Stock Market Analysis using Long Short-Term Model - Gupta, Pulkit; Malik, Suhani; Apoorb, Kumar; Sameer, Syed Mahammed; Vardhan, Vivek; Ragam, Prashanth (EAI Endorsed Transactions on Scalable Information Systems, 2023)
In today’s world of value and improved investments, financial analysis has become a difficult task. The implementation of recurrent neural networks (RNN) and long short-term memory (LSTM) cells for stock market forecasting using time series of historical portfolio stock data is demonstrated in this study. In this study, we applied LSTM to predict stock market values using Yahoo Finance data along with Python modules Pandas and Matplotlib to evaluate the performance of the model. Our results show that the LSTM model is able to make accurate predictions of stock market prices and trends using historical data. The results of the correlation study showed a significant relationship between the daily return and the closing price of four randomly chosen companies. Overall, using LSTM, Yahoo Finance, Python Pandas, and Matplotlib modules to predict stock prices and provide useful information to investors was a successful strategy.