Price Prediction for Starbucks Corporation Based on Random Forest, Linear Regression, and Decision Tree

Abstract

The outbreak of the virus COVID-19 in 2019 has dealt an unprecedented blow to the global economy. Contemporarily, the global economy is in the recovery stage after the end of the epidemic, and the economic recession is likely to be the future trend in terms of current status. In this paper, three state-of-art multivariate supervised learning regression scenarios (i.e., Random Forest, Linear Regression, and Decision Tree) are implemented to predict the stock price of Starbucks. Through comparison, it is concluded that Linear Regression and Random Forest are the most accurate models that are expected to be used in the future. Moreover, investing in Starbucks stock is not a wise choice at this time. It is hoped to reduce investor losses by choosing the optimal model for the most accurate stock predictions and minimizing losses in a recession. These results shed light on guiding further exploration of targeted analysis of the new economic environment.