Stock market prediction without sentiment analysis: Using a web-traffic based classifier and user-level analysis

This paper provides further evidence on the predictive power of online community traffic with regard to stock prices. Using the largest dataset to date, spanning 8 years and almost the complete set of SP500 stocks, we train a classifier using a set of features entirely extracted from web-traffic data of financial online communities. The classifier is shown to outperform the predictive power of a baseline classifier solely based on price time-series, and to have similar performances as the classifier built considering price and traffic features together. The best predictive performances are achieved when information about stock capitalization is coupled with long-term and midterm web traffic levels. In the second part of the paper we show how there exists a group of users whose traffic patterns constantly outperform the other users in predictive capacity. The findings set interesting future works in the definition of novel market indicators for market analysis.