Enhancing Stock Market Forecasting Using Machine Learning Models

Andrea Lim

Abstract: This study explores the integration of machine learning techniques in forecasting stock market trends. Focusing on three core models linear regression, neural networks, and decision trees it compares their predictive accuracy using historical data from leading technology firms. Additional enhancements such as sentiment analysis, time series extrapolation, and cross-company data incorporation are tested to evaluate their influence on prediction performance. Findings suggest that linear regression consistently outperforms other models in accuracy, with time series augmentation providing notable improvement. This work highlights the potential of hybrid AI-driven approaches to refine financial forecasting models.

Keywords: Machine Learning, Stock Forecasting, Linear Regression, Sentiment Analysis, Time Series Modeling

How to Cite?: Andrea Lim, "Enhancing Stock Market Forecasting Using Machine Learning Models", Volume 14 Issue 8, August 2025, International Journal of Science and Research (IJSR), Pages: 329-333, https://www.ijsr.net/getabstract.php?paperid=SR25730031803, DOI: https://dx.doi.org/10.21275/SR25730031803