Information demand and stock market volatility

Google search volume and its influence on liquidity and returns of German stocks

“…The authors interpret this observation as support for attention-induced price pressure as proposed by Barber and Odean (2008). Vlastakis and Markellos (2010) also use data from Google Trends to approximate information demand and public interest at the firm and market level for 30 stocks traded on the NYSE. In addition to seasonal patterns in the information demand, they find that Internet search volume is positively related to trading volume and return volatility.…”

How does Google search affect trader positions and crude oil prices?

“…Their work was the first to illustrate the usefulness of the GSVI in financial applications. Thereafter, Vlastakis and Markellos (2012) employed the GSVI to measure information demand for the largest 30 stocks traded on the New York Stock Exchange, and used the Reuters NewsScope Archive as the information supply. Their research concluded that investors needed more information when their level of risk aversion increased.…”

“…(Alquist and Kilian, 2010;Benhmad, 2012;Carfí and Musolino, 2014;Ding et al, 2014;Wang and Chueh, 2013;Yu et al, 2008). Recently, investor attention has become a newly emerging concept that can be considered as an important factor in price fluctuations (Vlastakis and Markellos, 2012). However, it is hard to measure investor attention due to some of its intrinsic features, such as the subjectivity of public concern and difficulty in census data collection.…”

“…However, it is hard to measure investor attention due to some of its intrinsic features, such as the subjectivity of public concern and difficulty in census data collection. For the past few years, Internet-based data such as the Google Search Volume Index (GSVI) have been widely used to measure investor attention in financial markets, which is relatively objective compared with traditional measures of investor attention (Da et al, 2010;Drake et al, 2012;Vlastakis and Markellos, 2012). Studies have seldom analyzed the roles of the GSVI in influencing trader positions and energy prices.…”

A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data

“…Bank et al (2011) used the search data to study the liquidity and returns of German stocks. Vlastakis and Markellos (2012) used Google trends data to predict stock market volatility.…”