International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Deep Learning the Dynamics of Financial Contagion: LSTM Networks for Spillover Detection in Four GCC Stock Markets

Abstract

This paper presents Long Short-Term Memory (LSTM) networks for detecting and predicting financial spillovers in the Gulf Cooperation Council (GCC) stock markets. Although widely used, traditional Vector Autoregression (VAR) models do not capture the nonlinear and asymmetric dynamics common in regional financial contagion, especially during crises. We apply LSTM networks to the daily returns of four major GCC stock indices: Saudi Arabia, Oman, Dubai, and Qatar. The sample spans 2,273 trading days from March 2012 to December 2024. Our results show that LSTM models improve out-of-sample prediction accuracy by 23% compared to VAR models and detect spillover events three days earlier. The study also shows that Saudi Arabia's market share has grown to 67% since 2020, underscoring its growing importance as a financial center in the region as part of the Vision 2030 initiatives. Spillovers vary widely. For instance, correlations average 67% during crises but only 28% during normal times. These results have immediate implications for regulatory authorities and investors, providing a framework for real-time early-warning systems that can enhance monitoring of regional financial stability.

Country : Saudi Arabia

1 Mohanned H. Alharbi

  1. Finance and Business Sector, Institute of Public Administration, P.O.Box 205 Riyadh 11141, Saudi Arabia

IRJIET, Volume 10, Issue 1, January 2026 pp. 74-86

doi.org/10.47001/IRJIET/2026.101009

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