Volatility is an important metric of financial performance, indicating uncertainty or risk. So, predicting and managing volatility is of interest to both company managers and investors. This study investigates whether volatility in user-generated content (UGC) can spill over to volatility in stock returns and vice versa. Sources for user-generated content include tweets, blog posts, and Google searches. The authors test the presence of these spillover effects by a multivariate GARCH model. Further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content. Results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. Further, specific marketing events drive the volatility in user-generated content. In particular, new product launches significantly increase the volatility in the growth rates of usergenerated content, which in turn can spill over to volatility in stock returns. Moreover, the spillover effects differ in sign depending on the valence of the user-generated content in Twitter. The authors discuss the managerial implications.

Additional Metadata
Keywords User-generated content, Stock market performance, Volatility, Multivariate GARCH model, Spillover effects, Natural language processing
Persistent URL hdl.handle.net/1765/129422
Journal Industrial Marketing Management
Citation
Deijen, C.L., Borah, A., Tellis, G.J, & Franses, Ph.H.B.F. (2020). Big Data Analysis of Volatility Spilovers of Brands across Social Media and Stock Markets. Industrial Marketing Management, 88, 465–484. Retrieved from http://hdl.handle.net/1765/129422