Stock Market Trend Prediction Using Deep Learning Approach

Mahmoud Ahmad Al-Khasawneh (), Asif Raza (), Saif Ur Rehman Khan () and Zia Khan ()
Additional contact information
Mahmoud Ahmad Al-Khasawneh: Skyline University College, University City Sharjah
Asif Raza: Bahauddin Zakariya University
Saif Ur Rehman Khan: Central South University
Zia Khan: Central South University

Computational Economics, 2025, vol. 66, issue 1, No 15, 453-484

Abstract: Abstract Since the dawn of financial market trading, traders have continually sought methods to enhance their predictive capabilities for future price movements. This pursuit is driven by the significant daily trading volumes observed in financial markets worldwide. While traditional econometric and statistical methods have historically dominated in forecasting the behaviors of stock exchanges such as the Pakistan Stock Exchange, there remains a relatively limited exploration into the realm of artificial intelligence (AI) and machine learning (ML) techniques for addressing the inherent unpredictability of these markets. This study aims to improve the accuracy of forecasting the closing index of the Pakistan Stock Exchange by leveraging AI-based models, particularly employing the Deep Learning (DL) Long Short-Term Memory (LSTM) recurrent neural network. These DL models are anticipated to outperform traditional time series methods in predicting market indices. The primary objective of this work is to empower short-term investors with more precise index forecasts, enabling them to make informed and strategic trading decisions through the application of AI-based models.

Keywords: Artificial intelligence; Long-short term memory; Stock market; Forecasting problem (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10714-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10714-1

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10714-1

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().