RNN, GRU, and LSTM Model Analysis for AI-Powered Indian Stock Market Index Forecasting

The nonlinear and volatile nature of financial markets makes stock price prediction highly challenging. Traditional models, such as ARIMA, struggle to capture the complex relationships and unpredictable trends in stock market data. This research utilizes advanced deep learning methods, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), to predict stock prices based on historical data and key financial indicators. The YFinance library collects comprehensive financial data, while feature selection is enhanced through techniques such as hysteresis analysis, moving averages, and sentiment analysis using Natural Language Processing (NLP). Model performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared. Additionally, back-testing using historical data ensures the model's reliability. The results indicate that GRU outperforms other models in terms of computational efficiency and predictive accuracy. This study underscores the growing role of deep learning in financial analytics, providing investors with valuable insights and a structured, data-driven approach to decision-making in highly volatile market conditions.