Year: 2025
Type: article
Abstract: Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features derived from technical indicators. Empirical analysis is conducted on data from Apple, Tesla, and NVIDIA, employing nine classification algorithms, including support vector machines, random forests, extreme gradient boosting, and logistic regression. Results indicate that momentum-based indicators are the most influential predictors. While support vector machines achieve the highest accuracy for Apple, extreme gradient boosting performed best for NVIDIA and Tesla. In addition, explainable AI techniques are applied to interpret individual model predictions, thereby enhancing transparency and trust in the results. The study contributes to financial analytics research by providing a comparative evaluation of diverse machine learning methods and highlighting key indicators critical for short-term stock price forecasting.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc9100248
Authors: Bharatendra Rai, Leili Soltanisehat
Institution: University of Massachusetts Dartmouth
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4414613871