Predicting Stock Market Prices using Deep Learning Models

Introduction 

Predicting stock market prices accurately remains a significant challenge due to the dynamic nature and complexity of financial markets. Numerous factors, including company-specific events, macroeconomic indicators, investor sentiment, and market trends, simultaneously impact stock prices, complicating predictive modeling. Recent advancements in machine learning and deep learning methodologies have provided promising frameworks for enhancing the accuracy of stock market forecasts. This project explores various statistical and deep learning approaches, including Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and an ensemble model combining LSTM and GRU architectures, to forecast stock prices using historical NIFTY 100 index data. The objective is to identify and develop robust predictive models capable of capturing complex temporal dependencies inherent in financial time-series data. Our research aims to test different modeling approaches systematically and identify the most effective models for stock price prediction, thereby providing valuable insights for informed investment decisions.

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