Stock Market Forecasting Using LSTM Neural Network

Abstract

This study investigates the widespread use of machine learning techniques, including recurrent neural networks (RNNs) and long short-term memory (LSTM) models to the analysis of stock market data. By utilizing RNN and LSTM model capabilities to identify temporal relationships and patterns in stock market data, it seeks to overcome conventional techniques' constraints. The research provides empirical proof of the efficiency of RNNs and LSTM models in enhancing investment decision-making by analyzing the project outcomes using real-world stock market data. The inclusion of RNNs and LSTM models in this research paper strengthens the exploration of machine learning techniques in stock market analysis.