Analysis of Regional Financial Risk Identification and Prediction Under CVM-GM(1, N) Algorithm

DOI

10.2991/aebmr.k.220502.039How to use a DOI?

Keywords

Regional financial risk; CVM; GM(1,N); Macro risk; Industry risk

Abstract

Financial risk is characterized by “bottom-up” and “accumulation followed by the outbreak,” and the study of core financial risk factors is important for effective identification and prediction of regional financial risk. In this paper, 16 financial risk-related indicators of Guangdong Province, China, from 2011 to 2020 are selected and classified into macro risk and industry risk levels according to their correlation. Objective weights are assigned through the coefficient of variation method (CVM) method, and the GM(1, N) method is used to forecast the future regional financial risk of Guangdong province using the weighted indicator data. The results show that the above study can provide research implications for regional financial risk forecasting and provide a more comprehensive and scientific analytical framework and metrics for effectively monitoring regional financial risk in practice. It reveals the financial risk situation and the trend of risk changes in the province in the past ten years more realistically and objectively and provides a strong basis for formulating relevant risk prevention measures in the future.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

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TY  - CONF
AU  - MengIan Lei
AU  - Guanxu Wang
PY  - 2022
DA  - 2022/05/16
TI  - Analysis of Regional Financial Risk Identification and Prediction Under CVM-GM(1, N) Algorithm
BT  - Proceedings of the 2022 International Conference on Urban Planning and Regional Economy（UPRE 2022）
PB  - Atlantis Press
SP  - 213
EP  - 219
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220502.039
DO  - 10.2991/aebmr.k.220502.039
ID  - Lei2022
ER  -