The first paper investigates the predictive power of investors' sentiment and attention for the stock returns' volatility. We introduce a novel and extensive dataset that combines information from social media platforms, news articles, search engine data, and information consumption. Applying a...

Given the increasing interest in investor sentiment derived from social media platforms, we address one overlooked question - are there structural breaks in online investor sentiment? We cast the problem of break-point estimation in the dynamics of the sentiment series as a model selection...

We empirically investigate how retail and institutional investor attention is related to the way stock markets process information. With a focus on 360 US stocks in the S&P 500 universe, our results show that higher retail investors' attention around news releases increases the post-announcement...

We analyze the impact of sentiment and attention variables on volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. Applying a state-of-the-art sentiment classification technique, we investigate the question...

Given the increasing interest in and the growing number of publicly available methods to estimate investor sentiment from social media platforms, researchers and practitioners alike are facing one crucial question - which is best to gauge investor sentiment? We compare the performance of daily...

We develop a novel and direct measure of investor sentiment for sustainability using microblogging-data from a social media investing platform and present evidence that it predicts asset prices. Our sustainability sentiment index contains granular information on environmental, social, and...