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Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN
Version 1
: Received: 22 March 2024 / Approved: 24 March 2024 / Online: 25 March 2024 (13:10:23 CET)
A peer-reviewed article of this Preprint also exists.
Tian, Y.; Wu, Y. Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN. Mathematics 2024, 12, 1610. Tian, Y.; Wu, Y. Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN. Mathematics 2024, 12, 1610.
Abstract
Accurate measurement of systemic financial risk is critical to maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was con-structed by integrating six dimensions and employing Gray Relation Analysis (GRA) for dimensionality reduction of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA) optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trend of financial pressure indicators, the relationship between financial pressure and economic activity can be effectively identified. The research findings indicate that: (1) The CFSI is capable of accurately reflecting the current financial stress situation in China; (2) The IGSA-RBFNN demonstrates good robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology benefits policymakers and regulatory authorities to formulate preventive measures and risk management by identifying potential risks and vulnerabilities in advance.
Keywords
Financial stress index; Risk prediction; AHM; CRITIC; IGSA-RBFNN
Subject
Business, Economics and Management, Business and Management
Copyright: 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.
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