Predicting Day-Ahead Stock Returns using Search Engine Query Volumes: An Application of Gradient Boosted Decision Trees to the S&P 100
利用搜索量预测次日股票收益:梯度提升决策树在标普100中的应用
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…