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The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.”

In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes.

Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes.

The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its weaker prediction capacity. This avoids the mistake of eliminating an index that has strong ability to distinguish default and non-default samples. The greater the deviation of the default sample’s mean from the whole sample’s mean, the greater is the capability of the index to distinguish the default state. According to this rule, the authors assign a larger weight to the index that exhibits the ability to identify the default state. This is different from the existing index system, which does not take into account the ability to identify the default state.

The purpose of this paper is to explore the important role of supply chain risk management (SCRM) capabilities as pre-factors for SMEs to improve supply chain financing performance (SCFP), also incorporating the effect of supply chain integration (SCI).

From the intersection of SCRM and SCF literature, this paper proposed hypothesis to discuss the impact of SCRM capabilities on SCFP and the role of SCI, aiming at combine SCRM with supply chain financing management. The research model was validated applying structural equation modeling on survey data from 286 Chinese small and medium-sized enterprises (SMEs).

Four dimensions of SCRM capabilities have significant positive effects on SCFP with different significant levels, confirming that they are important pre-factors in supply chain finance (SCF). In addition, the impact of SCRM capabilities on SCFP differ when SCI varies, indicating the promoting effect of SCI.

SMEs should establish SCRM capabilities as supply chain risks greatly influence the evaluation of financial providers and the achievement of SCF. Meanwhile, SCI should be attached for it enables superior SCFP even if SCRM capabilities are relatively limited.

This study represents a pioneering attempt to analyze the pre-factors of SMEs in improving SCFP by combing SCRM with SCF management. Few prior studies have highlighted the importance of SCRM in SCF.

The purpose of this paper is to apply duration methods to a sample of Farm Service Agency (FSA) direct, seven‐year operating loans to identify those variables that influence the time to loan termination and type of termination. Variables include both those known at time of loan origination and those that characterize the changing economic environment over the life of the loan. Also, to examine the impact of various FSA programs promoting policy objectives.

A systematic sample of 877 seven‐year, FSA direct loans originated between October 1, 1993 and September 30, 1996 was collected. Cox regression, competing risks models are estimated as a function of borrower and loan characteristics observable at loan origination. Economic indicator variables emphasizing the farm economy and observed quarterly over the life of the loan are also included as explanatory variables.

Loan characteristics, borrower financial characteristics and degree of borrower interaction with FSA observable at origin are significant variables in determining type of loan outcome (default or paid‐in‐full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant.

The sample consists only of FSA direct loans which implies borrowers are at financial margin. Application of method to agricultural loans from conventional commercial lenders could identify different significant factors.

Using length of time to loan termination instead of just type of outcome provides for a richer analysis of loan performance. Loan performance over time is influenced by the larger economy and should be incorporated into loan performance modeling.

The study described in the paper demonstrates use of competing risks models on intermediate agricultural loans and develops how this technique can be used to learn about dynamic aspects of loan performance. Sample consists of observations on individual FSA direct loan borrowers. The FSA direct loan program is the major source of credit for agricultural borrowers at the financial margin.

This paper aims to establish a systematic cognition to alleviate the supply–demand contradiction in rural financial markets from an integrated perspective of knowledge management and proposes the concept of rural financial knowledge ecosystem (RFKE) to encourage multifaceted solutions.

The authors qualitatively describe the process that the knowledge management dilemmas cause the supply–demand contradiction in the rural finance and further summarize a systematic methodology from three dimensions: the knowledge subject, the knowledge environment and the knowledge ecology.

The authors list four types of knowledge management dilemmas leading to the supply–demand contradiction in the rural finance, i.e. the weak knowledge sharing, the poor knowledge flow, the slow knowledge updating and the imperfect knowledge environment. Meanwhile, the RFKE model consisting of the ecological subject, the ecological environment and the ecological regulation is also presented.

The role of knowledge management in improving the allocation of financial resources to various rural financial market participants (government, rural financial institutions, farmers, agricultural enterprises, etc.).

The authors creatively give the RFKE model, which complements and enriches the theory of knowledge management. Meanwhile, relevant management practices are urgently needed under the macro circumstance of the COVID-19 pandemic and the rural revitalization in China.

This paper aims to explore the drivers of banking stability in the case of QISMUT+3 countries (Qatar, Indonesia, Saudi Arabia, Malaysia, United Arab Emirates, Turkey, Pakistan, Kuwait and Bahrain) focusing on social and governmental responsibility (SGR) determinants. Both main indicators of banking stability, namely, profitability and nonperforming loans, were treated as dependent variables. The model is examined with the whole sample and separately by examining commercial banks and Islamic banks.

Cross-country bank-level panel data spanning from 2011 to 2018 is used. Two-step system generalized methods of moments alongside both panel-corrected standard error and feasible generalized least squares models were applied to ensure the robustness of the results.

Findings reveal that capital adequacy and corruption control are the most dominant determinants of banking profitability in the studied sample regardless of the type of the bank. In addition, profitability, efficient management, inflation and government effectiveness were found to be the main drivers of financial vulnerability risk.

Findings of this study offer many insights and policy implications to help stakeholders gain a comprehensive understanding of banking stability. Suggested policy implications targeting bank management, governmental policymakers and investors are offered to better the banking stability of QISMUT+3 countries.

This paper has multiple contributions to the existing literature. The determinants of banking stability are examined in QISMUT+3 group of countries which is the focus of a limited number of studies. In addition, the use of a comprehensive variable set alongside the addition of SGR determinants in the case of banking system stability is one of the main contributions of this paper.

The purpose of this paper is to examine changes in the structures of US farms and lenders and identify prospective implications for federal credit.

Data from US farm operations for 1996-2014 were adjusted to 2014 values using commodity price indices. Farm size groups were constructed by value of farm production to analyze changes in farm numbers, production, assets, debt, leverage, liquidity, profitability, land tenure, commodity type, contract production, organization type, and use of Farm Service Agency (FSA) direct and guaranteed loans by farm size. Bank, Farm Credit System (FCS), and FSA data from 1996 to 2015 were adjusted to 2014 values. Lender size groups were constructed to analyze changes in bank and association numbers, farm loans, and use of FSA guaranteed loans by lender size.

The greatest consolidation has been by farms with over $2 million in production. More farm debt is held by large, complex organizations, frequently with multiple operators, more variable income, and greater reliance on production contracts and operating and nonreal estate credit. Large farms have greater leverage, are more profitable, and have a larger share of household income from the farm. Banks and FCS institutions are fewer and larger, yet smaller institutions use FSA guarantees to a greater extent. Larger farms tend to be more reliant on both direct and guaranteed FSA loans and are likely to become more dependent on FSA credit.

Changing farm and lender structure together with softening farm income may require FSA farm loan program changes to meet any increase in loan demand. Policy alternatives are provided to meet changing demand for farm credit.

This study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.

The authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.

The results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.

The originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.

This study investigates important factors that should be used by lenders in risk‐rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.

The purpose of this paper is to investigate the performance of the revolving fund (RF) regarding the ability of smallholder debtors to retrieve land title deeds, and also to examine the factors influencing the outstanding debts and percentage of outstanding interest of the smallholders in the Central and Northeastern regions of Thailand.

Primary data were collected from 430 debtors in the Central and Northeastern regions of Thailand in order to compare the differences in livelihood assets as well as their opinions on benefits derived from the operation of the RF. Secondary data were also collected from the RF administration, in order to evaluate the effectiveness and efficiency of the fund. Heteroskedasticity-corrected ordinary least squares and Tobit regression models were employed to examine the factors influencing the outstanding debts and percentage of outstanding interest of the smallholders, respectively. Furthermore, the student’s t-test was used to examine the differences in the livelihood assets among debtors in the two regions; and one-way analysis of variance (ANOVA) was used to examine differences in livelihood indicator scores among the three types of debtors.

The empirical results revealed that the RF is effective as the fund could provide loan to smallholders to enable them redeem their land title deeds from their previous creditors. The t-test results reveal significant differences in the livelihood assets among debtors in the two regions. One-way ANOVA indicates differences in livelihood indicator scores among the three types of debtors. The results of the heteroskedasticity-corrected ordinary least squares regression revealed that being married, low frequency of floods and less influence of third parties significantly reduced the outstanding debts. The results of the censored Tobit regression revealed that increased frequency of meeting with the RF administration, less influence of third parties, high land potential and interaction of age and experience significantly decreased the percentage of outstanding interest.

It is imperative to intensify information and education regarding the regulations, payment terms and modalities to clients in order to facilitate repayments of the loans disbursed. The organization of the RF should pay particular attention to the role of the committees involved, information administration and loan repayment monitoring. The RF should increase the frequency of meetings with smallholders, minimize the influence of third parties and give priority to old and experienced smallholders who possess land with high potential for earning incomes to enable them repay the loans.

To the best of the authors’ knowledge, this is the first study that examined the effectiveness of the RF to enable smallholders retrieve their land title deeds.

The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.

The current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms.

The results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk.

The findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers.

To the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.