Editorial
Stock Market Analysis using Long Short-Term Model
@ARTICLE{10.4108/eetsis.4446, author={Pulkit Gupta and Suhani Malik and Kumar Apoorb and Syed Mahammed Sameer and Vivek Vardhan and Prashanth Ragam}, title={Stock Market Analysis using Long Short-Term Model}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={11}, number={2}, publisher={EAI}, journal_a={SIS}, year={2025}, month={4}, keywords={Long Short-Term Model, Yahoo Finance, Stock market return, average}, doi={10.4108/eetsis.4446} }- Pulkit Gupta
Suhani Malik
Kumar Apoorb
Syed Mahammed Sameer
Vivek Vardhan
Prashanth Ragam
Year: 2025
Stock Market Analysis using Long Short-Term Model
SIS
EAI
DOI: 10.4108/eetsis.4446
Abstract
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.
Copyright © 2023 P. Gupta et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
