Revolutionizing Stock Market Prediction: Harnessing the Power of LSTM and CNN Hybrid Approach

Researchers have been investigating various methods to effectively predict stock market prices. These predictive systems are crucial as they provide traders with valuable insights into future trends and give investors essential information about potential market conditions. One promising technique is the application of machine learning algorithms for forecasting. The objective of this research is to improve the accuracy of stock market predictions by leveraging stock value data. Traditional methods, like artificial neural networks, have been used to detect hidden patterns and classify data for predicting stock market movements. However, this project introduces an alternative method that focuses on identifying the underlying dynamics within the data through advanced machine learning architectures. Specifically, the research employs Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and a hybrid approach that combines both LSTM and CNN for forecasting the prices of companies listed on the NSE. The study evaluates the long-term performance of these models using a sliding window approach. This method involves training the models on a rolling basis, which helps in capturing the evolving market trends over time. The effectiveness of these forecasting techniques is measured using the root mean square error (RMSE) metric, which provides a quantitative assessment of their predictive accuracy. By comparing these machine learning architectures, the research aims to determine the most effective approach for stock price prediction.