Bank Nifty: Study of Stock Price Predictions using Machine Learning and Sentiment Analysis

Abstract

Accurate prediction of stock prices has been a cornerstone of financial market research, especially for dynamic indices like Bank Nifty, which encapsulates the performance of India’s banking sector. This paper explores integrating machine learning techniques and sentiment analysis for forecasting stock prices. Leveraging advanced statistical metrics and data-driven models provides a comparative analysis of various prediction methodologies. The research study also incorporates recent trends, practical case studies, and future scopes in predictive analytics, highlighting the transformative role of artificial intelligence and big data. This comprehensive review underscores the potential of hybrid models in enhancing forecasting accuracy and reliability, offering invaluable insights for researchers, traders, and financial institutions.