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Benchmarking Hybrid ANN-LSTM and Physics-Informed Neural Networks for Forecasting Stock Market Prices

by Anika Tahsin Biva, A.B.M. Shahadat Hossain, Md. Shafiul Alom Khan, Iqbal Habib
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 74
Year of Publication: 2025
Authors: Anika Tahsin Biva, A.B.M. Shahadat Hossain, Md. Shafiul Alom Khan, Iqbal Habib
10.5120/ijca2025924624

Anika Tahsin Biva, A.B.M. Shahadat Hossain, Md. Shafiul Alom Khan, Iqbal Habib . Benchmarking Hybrid ANN-LSTM and Physics-Informed Neural Networks for Forecasting Stock Market Prices. International Journal of Computer Applications. 186, 74 ( Mar 2025), 1-8. DOI=10.5120/ijca2025924624

@article{ 10.5120/ijca2025924624,
author = { Anika Tahsin Biva, A.B.M. Shahadat Hossain, Md. Shafiul Alom Khan, Iqbal Habib },
title = { Benchmarking Hybrid ANN-LSTM and Physics-Informed Neural Networks for Forecasting Stock Market Prices },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 74 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number74/benchmarking-hybrid-ann-lstm-and-physics-informed-neural-networks-for-forecasting-stock-market-prices/ },
doi = { 10.5120/ijca2025924624 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-25T22:41:41.108653+05:30
%A Anika Tahsin Biva
%A A.B.M. Shahadat Hossain
%A Md. Shafiul Alom Khan
%A Iqbal Habib
%T Benchmarking Hybrid ANN-LSTM and Physics-Informed Neural Networks for Forecasting Stock Market Prices
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 74
%P 1-8
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study presents a novel approach for forecasting stock market prices by combining Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models into a hybrid ANNLSTM framework. This study focus on forecasting the closing prices of the S&P 500 and Toronto Stock Exchange (TSX) indices, evaluating the performance of the proposed hybrid model against traditional ANN, LSTM, and Physics-Informed Neural Network (PINN) models. The hybrid ANN-LSTM model demonstrates superior forecasting accuracy, outperforming the individual models and the PINN in terms of multiple evaluation metrics. The training dataset spans from January 1, 2005, to December 31, 2020, while the testing period covers January 1, 2021, to January 31, 2024. The results highlight the potential of hybrid deep learning models, specifically the ANN-LSTM combination, in enhancing stock market prediction accuracy, representing a significant advancement over conventional methods.

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Index Terms

Computer Science
Information Sciences
Stock Market Forecasting
Hybrid ANN-LSTM Model

Keywords

Artificial Neural Networks Long Short-Term Memory Physics- Informed Neural Networks Deep Learning in Finance Time Series Prediction

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