  The internet has changed the way we live, work and take decisions. As it is
the major modern resource for research, detailed data on internet usage
exhibits vast amounts of behavioral information. This paper aims to answer the
question whether this information can be facilitated to predict future returns
of stocks on financial capital markets. In an empirical analysis it implements
gradient boosted decision trees to learn relationships between abnormal returns
of stocks within the S&P 100 index and lagged predictors derived from
historical financial data, as well as search term query volumes on the internet
search engine Google. Models predict the occurrence of day-ahead stock returns
in excess of the index median. On a time frame from 2005 to 2017, all disparate
datasets exhibit valuable information. Evaluated models have average areas
under the receiver operating characteristic between 54.2% and 56.7%, clearly
indicating a classification better than random guessing. Implementing a simple
statistical arbitrage strategy, models are used to create daily trading
portfolios of ten stocks and result in annual performances of more than 57%
before transaction costs. With ensembles of different data sets topping up the
performance ranking, the results further question the weak form and semi-strong
form efficiency of modern financial capital markets. Even though transaction
costs are not included, the approach adds to the existing literature. It gives
guidance on how to use and transform data on internet usage behavior for
financial and economic modeling and forecasting.
