Deep learning–driven automated prediction and data analysis of the Indian stock index

Abstract

This study focuses on the Indian stock index (NIFTY50), employing advanced deep learning methodologies to enhance predictive accuracy and support intelligent trading decisions. The research investigates the relationship between the volatility index (VIX) and NIFTY50, analyzing how fluctuations in VIX influence market direction and futures performance. By monitoring real-time variations in VIX during trading hours, the study aims to capture intraday volatility patterns and provide actionable insights for investors. To achieve this, the proposed framework leverages long short term memory (LSTM) networks, a sophisticated deep learning technique well-suited for sequential data analysis and financial forecasting. The methodology encompasses data collection, preprocessing, feature engineering, and the design of an LSTM-based predictive model, followed by rigorous evaluation and back-testing. Experimental results demonstrate the effectiveness of the approach in forecasting NIFTY50 movements, highlighting the potential of LSTM networks to outperform traditional statistical and machine learning models. The findings contribute to the growing body of research on AI-driven financial prediction, offering a paradigm shift in the application of deep learning for stock market analysis and paving the way for more reliable, automated decision-making in investment strategies.