Template-Type: ReDIF-Paper 1.0
Author-Name: van Dieijen, M.J.
Author-Name-Last: van Dieijen
Author-Name-First: Myrthe
Author-Name: Borah, A.
Author-Name-Last: Borah
Author-Name-First: Abhishek
Author-Name: Tellis, G.J.
Author-Name-Last: Tellis
Author-Name-First: Gerard
Author-Name: Franses, Ph.H.B.F.
Author-Name-Last: Franses
Author-Name-First: Philip Hans
Author-Person: pfr226
Title: Big Data Analysis of Volatility Spillovers of Brands across Social Media and Stock Markets
Abstract: 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.
Length: 20
Creation-Date: 2019-01-15
Series: RePEc:ems:eureir
Number: EI-1691
Keywords: User-generated content, Stock market performance, Volatility, Multivariate GARCH model, Spillover effects, Natural language processing
Handle: RePEc:ems:eureir:129317