Employing asset-pricing models over the period 2012 to 2017, this study examines whether a search attention index (SAI) explains the variation in the weekly excess return of stocks. The study finds that the estimated abnormal return of a portfolio based on search intensity is significantly high...

In recent years, exchange-traded fund (ETF) markets have grown exponentially due to their rising popularity amongst retail investors with a preference for passive investments. However, the effect of this rising popularity on the performance of ETF markets remains understudied. Therefore, the...

We examine the effects of limited investor attention on stock returns by using Google search volume index to measure investor attention. We also investigate whether national culture and market development have any role in this relationship. We find that the impact of investor attention on stock...

This study aims at comparing Google Search Volume Indices (GSVIs—including market crash and bear market) and VIX (Investor Fear Gauge Index) in terms of explaining the S&P 500 returns. The VIX is found a more robust predictor of stock market returns than Google indices, and it does granger...

This research observes a time varying relationship between stock returns, volatilities and the online search volume in regard to selected CESEE (Central, Eastern and South-Eastern European) stock markets. The main hypothesis of the research assumes that a feedback relationship exists between...

We construct a new measure that captures the disparity between the market reaction to earnings information and the earnings surprise ("Return-Earnings Gap", "REG"). High REG scores positively predict analyst forecast errors and firm mispricing (overvaluation) scores, especially for build-up...

This paper studies model-free trade-revealed subjective expectations of returns using complex options trades from 2010 to 2021. Contrary to the extrapolative survey-based expectation of returns, trade-based expectations of returns positively predict future returns and are negatively correlated...

This study shows how correlated information consumption (CIC) of retail investors relates to comovement in stock market outcomes. We construct clusters of stocks with CIC by employing network analysis on Google co-search data. We predict significant comovement in returns and liquidity of stocks...