Structural transmissions among investor attention, stock market volatility and trading volumes

• Published date: 01 January 2022
• Author: Helmut Herwartz,Fang Xu
• Date: 01 January 2022
• DOI: http://doi.org/10.1111/eufm.12315

Eur Financ Manag. 2022;28:260–279.260

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wileyonlinelibrary.com/journal/eufm

DOI: 10.1111/eufm.12315

ORIGINAL ARTICLE

Structural transmissions among investor

attention, stock market volatility and trading

volumes

Helmut Herwartz

1

|Fang Xu

2

1

Department of Economics, Georg‐

August‐University of Goettingen,

Goettingen, Germany

2

Department of Economics and Finance,

Brunel‐University of London,

Uxbridge, UK

Correspondence

Fang Xu, Department of Economics and

Finance, Brunel‐University of London,

UB8 3PH Uxbridge, UK.

Email: fang.xu@brunel.ac.uk

Abstract

We employ data‐based approaches to identify the

transmissions of structural shocks among investor at-

tention measured by Google search queries, realised

volatilities and trading volumes in the United States,

the United Kingdom and the German stock market.

The two identification approaches adopted for the

structural vector autoregressive analysis are based on

independent component analysis and the informa-

tional content of disproportional variance changes. Our

results show robust evidence that investors' attention

affects both volatilities and trading volumes con-

temporaneously, whereas the latter two variables lack

immediate impacts on investors' attention. Some

movements in investors' attention can be traced back to

market sentiment.

KEYWORDS

realised volatility, search engine data, structural VAR

JEL CLASSIFICATION

G10, G14

EUROPEAN

FINANCIAL MANAGEMENT

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and

reproduction in any medium, provided the original work is properly cited.

© 2021 The Authors. European Financial Management published by John Wiley & Sons Ltd.

We are grateful for the feedbacks from an anonymous referee. Financial support by the Deutsche For-

schungsgemeinschaft (HE2188/14‐1) is gratefully acknowledged.

1|INTRODUCTION

The Google search engine has become an integral tool to find information for more and more

people around the globe. The aggregate search frequency in Google provides a direct measure

of individual/retail investor's attention (Da et al., 2011). When an individual searches for DOW

in Google, she/he certainly pays attention to it. Empirical analysis has shown that stock return

volatilities and trading volumes are positively associated with Google search queries (Vlastakis

& Markellos, 2012). Moreover, changes in search queries today can help to explain future

changes in return volatility. Dimpfl and Jank (2016) show that search queries Granger‐cause

volatility and including search queries in models of realised volatility improves volatility

forecasts out‐of‐sample. It is, however, unclear what is the propagation mechanism of the

shocks. Does a volatility shock trigger search queries (investors' attention), or/and is it the

increased investor's attention (reflected in a positive shock in search queries) that triggers more

trading and thereby higher volatility?

On the one hand, there are several theoretical underpinnings for the impact of investors'

attention on volatility. If investors pay more attention, new information is quickly incorporated

into prices and, thus, can induce high return volatility (Andrei & Hasler, 2015). Moreover, as

retail investors are often regarded as uninformed noise traders, their trading can lead to ex-

cessive volatilities of asset prices according to the noise trader model (De Long et al., 1990).

Similarly, exogenous shocks of the fundamental prices can be interpreted by noise traders as a

potential future trend in agent‐based models (Lux & Marchesi, 1999). When there is a large

fraction of noise‐trader agents in the market, the volatility of the stock becomes larger. Thus,

the higher the volume of the search queries, the more likely it is that retail investors are actively

trading and the larger are the volatilities of the relevant stocks. On the other hand, volatile

movements in the stock markets have been frequently featured in the news, specially in

downturn periods. This could attract retail investors' attention and increase the count of search

queries for the stock indices. The recursive structural vector autoregressive (SVAR) model in

Dimpfl and Jank (2016), for example, builds upon the hierarchical assumption of an immediate

impact of volatility on search queries.

This paper contributes to the literature by estimating the contemporaneous relationship

between search queries and return volatilities. For such a purpose, ad‐hoc impositions of

triangular structures (e.g., in terms of lower triangular Cholesky factors) for SVAR models leave

no room for the data to object against the model implied hierarchy. In this paper, we use data‐

driven identification approaches, which let data determine the latent structural relationships.

Our analysis is based on daily Google search queries for US, UK and German stock market

indices from 2006 to 2011. Our data exhibit both deviations from a conditionally multivariate

Gaussian model and conditional changes in the covariance structure. Therefore, we exploit the

uniqueness of independent structural shocks (Matteson & Tsay, 2017) and the informational

content of disproportional variance changes of the model implied structural shocks (Bouakez &

Normadin, 2010; Lanne & Saikkonen, 2007; Normadin & Phaneuf, 2004) for SVAR

identification.

Results from both identification approaches and the three markets point to the same

evidence—shocks in Google search queries affect return volatilities immediately, whereas

shocks in volatilities exert an only mild (if any) instantaneous effect on search queries.

Therefore, what underlies the positive correlation observed among search queries and volatility

is the increased investor's attention which triggers more trading and, thus, higher volatility.

Introducing the trading volume as a third variable into the SVARs confirms that search queries

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