Can Internet Search Queries Help to Predict Stock Market Volatility?

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Aufrufstatistik

URI: http://nbn-resolving.de/urn:nbn:de:bsz:21-opus-58552
http://hdl.handle.net/10900/47872
Dokumentart: ResearchPaper
Date: 2011
Source: University of Tübingen Working Papers in Economics and Finance ; 18
Language: English
Faculty: 6 Wirtschafts- und Sozialwissenschaftliche Fakultät
Department: Wirtschaftswissenschaften
DDC Classifikation: 330 - Economics
Keywords: Volatilität , Prognose
Other Keywords:
Realized volatility , Forecasting , Investor behavior , Noise trader , Search engine data
License: Publishing license excluding print on demand
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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.

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