Predicting BMW Stock Price Based on Linear Regression, LSTM, and Random Forest Regression

Abstract

The stock market is one of the most significant aspects of a county’s economy. Stock price prediction is not easy to implement due numerous features influencing it. This study will choose BMW as the target company, and predict its stock price with various state-of-art scenarios. The central claim of this article is to construct three reliable models that could predict the stock price of BMW by extracting and analyzing previous days’ stock prices. The major analysis is done by modeling and exploring data analysis, and is composed of several graphical methods. For the modeling part, three models (i.e., Multiple Linear Regression, LSTM, and Random Forest Regression) are constructed to predict BMW stock price. The database consists of five years of BMW stock prices on Kaggle to explore more analysis. Then, step by step checking and demonstrating are presented to prove the feasibility and precision of these three models. According to the analysis, the most precise model that has the highest accuracy and lowest error by analyzing regression and model results. These model final outcomes indicate that the Multiple Linear Regression model presents a higher accuracy and lower mean squared error than LSTM and Random Forest Regression. These results shed light on guiding further exploration of BMW stock price prediction.