Stock Price Prediction Based on Simple Decision Tree Random Forest and XGBoost

Abstract

Stock price prediction is the process of using a variety of methods and techniques to forecast the future performance of the stock. It is helpful for investors and traders to make decisions before and during trading. In this study, three stock prediction models depending on the decision tree algorithm are developed (i.e., Decision Tree, Random Forest and XGBoost) To be specific, their performances are compared to select the best prediction approach. Both accuracy and mean squared error (MSE) are utilized as metrics to evaluate the performances of models. By comparing the results and the metrics calculated according to the predictions, the random forest model is the best model with the highest accuracy and the smallest MSE. However, the XGBoost was the least accurate since this algorithm is too sensitive to the scale of the input variables. These results offer suggestions for choosing stock price prediction models and shed light on improving the models.