The prediction of Apple stock price based on linear regression model and random forest model

Abstract

In the financial market, due to various factors, the stock price fluctuation is universal. Therefore, the directional prediction of stock market price based on technical analysis is very important in stock investment. This paper conducted a regression analysis and forecasted the future trends in the close price of Apple stock through recent five years between 2018 and 2023. For the purpose of this specific study, this paper did descriptive statistical analysis of the dataset, and made graphs and analyses of regression and predictions relied on the techniques of the Linear Regression Model and Random Forest Model. Based on the three indices: MSE, RMSE, and MAE, the paper compared the advantages and disadvantages of the two machine learning methods. The result of the experiments indicated that the regression generated through employment of the Linear Regression Model outperforms the result of the Random Forest Model, leading to the conclusion that Linear Regression Model is a more effective method to forecast in this dataset.