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...

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...

I study whether evolution in the number of Google Internet searches for particular keywords can predict volatility in the market for foreign currency. I find that data on Google searches for the keywords "economic crisis and financial crisis" and "recession" has incremental predictive power...

This paper studies the dynamics of stock market volatility and retail investors' attention to the stock market, where attention to the stock market is measured by internet search queries related to the leading stock market index. We find a strong co-movement of the Dow Jones' realized volatility...

This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms...

Accurately forecasting volatility is key in many financial applications. In this study, I suggest that individuals gather information online before implementing their trading decisions. In periods of higher investor concern, online information seeking intensifies. By analysing Google search data...