Predictive Analytics for Stock Market Price Using LSTM Model

The stock market is a dynamic and complex financial system that poses significant challenges to researchers and investors due to its inherent randomness. Predicting stock prices is particularly difficult because of the numerous uncorrelated and volatile features involved, which often exceed the capabilities of traditional statistical methods. This paper presents a state-of-the-art machine learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict stock prices. Leveraging historical stock data—including opening and closing prices, high and low values, and trading volumes—the proposed model captures temporal dependencies essential for time series forecasting. The study employs a comprehensive dataset spanning nearly a decade, coupled with systematic preprocessing to ensure data accuracy. The LSTM model is implemented using Python libraries for the stock prices of three large-cap companies listed on the NSE of India. Its predictive performance is validated through metrics such as Root Mean Squared Error (RMSE). This study not only demonstrates the efficacy of deep learning for stock price forecasting but also paves the way for incorporating additional factors, such as sentiment analysis and macroeconomic indicators, to enhance real-time applications. By leveraging artificial intelligence (AI), the model offers data-driven insights to support informed trading decisions.