Predicting Day-Ahead Stock Returns using Search Engine Query Volumes: An Application of Gradient Boosted Decision Trees to the S&P 100

TL;DR

This paper demonstrates that combining internet search query data with financial data using gradient boosted decision trees can predict day-ahead stock returns, outperforming random guessing and challenging market efficiency assumptions.

Contribution

It introduces a novel approach integrating search engine query volumes with financial data for stock return prediction using gradient boosting, showing promising empirical results.

Findings

Models achieve AUC between 54.2% and 56.7%.

Trading strategies based on predictions yield over 57% annual returns before costs.

Results challenge the weak and semi-strong market efficiency hypotheses.

Abstract

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.