Can Internet Search Queries Help to Predict Stock Market Volatility?

Abstract

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 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. We utilize this finding to improve several models of realized volatility. Including search queries in autoregressive models of realized volatility helps to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful in high-volatility phases when a precise prediction is vital.

Keywords: realized volatility, forecasting, investor behavior, limited attention, noise trader, search engine data

JEL Classification: G10, G14, G17