Extreme Dependence in Investor Attention and Stock Returns - Consequences for Forecasting Stock Returns and Measuring Systemic Risk
Quantitative Finance, Forthcoming
53 Pages Posted: 20 Jun 2019
Date Written: June 13, 2019
Abstract
We characterize co-movements in investor attention by modeling multivariate internet search volume data. Using a variety of copula models that can capture both asymmetric and skewed dependence, we find empirical evidence of strong non-linear and asymmetric dependence in the attention investors give to companies. Modeling three years of daily stock returns and search volumes from Google Trends for 29 bank names, we find a striking similarity between the dependence structure inherent in stock returns and the dependence in the corresponding time series of search queries. We then document the existence of significant asymmetric and skewed tail dependence in the joint distribution of stock returns and investor attention. Finally, stock returns and internet search volumes appear to evolve concurrently in real time with neither one leading the other. Our findings have important implications, e.g., for the analysis of banks' interconnectedness based on equity data and the pricing of investor attention in the cross-section of stock returns.
Keywords: dependence structures, tail dependence, investor attention, Google Trends
JEL Classification: C58, G02, C01
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