Regional Financial Risk Research Articles

Assessment of regional financial risk based on an integrated approach

Subject. The article considers a comprehensive model to assess the financial risk of the region. Objectives. The aim is to develop a financial risk prevention model that integrates economic, social, political, environmental, and financial indicators, having a significant impact on the economy. Methods. The study rests on methods of synthesis, analysis, comparison, and economic and mathematical calculations, i.e. weighted summation with preliminary normalization of each estimated indicator. The process of data collection, normalization, selection of indicator weights, and calculation of integral indicator of financial risk was implemented for 2022. Results. We established the presence of financial risks in the region. Both the results and the model itself can be useful for further research. The advantage of the model is the relative small number of applied indicators. This facilitates the collection of information, its processing and evaluation. The evaluation of the above calculations reflects the conditionality of ranking. The results that reflect dynamics are more indicative. Conclusions. The formed model, when assessing regional financial risk, enables to take into account the multidimensionality of modern regional economies.

Application of Deep Learning in Regional Financial Risk Assessment and Prediction

As the economy enters the new normal, more and more risk problems are exposed in the financial market, and the market supervision system is still not perfect. Preventing financial risks has become the focus of domestic attention. Ubiquitous risks have increasingly become the "sword of Damocles" hanging over the main body of financial markets. Due to the differences in regional economic development, regional financial risks will become more complicated, which is likely to lead to a nationwide financial crisis. The application of DL (Deep Learning) has become the research frontier in the field of financial risk management, which will surely bring about subversive changes in the field of financial risk management. Through DL, we can quantitatively evaluate the financial risks in different fields, closely monitor the key industries and fields with the highest regional financial risk distribution, strengthen risk monitoring and analysis, promptly use risk warning letters, situation reports and other forms to prompt risks, prevent the spread and spread of risks in specific industries or fields, and do a good job in risk prevention and response. In order to effectively identify regional financial risks, timely understand and master the distribution and impact of various risks, and do a good job in risk monitoring and prompting.

Research on Regional Financial Risk Identification and Contribution Analysis Based on ANN-RBF Neural Network

Regional financial risk is an issue that cannot be ignored around the world today. Studying financial risk and analyzing the deep-seated influential factors is conducive to reducing the generation of financial crises. This paper aims to study the influencing factors of regional financial risk and their contribution. Firstly, we identify influential factors of regional risk in the three northeastern provinces of China, then based on the artificial neural network-radial basis function (ANN-RBF) algorithm, we collect data from the region for the past 20 years for analysis and calculate the contribution of different risk influential factors to regional risk. The results show that: (1) CPI dominates in influencing the regional financial risk in the three northeastern provinces of China, followed by the growth rate of import and export and the local public finance surplus. (2) When the neuron is 29, the accuracy of the algorithm converges at this point and the model reaches optimum. The research in this paper is conducive to uncovering the influential factors of regional financial risk and reducing the generation of financial crises.

Early warning model and prevention of regional financial risk integrated into legal system.

In order to improve the laws and regulations of the financial system, in the construction of laws and regulations, the traditional financial risk Early Warning (EW) model is optimized. The financial prevention and control measures with legal protection are implemented to warn the financial risks, which plays an important role in the construction of the rule of law in the Financial Market (FM) and the establishment of financial risk prevention and control laws and regulations. This paper combines the deep learning model and the Markov regime Switching Vector Auto Regression (MS-VAR) model and constructs a regional financial risk EW model from the following aspects: macroeconomic operation EW indicators, regional economic risk EW indicators, regional financial institution risk EW indicators. The model is empirically researched and analyzed. The results show that the fluctuation trend of the macroeconomic pressure index in the time series is relatively large, and the overall fluctuation of the regional economic pressure index is small, and fluctuates around 0 in most periods. After the financial crisis, local governments stepped up their supervision of non-performing corporate and household loans. From 2011 to 2018, the non-performing loan ratio began to decline, and the overall fluctuation of the regional financial comprehensive stress index was small, fluctuating around 0. Due to the lack of legal regulation, from the perspective of the regional economy, the risk level is more likely to change from low risk to moderate risk, while the risk status is less likely to change from high risk to moderate risk. From the perspective of regional financial institutions, the probabilities of maintaining low risk and moderate risk are 0.98 and 0.97, respectively, which is stronger than maintaining the stability of high risk. From the perspective of the state transition of the regional financial risk composite index, the probability of maintaining low risk and high risk is 0.97 and 0.93, which is higher than maintaining the stability of medium risk. The Deep Learning (DL) regional financial risk EW MS-VAR model has strong risk prediction ability. The model can better analyze the conversion probability of regional financial risk EW index and has better risk EW ability. This paper enhances the role of legal systems in financial risk prevention and control. The regional financial risk EW model incorporating financial legal indicators can better describe the regional financial risk level, and the EW results are basically consistent with the actual situation. In order to effectively prevent financial risks and ensure the safety of the financial system, it is recommended that the government improve local debt management, improve financial regulations and systems, and improve the legislative level of financial legal supervision.

The Impact of Financing Constraints on the Shadow Banking of Non-Financial Firms

According to China's Financial Stability Report, since 2020, instability and uncertainty at both domestic and international levels have risen significantly. While financial risks are multifaceted, regional financial risks remain hidden, the risk of default on some enterprises' debts has increased, and the risks of individual small and medium-sized banks are more prominent. In the face of risks, China clearly pointed out that financial security is an important part of economic security. While promoting economic and social development, China should maintain the continuity, stability, and sustainability of macro financial policies, prevent financial risks, and deepen the reform of financial institutions. Currently, some non-financial enterprises in China are engaged in shadow banking through direct or indirect means. However, shadow banking has the characteristics of hidden risks and cross-market risk contagion, which can induce risks in the process of development and threaten the stability of the financial system and financial security. Therefore, further research on shadow banking is needed. Using a sample of A-share non-financial listed companies in China from 2010-2021, this paper conducted a fixed-effects regression on panel data to empirically investigate the regional heterogeneity of financing constraints on the shadow banking of non-financial enterprises and the heterogeneity of the nature of corporate ownership, and obtained the following conclusions. a) financing constraints will enhance the scale of shadow banking of non-financial enterprises. b) there is regional heterogeneity concerning the influence of financing constraints on the shadow banking of non-financial enterprises. c) there is heterogeneity in the nature of ownership for financing constraints.

Analysis of regional financial risks in Shenzhen, China

Financial risks are characterized by complexity, concealment, abruptness, infectivity and harmfulness. In the 40 years since the establishment of China's Special Economic Zone, Shenzhen's financial industry has become the third largest financial center on the mainland. At present, the financial industry has developed into one of the four pillar industries in Shenzhen. The significance of this paper is to explore and evaluate the regional financial risks in Shenzhen, and put forward targeted suggestions for Shenzhen's banking, securities and insurance, and real estate markets according to the quantitative results. Based on the annual data of Shenzhen from 2000 to 2020, entropy weight method and CRITIC method are used to determine the weight of grading indicators. This paper proposes three innovative scenarios using Holt Winters three exponential smoothing method to predict data. Finally, the risk scoring system is constructed according to the scenario analysis results, and optimization suggestions are put forward according to the early warning results.

Investigation on the Impact of Digital Finance Development on Regional Financial Risks

Investigation on the Impact of Digital Finance Development on Regional Financial Risks

Progress on Machine Learning for Regional Financial Risk Prevention

Progress on Machine Learning for Regional Financial Risk Prevention

Financial risk assessment of six regions in China under the TOPSIS model based on a coupling perspective

As China's economy enters a new normal, the economy gradually changes from high speed to high quality, and the task of resolving various types of risks in each region becomes more arduous, while the prevention and control of regional financial risks will certainly become one of the severe challenges faced under the new economic normal. Based on the parallel data of six regions in China, this paper combines EWM subjective weights and AHP objective weights to construct EWM-AHP coupling weights. It also adopts the distance between ideal point superiority and inferiority solution (TOPSIS) ranking method to quantify the relative relationship between the sample and the evaluation criteria and establishes a differentiated financial risk indicator model for the six regions. The results show that the financial risk is lower in regions with a weaker economic base and lower level of financial development. Financial risk is higher in regions with a higher degree of financial openness. The integrated EWM-AHP and TOPSIS models can effectively reduce the financial risks in the development process of the six regions and improve the smoothness of economic operation, which has a particular application value. The results show that the financial risk is higher in regions with more backward economic development, and the financial risk index is lower in regions with sound capital market development.

Analysis of Regional Financial Risk in Guangdong Province Based on the DCN Deep Learning Model.

In the free flow of financial factors oriented to capital, returns will be accompanied by the concentration and diffusion of financial resources to form regional financial spatial differences, which is an objective phenomenon of regional financial practice. Localized regional financial risks may appear in the process of regional financial practice in each region. To address the abovementioned problems, we propose a model for regional financial risk analysis based on the DCN deep learning model. The main contents are as follows: elaborating the financial risk transmission mechanism involving intra- and interregional financial risks, sorting out the relationship between sectors as clues; the designing process of regional financial risk index as well as the measurement method, and the regional financial risk index for typical regions is measured and found to be at peak in 2017 with a risk index of 0.58; and the construction of an early warning model based on the value of the regional financial risk index and the expansion of the RNN network applied to the construction of the regional financial risk early warning system. Based on the construction of the RNN network application risk early warning system, the three types of risks, payment risk, loan loss risk, and market risk with the percentages of 49.62%, 26.82%, and 23.56%, respectively, are derived, and the focus is on their supervision and management in the follow-up work.

Study on the comprehensive evaluation of regional financial risk

Since 2011, the Chinese government began to pay attention to the prevention of regional financial risk in the financial field. The effective assessment of regional financial risk is an indispensable link in risk prevention, which is of great significance to maintain economic operation. This paper selects 5 first-level indicators and 15 second-level indicators to build a regional financial risk evaluation system. Based on the economic data of Jiangsu from 2015 to 2020, AHP and entropy weight method are used to calculate the corresponding weights of indicators at all levels. The multi - level fuzzy evaluation model is used to evaluate the financial risk in Jiangsu province. The results show that the membership degree of financial risk to the "general security" level is 0. 5050 at most, among which the proportion of macro environment is 0. 3932 at most, and the degree of external environment is 0. 0625 at least. It can be seen that the economic operation of Jiangsu is relatively stable and the financial risk is small.

Research on the early warning system of regional financial risk

Research on the early warning system of regional financial risk

Retraction Note: Climate change in river basin based on machine learning and regional financial risk identification

Retraction Note: Climate change in river basin based on machine learning and regional financial risk identification

RETRACTED ARTICLE: Climate change in river basin based on machine learning and regional financial risk identification

RETRACTED ARTICLE: Climate change in river basin based on machine learning and regional financial risk identification

A Study on Regional Financial Risks Based on CoCVaR Model

For the purpose of accurate measurement of regional systemic financial risks and prevention of regional economic turmoil, this paper proposes a new measure called the CoCVaR model, based on the tail mean loss, which is applied to measure the impact of stock returns of each listed company on the overall stock returns in Guangdong Province, China, from January 2010 to December 2020. It is found that there are significant CoVaR and CoCVaR for real estate, finance, utilities, and energy companies, while the risk spillover to the real economy market in Guangdong Province is more significant when companies in these industries are in extreme situations. There are insignificant CoCVaR for daily consumption, information technology, and health care. The risk spillover to the real economy market in Guangdong Province is smaller when companies in these industries are in crisis.

Research on the early warning system of regional financial risk

Research on the early warning system of regional financial risk

Study on the Risk Transmission Mechanism of Rural Banks in China

This paper studies the rural banks’ credit risk factors and risk transmission path, sets up a nonlinear differential dynamical system model for the transmission and analyzes its equilibrium by phase-plane method, then discusses how credit risk dynamically configures to reach equilibrium among the nodes in the transmission process. The paper’s main conclusions are as follows: the sources of rural banks’ credit risk are transmitted inside the banks by virtue of the carriers of capital flow and information flow, and the recipients in the banks’ internal credit risk transmission may become new risk sources and lead to regional financial risks; the credit risk dynamic configuration may reach equilibrium among the nodes in the transmission subsystem. Finally, this paper gives suggestions on credit risk management of rural banks in China.

Systemic Financial Risk Inference in a Global Setting

We propose a new top-down approach to measure systemic risk in the financial system. Our framework uses a combination of macroeconomic, financial and rating factors in representative regions of the world. We formulate a mixed-frequency state-space model to estimate macroeconomic factors. To derive financial risk factors, we use Moody’s/KMV expected default frequencies after accounting for ratings of major financial institutions in the considered regions. The estimated factors are combined to derive probabilities for systemically relevant defaults in the financial industry. Regional macroeconomic factors are significant predictors of the existence and number of systemically important defaults, while regional financial risk and ratings factors are relevant for the existence only. For major events, global credit risk also matters.

The Regional Financial Risk Early-Warning Model Integrating the Regression of Lagging Factors

It is an essential demand to establish an effective financial risk early-warning system for financial security. But some factors have a lagging effect on financial risk. So this paper aims to construct a reasonable and practical indicator system for financial risk early-warning. First, determine the explained variables of the indicator made up by Chinese financial stress index. Second, confirm the explanatory variables consisting of the macroeconomic variable, the currency credit variable, asset price variable and the macroeconomic variable of correlative economic powers. Finally, propose an optimum forecast equation to predict financial systemic risk status. In conclusion, the forecast outcomes indicate that Chinese financial systemic risk is on rising trend in the past three quarters in 2011 and higher than the peak of 2008, whereas financial systemic risk declines at the fourth quarter.

The Research of the Regional Financial Risk Early-Warning Model Integrating the Regression of Lagging Factors

This paper make the explained variables our financial stress index consist of the synchronous variables financial systemic risk, and make the explanatory variables the macroeconomic variable, currency credit variable, asset price variable and the macroeconomic variable of correlative economic powers, then use stepwise regression method to establish the financial systemic risk best predict equation, thus set up the reasonable and practical financial systemic risk early-warning index system; besides, use the best prediction equations predicts the financial systemic risk status in 2011. The predicted results show that Chinese financial systemic risk is on the rise in the first three quarters and higher than the peak of 2008; financial systemic risk start to decline since the fourth quarter.