Mining The Default Characteristics Of Small Private Businesses Lending Micro Credit Based On Quantile Regression

As an important part of the private economy,small private businesses are the special economic subjects formed during the reform of China's economic system and the development of the market economy.They have made important contributions to economic development and are indispensable "capillaries" of economic activities.As of the end of 2019,there were 82.61 million small private businesses registered in industry and commerce sectors in China,accounting for 66.95% of all market players(123.395 million households).The number of employed small private businesses registered in industrial and commercial registration reached 169.305 million,of which rural individuals the number of employees reached 58.595 million,accounting for 34.6% of the total number of private individuals employed in industry and commerce registration.As one of the largest and most widely distributed groups among rural financial customers,the financial needs of rural small private businesses have an important impact on the development of rural finance.However,compared with enterprises,rural small private businesses have a small business scale,opaque information,and difficult risk measurement.Therefore,banks are more inclined to lend to enterprises with higher credit levels,which makes financing difficult to become a bottleneck restricting the development of rural small private businesses.In turn,it has affected the development of China's rural economy.The key to improving financial services to support rural revitalization and promote rural financial reform and development is to alleviate the loan problem of rural small private businesses.And analyze the key characteristics of indicators,systematically sort out and analyze the regular relationship between credit evaluation indicators and default losses,and provide countermeasures and suggestions for alleviating financing constraints of individual rural small private businesses.This paper consists of five parts,as follows:The first chapter is an introduction,pointing out the research purpose and significance of this paper,in-depth analysis of the status quo of merchant development and domestic and foreign research on merchant credit risk evaluation index system and shutdown characteristics mining status quo,and briefly introduce the research content and research methods of this paper.The second chapter defines the relevant concepts involved,sorts out the theoretical basis of credit risk research,and elaborates the methods used to construct the merchant credit risk evaluation index system.Chapter three is the empirical process of constructing the merchant credit risk evaluation index system.Using the credit data of 2,157 merchants in the commercial microfinance system of a large state-owned commercial bank in China,the selection index of commercial credit risk assessment with 53 indicators was collected.Screened on inventory turnover,engaged in the industry,bank loan ratio,revenue,sales net interest rate,per capita average of fixed assets,loan purposes,liquidity ratio,household spending and business area,total assets,the number of members,debt ratio,average age,month operating income,net income,operation time,accounts receivable turnover ratio,total assets return rate,operating expenses,education and other 21 small loan credit risk affect significantly the merchant key indicators.The fourth chapter is the merchant credit risk key characteristics mining.The quantile regression method is used to further explore the key characteristics of the indicators in the evaluation indexes selected in the third chapter,and further analyze the influence differences of the key indicators at different points of the merchant Loss Given Default.Chapter five is the conclusion and suggestion.Summarized in this paper,we conduct empirical work and relevant conclusions,respectively on the basis of the credit segment of the merchants involved in the main body(demanders),Banks and other financial institutions(suppliers)and the people's bank of China and the China banking regulatory commission(regulators)respectively,puts forward relevant policy recommendations,it points out the limitations of this article,put forward the direction of the follow-up to continue to improve and perfect.The research shows that the loan merchants are divided into two categories according to the Loss Given Default: high default loss merchants and low default loss merchants,and the quantile regression is used to analyze the credit evaluation index of 21 small loan merchants.The empirical results show that the influence of different characteristics of the same index on the Loss Given Default of merchants is significantly different.Specifically,in the "industry engaged" indicator,the retail wholesale industry's Loss Given Default is generally higher than other industries,indicating that retail wholesale is a key feature of the "engaged industry" indicator.In the same way,it can be concluded that "junior high school and above" is the key feature of merchants' default losses,"2" is the key feature of the number of members,and "purchasing real estate" is the key feature of loan use.Among the indicators reflecting the situation of merchants 'households,household expenditure has a significant impact on merchants with a low Loss Given Default,while the operating area index has a more significant impact on merchants with a higher Loss Given Default.At the level of financial indicators,indicators that reflect solvency,profitability,and development capabilities all have a more significant impact on merchants with higher Loss Given Default.