods are transformed into a monthly panel data. We conduct fixed effects model on the panel data. We find that UGC has a significant impact on firms' stock performance and that its impact on stock performance is much stronger among B2C firms than among B2B firms. While consumers' positive sentiment does not play a significant role in stock performance, consumers' negative sentiment and WOM significantly impact stock prices.