KMV Model Research Articles

관광기업의 공유가치창출과 종사원의 신뢰, 관계몰입 및 성과태도의 관계

This study discussed the structural relationship between creating shared value, trust, relationship commitment, and performance attitude of tourism companies based on the KMV model. This study collected data from 335 tourism employees to verify the proposed research model. Structural equation analysis was performed to verify the relationship between the constructs. A process macro(4) analysis was conducted to verify the mediating effect of trust and relationship commitment. According to the results, creating shared value had a positive influence on trust, relationship commitment, and performance attitude. And trust had a positive influence on relationship commitment. Trust and relationship commitment had a positive influence on performance attitude. Lastly, trust and relationship commitment mediated the relationship between creating shared value and performance attitude. This study has the following academic implications: First, this study is meaningful in that it discussed creating shared value as claimed by the KMV model of Morgan and Hunt (1994). Second, this study filled in some of the gaps that the existing KMV model didn't cover.

RETRACTED ARTICLE: Applying blockchain technology in the corporate bond model for default risk assessment under the marketization principle

This work expects to solve the problem that the traditional corporate bond model for default risk assessment has low accuracy and poor data collection and storage robustness. Firstly, this work uses the mature Kealhofer, McQuown, and Vasicek (KMV) model to evaluate the default risk of corporate bonds. Secondly, Blockchain (BC) technology's Information Security (SEC) performance in default risk assessment is studied. Finally, it analyzes the default disposal mechanism of Chinese bond corporates. The Wind database is selected to verify the proposed model. The results show that the proposed BC-based corporate bond model for default risk assessment has a good fitting effect and can meet practical needs. The BC-based data encryption only needs 14.1 ms to obtain a key from an 821-bytes public key. The encryption time and decryption time of the Number Theory Research Unit (NTRU) system are 7.9 ms and 2.9 ms, respectively. Therefore, BC technology shows good performance in data encryption and can improve ISEC. According to the KMV model, corporate bonds have certain default risks. The Distance to Default (DtD) of corporate bonds is 20.8% lower than that of companies. Thus, corporates have higher default risk. Therefore, methods are needed to reduce the default risk and improve the earnings. The finding provides important technical support for effectively reducing the default risk of bond corporates in China and ensuring the healthy development of bond markets.

The Prevention of Financial Legal Risks of B2B E-commerce Supply Chain

B2B supply chain finance is a new type of financial model, which is created to help companies raise funds and promote the production, operation, and development of companies in the supply chain. Based on the B2B e-commerce platform, it is used for online transactions and transactions between companies and companies. Information, integrating logistics, business flow, information flow, and capital flow for data analysis and processing. When people enjoy convenient and fast e-shopping, they must not only choose products carefully but also understand and be familiar with the relevant laws and regulations of shopping on the Internet. Avoiding potential legal risks is a key factor. The purpose of this article is to analyze the financial risks of the B2B e-commerce supply chain, according to the current Internet development trend, study the legal risks of the B2B e-commerce supply chain in the development, put forward corresponding recommendations, and build a relevant system to reduce risks. Combining some current legal issues faced by e-commerce, this article first analyzes the generation and operation mechanism of credit risk under the B2B platform online supply chain financial business model; then, based on the supply chain financial risk, a relevant system is constructed to reduce risks. This article first analyzes the generation and operation mechanism of credit risk under the online supply chain financial business model of the B2B platform; then, based on the supply chain financial risks, construct a system that can prevent and control the risks generated under this financial business model risk evaluation index system; finally, the KMV model and case are analyzed to verify whether this risk evaluation research is effective for supply chain financial risks. The experimental results show that through the KMV model, comparing the two sets of data, the default distance of most default groups is smaller than that of the normal group. It can be seen that the greater the default distance, the smaller the credit risk. When the default point coefficient is 0.85, use the KMV model which is most obvious when judging the company’s overall probability of default.

The Reduction of Credit Risk in the Health Care Industry in China: Are We Returning to the Pre-COVID-19 Era?

This study aims to evaluate the changes in the credit risk of the health care industry in China due to the COVID-19 epidemic by the modified KMV (named by Kealhofer, Mcquown, and Vasicek) model to calculate the default distances. We observe that the overall default distance mainly first decreased and then increased before and after the COVID-19 epidemic control in China; after the epidemic was controlled, the overall credit risk was reduced by 22.8%. Specifically, as shown in subdivided industries, health care equipment and health care facilities have larger credit risk fluctuations, while health care suppliers, health care distributors, and health care services have smaller fluctuations. These results can contribute to our understanding of why the COVID-19 epidemic in China could be controlled earlier, and software facilities are more important than hardware facilities in public health safety. Our methodological innovation is to use the GARCH (generalized autoregressive conditional heteroskedasticity) model and threshold regression model to modify the important parameters of the KMV model. This method has good accuracy in the Chinese environment.

Establishment of business risk information value assessment model based on RAROC

Aiming at the problem of low precision of current enterprise risk assessment methods, a RAROC based enterprise risk information value assessment model is proposed. The crawler search method with topic search is adopted to collect the business information data, and the extended tree structure is introduced to clean up the collected data, transformation of basic information data and business value. RAROC is used to process the information with commercial value and realise the value evaluation of commercial risk information. According to the model and KMV model, the risk adjustment coefficient was calculated. The results of the RAROC value evaluation are reflected through statements. The experimental results show that the goodness of fit index, standard fit index and comparison fit index of the model are all close to 1, and the approximate root-mean-square error is less than 0.02, which proves the effectiveness and accuracy of the method.

Risk Assessment of Local Government Debt based on KMV Model: A Case Study of Hangzhou, Zhejiang Province

The present government debt governance focuses on calculating, preventing, and controlling local government debt risk. The default probability of local government debt in Hangzhou, Zhejiang Province, is calculated using a modified “Kealhofer, McQuown, and Vasicek” (KMV) model. The findings reveal that Hangzhou’s debt risk in the next three years is usually manageable, but that debt risk will progressively emerge in the coming years when the debt payback cycle begins.

Research on the construction of stock portfolios based on multiobjective water cycle algorithm and KMV algorithm

The financial situation of listed companies has a great impact on the construction of stock portfolio. However, some traditional portfolio models only consider the fluctuation of stock price and ignore the impact of the financial situation of listed companies on the portfolio, which will affect the effectiveness of the portfolio. To fill the gap, a new portfolio model based on the KMV model and a multiobjective water cycle algorithm is proposed to further improve the framework of portfolio construction. Firstly, the KMV model is used to evaluate the financial situation of listed companies, and then the optimization algorithm directly uses both the results of KMV and the fluctuation of stock price to build a more reasonable portfolio. To evaluate the validity of the model, the data of 100 A-share listed companies collected from China were used in two experiments. The results of the experiments show that the model can determine the stock portfolio according to the internal financial information and stock history information of listed companies, which not only can improve the effect and stability of the investment portfolio, but also can play a guiding role in the performance evaluation of listed companies.

Research on modeling of government debt risk comprehensive evaluation based on multidimensional data mining

In order to solve the problems of low accuracy of data mining, high relative error rate of evaluation, and long time of evaluation in traditional government debt risk evaluation methods, this paper proposes a modeling method of government debt risk comprehensive evaluation based on multidimensional data mining. The MAFIA algorithm is used for multidimensional mining of government debt risk data, and K-means clustering algorithm is used for clustering processing of mined data. The KMV model is built based on the clustering findings, and the uncertainty factor is utilized to alter the model in order to provide a complete assessment of government debt risk using the modified KMV model. The experimental results show that the accuracy rate of government debt risk data mining is always above 91%, the relative error rate of evaluation is always below 3.4%, and the average evaluation time is 0.71 s, the practical application effect is good.

Credit risk and equity returns in China

We examine how credit risk affects equity returns in China, where the financial system is dominated by debt-financing. Based on EDF (expected default frequency) measured by the KMV model, we construct a credit risk factor UMT (untrustworthy minus trustworthy). The empirical results show that UMT significantly improves the pricing effectiveness of the Fama-French five-factor model and that the modified model with the investment factor replaced by UMT is more applicable to the Chinese environment. Our results also show that stocks with higher credit risk have higher expected returns, and credit risk premiums are an essential part of equity returns in China.

Research on Countermeasures and Financing Risk of Small and Medium-Sized Enterprises in China

SMEs have played an important role in deepening of construction of socialist market economy in China. Their contribution rate to our country's economy is rising continuously, they also stimulate technical innovation. However, because of their small scale, credit margin and other factors and imperfect market mechanism, SMEs are facing a severe situation in the process of financing, the huge financing risk has become the core problem restricting the development of SMEs. This paper employed the KMV model to make an empirical analysis. Then, the author puts forward corresponding suggestions for the financing of SMEs in the future.

Research on Optimal Allocation Strategy of Bank Credit Funds Based on KMV Model and Logit Model

Using KMV model, normal Copula function, K-means cluster analysis and logit model, this paper constructs the enterprise credit risk assessment model, bank credit fund optimal allocation model, banking risk index system, and synthetically uses software such as MATLAB、SPSS to solve the problem of credit fund distribution strategy for small and medium-sized enterprises, and draws the conclusion that the loan interest rate classification of enterprise credit risk assessment, the weight of bank to credit fund distribution, and the change of bank risk index weight in sudden situation.Finally, the above model provides the strategy for bank credit fund allocation and gives the test and evaluation.
 The outstanding features of this paper are: using the KMV model and the normal Copula function, combining the enterprise credit rating and default times to establish a linear model to quantify the enterprise credit risk, will not beeasy to calculate the industry violation probability quantitative analysis, also get the bank credit annual interest rate fordifferent industries and levels of enterprises, and through the representative industries of the optimal loan weight calculation, so that the bank decision has the characteristics of the least unit risk. This paper also establishes a banking risk index system with emergency factors, which is of practical significance to make decision analysis of emergency events.

The application of KMV model in China’s insurance market during the COVID-19

In 2020, the COVID-19 has a certain impact on the credit risk of China’s listed of insurance companies. This paper selects relevant data of four China’s listed insurance companies from the first quarter of 2019 to the third quarter of 2020, and then uses revised KMV model to measure the credit risk of these four insurance companies. The empirical results show that in the season of the outbreak, the default distance of China’s listed insurance companies has decreased to varying degrees, indicating that the epidemic has caused a temporary increase in the credit risk of the insurance industry.

Research on Credit Risk Assessment of Commercial Banks Based on KMV Model

Under the background of the new normal economy, the financing mode of local governments has changed, which has led to the emergence of a new pattern in China's financing market. Under this background, banks are facing the development pressure of subject diversification, financial disintermediation and cross-border competition. Commercial banks also actively promote the compliance development of emerging businesses such as financial market through the "comprehensive direction", so as to form a balanced and complementary development situation with traditional credit business. Therefore, this paper taking credit risk and related theories as the starting point, KMV model is selected to study domestic bank risk monitoring. Then, China Merchants Bank is taken as the research object, Shengjing Bank, Harbin Bank, Qingdao Bank and Chongqing Bank are taken as horizontal comparison. The data sampling time span is January to December 2018, as vertical comparison. Five banks were evaluated for credit risk, and it was found that the default distance of China Merchants Bank was relatively small and the default probability was relatively large. However, according to the calculation and analysis of KMV model, the default probability and return volatility of China Merchants Bank rank first among the five commercial banks, but at the same time, it maintains a high net profit margin of 129.3% in 2018, which shows that China Merchants Bank has a strong ability to control credit risk.

COVID-19’s influence on the credit risks and enterprise innovation of the Guangxi manufacturing——Based on the measure of KMV model

The paper used the KMV model to manufacturing industry of Guangxi in China to concretely abstract the credit risk and enterprise innovation into a measurable quantitative index, and compare the changes in credit risk before and after COVID-19. This paper selects 17 Listed Companies in Guangxi manufacturing industry as empirical samples, and calculates the expected default rate of different companies by using the traditional and modified KMV models. The larger the index value is, the higher the credit risk is, And then affect the enterprise innovation activities. The results show that the overall credit risk management ability of Guangxi’s manufacturing industry is relatively high, but by the impact of COVID-19, credit risk has increased. If left unguarded, it will have an impact on enterprise innovation.

Local government debt risk measurement and early warning structural system building — Research Based on KMV and TOPSIS Models

In this paper, by using the Statistical analysis Software SPSS20, 30 and Cities of our country’s local government structural debt risk has been carried on the empirical analysis, using the entropy weight method to determine the specific weight of each index, reduced the subjective factors of influence on the result of the index weight distribution, expectations of local government debt defaults and the situation of the comprehensive evaluation index of local government debt. By comparing the size of the threshold value, to determine the risk of local government debt, the results show that in 7 provinces and cities in China are in the state of no warning of local debt risk, in 10 provinces and cities are in the state of mild warning. Means and Cities are in the State of Moderate warning, In other words, it means it’s time to generate the risk of local government debt. In the State of high warning, most means and cities need to take scientific measures to generate the risk of local government debt.

Study on credit risk of real estate industry based on genetic algorithm KMV model

This paper firstly analyzes the current situation of credit risk in China’s real estate industry, and then compares the traditional and modern credit risk measurement models. On this basis, the KMV model is selected and the artificial intelligence model genetic algorithm (GA) is introduced to improve the accuracy of KMV model. Secondly, annual financial data and stock trading data of 108 real estate listed companies from 2010 to 2019 are selected for empirical research. The analysis of the total default distance between the 108 companies and the actual economic development in China proves that the results of the GA-KMV model are in good agreement with the economic development trend, indicating that the model has good applicability. Finally, some suggestions are put forward according to the empirical results.

Research on the Effectiveness of KMV Model in China's Bond Credit Rating Market

In recent years, China's bond market has experienced rapid development, but the pace of credit risk supervision has not kept up. Since 2014, the number of domestic credit bond defaults has increased. In 2016, there were 79 domestic default bonds, with a default amount of up to 40.3 billion Yuan. From the perspective of domestic bond market credit risk supervision and early warning mechanism, rating is not objective, and tracking is not timely also rating methods are backward. Therefore, with the development of big data and other technologies, it is urgent to study credit risk supervision methods suitable for the domestic bond market. On the basis of combing the development of domestic bond market and analyzing the current situation of domestic credit rating, this paper combines the results of theoretical research at home and abroad, the information available in the domestic market, big data mining and automation technology, based on the financial and stock exchange information of listed companies, combined with BS option pricing theory, constructs KMV model.

A prediction model for the secure issuance scale of Chinese local government bonds

Purpose The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN). Design/methodology/approach The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis. Findings This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively. Practical implications The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings. Originality/value This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.

An analysis of credit risk of middle and small-sized enterprises in the inclusive financial system: an example from China

Inclusive finance is a development direction of current finance reform for the nations of the world. Based on the theory of inclusive finance, this paper analyses the credit risk of middle and small-sized enterprises in China through KMV model. In empirical analysis, we choose the sample data coming from ST company, *ST company and non-ST company in the growth enterprise market of China. After calculating the default risk and the default probability of enterprises, the results show that, in general, the enterprises in unnormal operation have higher default risks relative to the enterprise in normal operation. According to sample data, individual enterprises in unnormal operation also have lower default risks. In the inclusive financial system, governments should pay more attentions to the development of middle and small-sized enterprises, and establish some proper measures to provide financial services for middle and small-sized enterprises, especially for enterprises in unnormal operation.

Research on Regional Debt Risk in Hubei Province Based on Modified KMV Model

With the increasing amount of Regional debt, how to explain and describe this phenomenon has become very important. The revised KMV model can precisely undertake this work, and quantify the level of debt risk through the description of default probability and other factors, providing a theoretical basis for managers to solve this risk.