Can Internet Search Queries Help to Predict Stock Market Volatility?. (4th February 2015)

• Record Type: Journal Article
• Authors: Dimpfl, Thomas; Jank, Stephan
• Is Part Of: European financial management. Volume 22:Number 2(2016:Mar.)
• Page Start: 171
• Page End: 192
• Publication Date: 2015-02-04
• Subjects: realised volatility -- forecasting -- investor behaviour -- limited attention -- noise trader -- search engine data; Finance -- European Union countries -- Periodicals; International finance -- Periodicals; Corporations -- European Union countries -- Finance -- Periodicals; 332.094
• DOI: 10.1111/eufm.12058
• Languages: English
• ISSNs: 1354-7798

Abstract

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.