A Long Short-Term Memory (LSTM) Neural Architecture for Presaging Stock Prices

Tej Nileshkumar Doshi, Shubham Ghadge, Yamini Gonuguntla, Namirah Imtieaz Shaik, Ashutosh Mathore, Bonaventure Chidube Molokwu

2025

Abstract

Stock price prediction is crucial for informed investment decisions(Bathla, 2020; Hochreiter and Schmidhuber, 1997). This study explores the application of Long Short-Term Memory (LSTM) architecture for analyzing and predicting stock prices of major technology companies: Alphabet Inc. (GOOG), Apple Inc. (AAPL), NVIDIA Corporation (NVDA), Meta Platforms, Inc. (META), and Tesla Inc. (TSLA). The fundamental challenge addressed is capturing temporal dependencies and complex patterns in financial time series data, which traditional statistical methods often fail to model accurately(Box et al., 1978; Hyndman and Athanasopoulos, 2013). Our methodology involved collecting historical stock data from Yahoo Finance API(Edwards et al., 2018), preprocessing through normalization and sequence creation(Hochreiter and Schmidhuber, 1997), and training separate LSTM models for each stock. Results indicate that LSTM models provide satisfactory accuracy with R² scores exceeding 0.93 for most stocks(Li et al., 2023; Selvin et al., 2017), capturing both short-term and long-term patterns(Panchal et al., 2024; Ouf et al., 2024). The implications are significant for investors and financial analysts seeking enhanced predictive tools for market forecasting(Pramod and Pm, 2020).

Paper Citation

in Harvard Style

Doshi T., Ghadge S., Gonuguntla Y., Shaik N., Mathore A. and Molokwu B. (2025). A Long Short-Term Memory (LSTM) Neural Architecture for Presaging Stock Prices. In Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-772-6, SciTePress, pages 473-480. DOI: 10.5220/0013691900003985

in Bibtex Style

@conference{webist25,
author={Tej Doshi and Shubham Ghadge and Yamini Gonuguntla and Namirah Shaik and Ashutosh Mathore and Bonaventure Molokwu},
title={A Long Short-Term Memory (LSTM) Neural Architecture for Presaging Stock Prices},
booktitle={Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2025},
pages={473-480},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013691900003985},
isbn={978-989-758-772-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - A Long Short-Term Memory (LSTM) Neural Architecture for Presaging Stock Prices
SN - 978-989-758-772-6
AU - Doshi T.
AU - Ghadge S.
AU - Gonuguntla Y.
AU - Shaik N.
AU - Mathore A.
AU - Molokwu B.
PY - 2025
SP - 473
EP - 480
DO - 10.5220/0013691900003985
PB - SciTePress