Journal of Risk and Financial Management

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Special Issue Editors

Dr. Hassan Anjum Butt

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Guest Editor

Plaster School of Business, Missouri Southern State University, Joplin Missouri, Joplin, MO, USA
Interests: financial econometrics; energy economics; risk management

Dr. Brian Nichols

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Guest Editor

Plaster School of Business, Missouri Southern State University, Joplin Missouri, Joplin, MO, USA
Interests: corporate finance; financial technology; portfolio returns

Special Issue Information

Dear Colleagues,

The financial world is always changing, driven by new technology. This brings both big opportunities and new challenges, especially with regard to financial risk. This Special Issue wants to look at the evolving connection between financial risk and new technology. FinTech has changed how we do business, access money, and manage assets. Elements such as algorithmic trading, blockchain, mobile banking, and robo-advisors have made things more efficient and cheaper, and opened up financial services to more people, but they also create new weaknesses. Algorithmic trading can cause sudden market drops, and the decentralized nature of cryptocurrencies raises questions about regulation and protecting investors. Furthermore, technology makes global financial systems more connected, which can make systemic risk worse. On the other hand, technology also gives us strong tools in order to deal with these risks. Artificial intelligence and machine learning can help with risk assessment, fraud detection, and better regulatory compliance. Big data analytics help us understand complicated financial trends for better decision-making, and cybersecurity innovations are key to protecting financial institutions from increasingly advanced cyberattacks.

This Special Issue aims to gather research that carries out the following:

Examines the impact of specific technological innovations on financial risk.
Analyzes the effectiveness of technology-driven risk management solutions.
Investigates the regulatory challenges posed by FinTech.
Explores the ethical considerations surrounding the use of technology in finance.
Provides insights into the future of financial risk in a technologically advanced world.

We invite scholars, practitioners, and policymakers to submit original research that contributes to a deeper understanding of this critical and evolving field.

Dr. Hassan Anjum Butt
Dr. Brian Nichols
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Research

34 pages, 2684 KB

Open AccessArticle

Risk Prediction of International Stock Markets with Complex Spatio-Temporal Correlations: A Spatio-Temporal Graph Convolutional Regression Model Integrating Uncertainty Quantification

by Guoli Mo, Wei Jia, Chunzhi Tan, Weiguo Zhang and Jinyu Rong

J. Risk Financial Manag. 2025, 18(9), 488; https://doi.org/10.3390/jrfm18090488 - 2 Sep 2025

Viewed by 453

Abstract

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

(This article belongs to the Special Issue Financial Risk and Technological Innovation)

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