Abstract
This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three volatility components, defined over different time horizons, using the similarity measure between lagged Google search queries and volatility. Results show that investor attention positively affects future volatility in the short-run. The effect of investor attention is likely to reverse in the long-run, consistently with the price pressure hypothesis. The proposed model demonstrates important gains in terms of volatility forecast accuracy and outperforms highly competitive models.
Highlights
It has been commonly assumed that investors behave rationally
Our results are consistent with previous findings, which show that stock market volatility is positively affected by investor attention in the short-term
Though it is hard to beat the Heterogeneous Autoregressive (HAR) model, we find that the t-statistics of the pair HAR-Empirical Similarity (ES) vs. HAR are significantly negative for eight indices (AEX, BEL 20, CAC 40, DAX, DJIA, IBEX 35, IPC and SMI); and insignificant for the other markets
Summary
It has been commonly assumed that investors behave rationally. A long strand of literature provides evidence that this assumption is unrealistic and that investors behavior is, prone to psychological biases. Examining investor attention can shed light on a variety of observations in stock markets. In an intriguing study, Hamid and Heiden (2015) note that the movements of the GVI for the term “dow” are well aligned with that of the DJIA volatility. Based on this observation, they extend the Empirical Similarity (ES) model, developed by Lieberman (2012), in order to examine the link between investor attention and the volatility of the DJIA index. The aim of present paper is to investigate the role of investor attention in forecasting volatility for 14 international stock markets, by means of GVI.
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