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.
Cite this article
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 -