• ISSN: 2301-3567 (Print), 2972-3981 (Online)
    • Abbreviated Title: J. Econ. Bus. Manag.
    • Frequency: Quarterly
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Fiona Chu
    • Abstracting/ Indexing:  CNKI, Google Scholar, Crossref
    • Article Processing Charge (APC): 500 USD
    • E-mail: joebm.editor@gmail.com
HOME > Archive > 2025 > Volume 13 Number 2 (2025) >
JOEBM 2025 Vol.13(2): 174-179
DOI: 10.18178/joebm.2025.13.2.863

Analysis of the Transmission Mechanism of Exchange Rate Fluctuations in Emerging Markets to Global Portfolio Risk Based on Deep Learning

Wu Xiannan* and Ren Tingting
Finance Management, School of Accounting, Hangzhou Business School, Zhejiang Gongshang University, Zhejiang, China
Email: 2811758388@qq.com (W.X.N.); 3071925956@qq.com (R.T.T.)
*Corresponding author

Manuscript received November 5, 2024; accepted January 2, 2025; published April 23, 2025.

Abstract—As the process of global financial market integration accelerates, the role of exchange rate volatility in emerging markets is increasingly highlighted in the context of global capital flows and portfolio risk management. This paper employs deep learning techniques to comprehensively analyze the risk transmission mechanism of exchange rate volatility in emerging markets on global investment portfolios. Building upon a deep neural network model, the study effectively captures the time-series dependencies of exchange rates by employing Recurrent Neural Networks (RNN) and its variant, Long Short-Term Memory networks (LSTM), and combines Convolutional Neural Networks (CNN) to enhance the extraction of spatial features in exchange rate fluctuations, thus forming a deep understanding of the characteristics and risk transmission pathways of exchange rate volatility in emerging markets. The research finds that deep learning models have significant advantages in processing large-scale, high-dimensional financial data, especially in predicting the interconnectedness between exchange rate fluctuations and global asset prices during sudden market events. Furthermore, the paper utilizes Deep Reinforcement Learning (DRL) to explore the interactive effects between intervention policies of central banks in emerging markets and international capital flows, providing a profound analysis of the impact of global macroeconomic policies on exchange rate volatility and portfolio risk management. Ultimately, the conclusions of this study provide strategic guidance for the allocation of global multi-asset investment portfolios, optimize the risk control framework, and hold significant implications for the stability of financial markets and investment strategies.

Keywords—emerging market currency volatility, global portfolio risk, deep learning, long short-term memory networks, deep reinforcement learning

Cite: Wu Xiannan and Ren Tingting, "Analysis of the Transmission Mechanism of Exchange Rate Fluctuations in Emerging Markets to Global Portfolio Risk Based on Deep Learning," Journal of Economics, Business and Management, vol. 13, no. 2, pp. 174-179, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

PREVIOUS PAPER
New Thinking on Cost Analysis of Comparable Products
NEXT PAPER
The Influence of Internet Celebrity Economy on the Tourism
Copyright © 2008-2025. Journal of Economics, Business and Management. All rights reserved.
E-mail: joebm.editor@gmail.com