Predicting Day-Ahead Stock Returns using Search Engine Query Volumes: An Application of Gradient Boosted Decision Trees to the S&P 100. (arXiv:2205.15853v1 [econ.EM])

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 …

application arxiv decision gradient query returns search search engine stock trees