Leveraging Parallel Computing for Enhanced Stock Movement Forecasting Using Machine Learning

The advancements in computational techniques regarding parallel computing and machine learning are revolutionizing stock market prediction, This study explores the effectiveness of parallel computing architectures in predicting stock market movements. Existing literature reveals a huge shift towards employing machine learning models, especially in handling larger financial datasets, yet there remains a gap in understanding the full potential of parallel computing in this domain. Our research aims to bridge this gap by developing a comparative analysis between two Random Forest models: one utilizing parallel processing and the other based on sequential computation. Employing a comprehensive dataset that includes financial data from 2018 with 225 indicators of the US stock market, the data has been pre-processed to ensure its suitability for analysis. The methodology involves constructing and training both models, with the parallel model utilizing the multi-core capability of an Apple M1 chip and evaluating them based on accuracy and training time. The findings reveal that while both models achieve an impressive 100% accuracy, the parallel processing model significantly reduces training time, demonstrating the efficiency of parallel computing in rapid data processing. This research not only highlights the potential of parallel computing in enhancing the speed and accuracy of financial market predictions but also contributes to the broader field of financial analytics by suggesting new avenues for future research, including the application of deep learning models and the integration of a wider range of financial indicators.