Can Internet Search Queries Help to Predict Stock Market Volatility?

22 Pages Posted: 2 Mar 2016

See all articles by Thomas Dimpfl

Thomas Dimpfl

University of Tuebingen - Department of Statistics and Econometrics

Stephan Jank

Deutsche Bundesbank; University of Cologne - Centre for Financial Research (CFR)

Multiple version iconThere are 2 versions of this paper

Date Written: March 2016

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

Suggested Citation

Dimpfl, Thomas and Jank, Stephan, Can Internet Search Queries Help to Predict Stock Market Volatility? (March 2016). European Financial Management, Vol. 22, Issue 2, pp. 171-192, 2016. Available at SSRN: https://ssrn.com/abstract=2740779 or http://dx.doi.org/10.1111/eufm.12058

Thomas Dimpfl (Contact Author)

University of Tuebingen - Department of Statistics and Econometrics ( email )

Germany

Stephan Jank

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

University of Cologne - Centre for Financial Research (CFR) ( email )

Albertus-Magnus Platz
Cologne, 50923
Germany

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