Can internet search queries help to predict stock market volatility?

Thomas Dimpfl and Stephan Jank

No 11-15, CFR Working Papers from University of Cologne, Centre for Financial Research (CFR)

Abstract: This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high searches follow high volatility, and high volatility follows high searches. Using the latter feedback effect to predict volatility we find that search queries contain additional information about market volatility. They help to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful to predict volatility in high-volatility phases.

Keywords: realized volatility; forecasting; investor behavior; noise trader; search engine data (search for similar items in EconPapers)
JEL-codes: G10 G14 G17 (search for similar items in EconPapers)
Date: 2011
New Economics Papers: this item is included in nep-fmk
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Citations: View citations in EconPapers (79)

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https://www.econstor.eu/bitstream/10419/52242/1/671988344.pdf (application/pdf)

Related works:
Journal Article: Can Internet Search Queries Help to Predict Stock Market Volatility? (2016)
Working Paper: Can Internet search queries help to predict stock market volatility? (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfrwps:1115

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