Using Decision Tree Regression and Related Companies’ Stock Data to Predict Microsoft Stock Returns

Abstract

Using AI machine learning (ML) models to predict stocks is a topic that has already been studied by the stock market research community. However, two ML methods have not been analyzed in the current literature regarding stock prediction: the decision tree regression analysis and the related company training data approach. Thus, this study will utilize both of these unfamiliar stock prediction methods to predict Microsoft’s stock returns. To begin with, the company data of Microsoft's biggest partners and competitors were imported from YahooFinance; this data was then used to form all the features for the stock prediction model (mean, standard deviation, price gaps, etc.). Next, the machine learning model was created using Python's Decision Tree Regressor method; the model was trained using data before 10/1/2001 and tested using data after 10/1/2001. Through repeatedly testing this model, hyperparameter tuning was performed to determine the model's best features and max depth for predicting Microsoft’s stock returns. In the end, the final prediction model reached a percentage accuracy (percentage of times correctly predicting stock return's direction) of 56.68%, and the plot (net returns using model vs. historical net returns) showed that model use resulted in more consistent and significantly higher net Microsoft stock returns. Therefore, this study demonstrated that both the Decision Tree Regressor and the related company training data approach are successful machine learning methods in predicting Microsoft's stock returns. However, further research is required to extend this study's results to other companies and/or different stock metrics.