A Study on Machine Learning Applications in Stock Prediction with Reference to BSE and NSE
TL;DR
Investors generally recognize the usefulness of machine learning in improving prediction accuracy, supporting portfolio management, reducing emotional investment decisions, and enhancing financial analysis, and the integration of advanced machine learning algorithms with traditional financial analysis can substantially improve investment performance and reduce forecasting uncertainty.
Abstract
Machine learning has emerged as one of the most powerful technologies for improving stock market prediction and supporting intelligent investment decision-making. This study examined the application of machine learning techniques in stock prediction with reference to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). The findings indicate that investors generally recognize the usefulness of machine learning in improving prediction accuracy, supporting portfolio management, reducing emotional investment decisions, and enhancing financial analysis. Statistical analysis further demonstrates that investor awareness, trust in AI-based systems, and prediction accuracy significantly influence the adoption of machine learning technologies. Although challenges such as market volatility, data quality, and computational complexity remain, the integration of advanced machine learning algorithms with traditional financial analysis can substantially improve investment performance and reduce forecasting uncertainty. Continuous technological innovation, investor education, quality financial data, and responsible AI governance will play a crucial role in strengthening the future of AI-driven stock market forecasting in India.