Exploring Stock Price Prediction Using Machine Learning Combined With Supply Chain Analysis

Abstract

This paper hypothesizes that machine learning models can predict future stock price movements of a company with higher accuracy if we give them access to news sentiment scores of that company’s major suppliers and customers. The paper tests this idea on 38 companies across 10 different economic sectors. The paper finds that prediction accuracy improves by as much as 6.1% with this idea, and improvements are especially consistent among companies in sectors with high economic elasticity.