Can Internet Search Queries Help to Predict Stock Market Volatility?

被引:235
|
作者
Dimpfl, Thomas [1 ]
Jank, Stephan [2 ,3 ]
机构
[1] Univ Tubingen, Tubingen, Germany
[2] Frankfurt Sch Finance & Management, Cologne, Germany
[3] Ctr Financial Res CFR, Cologne, Germany
关键词
realised volatility; forecasting; investor behaviour; limited attention; noise trader; search engine data; ECONOMIC VALUE; IMPACT; MODEL; INVESTORS; ACCURACY; TESTS; RISK;
D O I
10.1111/eufm.12058
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页码:171 / 192
页数:22
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