P2P network lending refers to the individual through the oJfline trading platform provided by the Business Company provides small loans to other individuals. According to the sea tree Lingyi data stalistics, as of Nov...P2P network lending refers to the individual through the oJfline trading platform provided by the Business Company provides small loans to other individuals. According to the sea tree Lingyi data stalistics, as of November 2014. China has 1371 P2P network lending company, than by the end of2013 638 over more than doubled. Since 2014,The cumulative volume of the whole network lending industry is up to 431.2 billion yuan. With the increasing social awareness in the industry, the future of the number and amount of P2P network lending companies in China will continue to grow rapidly. However, at present our P2P network credit risk management issues is serious, lacking of professional risk management personnel,who audit on the borrower＇s credit mostly limited to the upload information of borrowers. Credit rating is largely dependent on the subjective judgment of the risk control personnel and audit staff, which can not meet the requirements of the transaction participants in the loan security measures.This paper to pat the loan company were empirical research, through the logistic model to find the breach the key influence factors, to construct the borrower＇s credit risk evaluation methods, and to provide the P2P network lending risk control with specific policy recommendations.展开更多
Joint loan guarantee contracts and mutual guarantee contracts among SMEs form the basis of SME guarantee networks. The expansion of these networks increases the fragility of a financial system as a result of the regio...Joint loan guarantee contracts and mutual guarantee contracts among SMEs form the basis of SME guarantee networks. The expansion of these networks increases the fragility of a financial system as a result of the regional and industrial risk contagion embedded within them. By providing a theoretical framework of a loan guarantee network, a method is proposed for calculating the amount of risk spillover caused by loan guarantees taking the perspective of the entire network. In addition,the route of risk contagion in guarantee networks is analyzed, revealing that when default risk shocks occur, risk contagion travels along the nodes not once but for several rounds and that the risk control of one firm cannot prevent these systemic risks. Therefore, a risk control scheme is designed based on the location and importance of firms in the network. Using data from a real guarantee network,we demonstrate that identifying the node locations of firms' in the guarantee network(including the coritivity and closeness of the firm) can help in understanding risk contagion mechanisms and preventing systemic credit risk before a crisis occurs.展开更多
Traditional credit evaluation models failed to produce partial results due to their ignorance of the whole risks of credit environment. An excellent evaluating model on credit should take into account the credit envir...Traditional credit evaluation models failed to produce partial results due to their ignorance of the whole risks of credit environment. An excellent evaluating model on credit should take into account the credit environment impersonally and comprehensively. In this paper, a novel area＇s macroscopical credit evaluation model based on Fuzzy Neural Network is constructed. A set of scientific and reasonable evaluating indexes are extracted from feature space of macroscopical credit, then based on these indexes a Fuzzy Neural Network （FNN） model on credit evaluation is constructed and applied into the practical credit evaluation of some Chinese provinces randomly selected for the first time. Results show our model is both practical and capable.展开更多
文摘P2P network lending refers to the individual through the oJfline trading platform provided by the Business Company provides small loans to other individuals. According to the sea tree Lingyi data stalistics, as of November 2014. China has 1371 P2P network lending company, than by the end of2013 638 over more than doubled. Since 2014,The cumulative volume of the whole network lending industry is up to 431.2 billion yuan. With the increasing social awareness in the industry, the future of the number and amount of P2P network lending companies in China will continue to grow rapidly. However, at present our P2P network credit risk management issues is serious, lacking of professional risk management personnel,who audit on the borrower＇s credit mostly limited to the upload information of borrowers. Credit rating is largely dependent on the subjective judgment of the risk control personnel and audit staff, which can not meet the requirements of the transaction participants in the loan security measures.This paper to pat the loan company were empirical research, through the logistic model to find the breach the key influence factors, to construct the borrower＇s credit risk evaluation methods, and to provide the P2P network lending risk control with specific policy recommendations.
基金supported by the National Nature Science Foundation of China under Grant Nos.71172186,71472148,71572144 and 71502138
文摘Joint loan guarantee contracts and mutual guarantee contracts among SMEs form the basis of SME guarantee networks. The expansion of these networks increases the fragility of a financial system as a result of the regional and industrial risk contagion embedded within them. By providing a theoretical framework of a loan guarantee network, a method is proposed for calculating the amount of risk spillover caused by loan guarantees taking the perspective of the entire network. In addition,the route of risk contagion in guarantee networks is analyzed, revealing that when default risk shocks occur, risk contagion travels along the nodes not once but for several rounds and that the risk control of one firm cannot prevent these systemic risks. Therefore, a risk control scheme is designed based on the location and importance of firms in the network. Using data from a real guarantee network,we demonstrate that identifying the node locations of firms' in the guarantee network(including the coritivity and closeness of the firm) can help in understanding risk contagion mechanisms and preventing systemic credit risk before a crisis occurs.
基金The research is supported by the Major Programs of Institute of Finance in Jinan University which is the Major Base of Social Science in Guangdong＇s Universities （04jdxm79001）, the Research Program of Innovative Team of Jinan University （04sk2d03）, National Natural Science Foundation of China（60574069） and the Soft Science Foundation of Guangdong Province （2005870101044）
文摘Traditional credit evaluation models failed to produce partial results due to their ignorance of the whole risks of credit environment. An excellent evaluating model on credit should take into account the credit environment impersonally and comprehensively. In this paper, a novel area＇s macroscopical credit evaluation model based on Fuzzy Neural Network is constructed. A set of scientific and reasonable evaluating indexes are extracted from feature space of macroscopical credit, then based on these indexes a Fuzzy Neural Network （FNN） model on credit evaluation is constructed and applied into the practical credit evaluation of some Chinese provinces randomly selected for the first time. Results show our model is both practical and capable.
