Predicting Stock Market Returns Using Sentiment in Business News Articles: An LSTM Machine Learning Approach

Abstract

This study aims to predict stock returns by leveraging sentiment derived from daily financial news articles published in leading newspapers. The dataset combines firm-level financial indicators with a sentiment index constructed from online news coverage, covering daily data of 98,750 observations from September 2010 to December 2020. Employing the Long Short-Term Memory (LSTM) model, the findings demonstrate a significant relationship between news sentiment and stock returns in the manufacturing sector. Notably, news sentiment emerges as the second most influential predictor after trading volume, surpassing several traditional financial variables. These results offer practical implications for investors seeking to improve decision-making and for policymakers aiming to monitor market volatility. The integration of textual data into predictive models provides a valuable resource for anticipating economic risks and enhancing regulatory responses.