A Hybrid Deep Learning Approach for Systemic Financial Risk Prediction

被引：3

作者：

Zhou, Yue ^{[1
]}

Yan, Jinyao ^{[2
]}

机构：

[1] Commun Univ China, Sch Informat & Engn, Beijing, Peoples R China

[2] Commun Univ China, State Key Lab Media Convergence & Commun, Beijing, Peoples R China

来源：

COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT I | 2020年 / 12249卷

基金：

中国国家自然科学基金;

关键词：

Financial risk; Deep learning; Time series prediction; NETWORK; IDENTIFICATION; CONTAGION;

D O I：

10.1007/978-3-030-58799-4_62

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Systemic financial risk prediction is a complex nonlinear problem and tied tightly to financial stability since the recent global financial crisis. In this paper, we propose the Systemic Financial Risk Indicator (SFRI) and a hybrid deep learning model based on CNN and BiGRU to predict systemic financial risk. Experiments have been carried out over Chinese economic and financial actual data, and the results demonstrate that the proposed model achieves superior performance in feature learning and outperformance with the baseline methods in both single-step and multi-step systemic financial risk prediction.

引用

页码：859 / 874

页数：16

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