Can Internet Search Queries Help to Predict Stock Market Volatility?

• Date: 01 March 2016
• Author: Thomas Dimpfl,Stephan Jank
• Published date: 01 March 2016
• DOI: http://doi.org/10.1111/eufm.12058

Can Internet Search Queries Help to

Predict Stock Market Volatility?

Thomas Dimpfl

University of Tübingen, Germany

E-mail: thomas.dimpfl@uni-tuebingen.de

Stephan Jank

Frankfurt School of Finance & Management and Centre for Financial Research (CFR), Cologne,

Germany

E-mail: s.jank@fs.de

Abstract

We study the dynamics of stock market volatility and retail investors’attention to the

stock market. The latter is measured by internet search queries related to the

leading stock market index. We find a strong co-movement of the Dow Jones’

realised volatility and the volume of search queries for its name. Furthermore,

search queries Granger-cause volatility: a heightened number of searches today is

followed by an increase in volatility tomorrow. Including search queries in

autoregressive models of realised volatility improves volatility forecasts in-sample,

out-of-sample, for different forecasting horizons, and in particular in high-volatility

phases.

Keywords: realised volatility, forecasting, investor behaviour, limited attention,

noise trader, search engine data

JEL classification: G10, G14, G17

1. Introduction

Large stock market movements capture investors’attention. This fact can be seen in

Figure 1 which depicts a strong co-movement between the volatility of the Dow Jones

stock market index and internet search queries for its name. If volatility is low, searches

related to the Dow Jones are generally at their overall average level, but search queries

We thank an anonymous referee, John A. Doukas (the editor), Jeremy Ginsberg, Joachim

Grammig, Isaac Hacamo, Heiko Jacobs, Kajal Lahiri, Terrance Odean, Franziska Peter,

Tomas Reyes, Maik Schmeling, Hal Varian, and Qingwei Wang for helpful comments and

suggestions. We retain responsibility for all remaining errors. Financial support of the

German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) is gratefully

acknowledged.

European Financial Management, Vol. 22, No. 2, 2016, 171–192

doi: 10.1111/eufm.12058

© 2015 John Wiley & Sons Ltd

surge to levels exceeding their average by multiples during times of market turbulence.

For example, when the volatility of the Dow Jones spiked to an almost record high of over

150% annualised on 10 October 2008, the number of previously submitted searches for

the index had increased to more than eleven times the average.

Internet search queries related to stock market index names can be interpreted as a

measure of retail investors’attention to the stock market (cf. Da et al., 2011). While

professional investors monitor the leading index all the time, retail investors presumably

do not. Once the latter perceive an increased demand for information about the stock

index, they are likely to use the internet via a search engine like Google as a source of

information. In contrast, professional investors most probably, if not certainly, are not

using a search engine to obtain information about the leading stock market index. Thus,

search queries qualify as a good proxy for retail investors’attention to the stock market.

After having searched for the stock market index, some individuals might be inclined to

act and trade immediately or the following day.

Retail investors are often considered to be uninformed noise traders. This notion is

supported by empirical evidence showing that retail investors lose money with their

trading decisions (e.g. Odean, 1998; Grinblatt and Keloharju, 2000). Several behavioural

finance models (e.g. Black, 1986; De Long et al., 1990) predict that trading of uninformed

noise traders can temporarily lead to mis-pricing and increase volatility. Similarly, in the

agent-based models of stock market volatility (e.g. Lux and Marchesi, 1999; Alfarano and

0.02 .04 .06 .08 .1

Realised volatility

2007 2008 2009 2010 2011 2012

Realised volatility of the Dow Jones

0 5 10

Search Volume Index

2007 2008 2009 2010 2011 2012

Search queries for the index name

Fig. 1. Realised volatility and search activity.

This figure presents daily realised volatility of the Dow Jones Industrial Average index (upper

graph) and the volume of Google search queries (lower graph) for its name from 1 July 2006 to 31

December 2011. Search queries are standardised such that the sample average equals one.

© 2015 John Wiley & Sons Ltd

172 Thomas Dimpfland Stephan Jank