Research on Predicting Amazon Stock Price Based on Linear Regression and Decision Tree Algorithms

Gengyuan He

doi:10.54691/bcpbm.v32i.2958

Authors

Gengyuan He

DOI:

https://doi.org/10.54691/bcpbm.v32i.2958

Keywords:

Amazon Stock Prediction, Linear Regression, Decision Tree.

Abstract

Stock price prediction is really popular in finance. A good stock price prediction can benefit investors. In this study, the stock price of Amazon was predicted using decision tree and linear regression. Besides this study compared the accuracy and prediction results of two algorithms. The prediction models use the previous stock price and volume as the predictor. This study finds that the Decision Tree algorithm fit a better model than the Linear Regression algorithm. It indicates that the Decision Tree algorithm fits a better model than the Linear Regression algorithm. Moreover, this study forecasts the Amazon's stock price for the next 60 days based on the two models. This research helps investors choose whether and when to buy Amazon stock.

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