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A credit rating model of microfinance based on fuzzy cluster analysis and fuzzy pattern recognition: Empirical evidence from Chinese 2,157 small private businesses
9 references·1 citing·44 second hop
A credit rating model of microfinance based on fuzzy cluster analysis and fuzzy pattern recognition: Empirical evidence from Chinese 2,157 small private businesses (2016)A credit rating model of microfinance bas…Technology credit scoring model with fuzzy logistic regression (2016)Technology credit scoring mod…Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions (2013)Quantitative credit risk asse…Credit scoring models for the microfinance industry using neural networks: Evidence from Peru (2012)Credit scoring models for the…A hybrid KMV model, random forests and rough set theory approach for credit rating (2012)A hybrid KMV model, random fo…Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach (2011)Credit rating analysis using …Predicting issuer credit ratings using a semiparametric method (2009)Predicting issuer credit rati…Modelling of insurers’ rating determinants. An application of machine learning techniques and statistical models (2006)Modelling of insurers’ rating…ZETATM analysis A new model to identify bankruptcy risk of corporations (1977)ZETATM analysis A new model t…FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)FINANCIAL RATIOS, DISCRIMINAN…A credit rating model based on a customer number bell-shaped distribution (2018)A credit rating model based o…Experiments in induction (1966)Experiments in Induction (1967)The Declining Credit Quality of U.S. Corporate Debt: Myth or Reality? (1998)A logical analysis of banks’ financial strength ratings (2012)Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective (2016)Credit rating and microfinance lending decisions based on loss given default (LGD) (2019)Differentiating between good credits and bad credits using neuro-fuzzy systems (2002)Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Application of support vector machines to corporate credit rating prediction (2006)Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations (2013)Anticipating Corporate Financial Performance from CEO Letters Utilizing Sentiment Analysis (2020)Machine learning in corporate credit rating assessment using the expanded audit report (2022)Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Neural network credit scoring models (2000)An insight into the experimental design for credit risk and corporate bankruptcy prediction systems (2014)Credit risk assessment for unbalanced datasets based on data mining, artificial neural network and support vector machines (2018)Monitoring and Early Warning of SMEs’ Shutdown Risk under the Impact of Global Pandemic Shock (2023)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Forecasting with artificial neural networks: (1998)Financial time series forecasting using support vector machines (2003)Credit risk assessment model for Jordanian commercial banks: Neural scoring approach (2014)Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach (2015)Machine Learning at Central Banks (2017)The Pricing of Options and Corporate Liabilities (1973)ON THE PRICING OF CORPORATE DEBT: THE RISK STRUCTURE OF INTEREST RATES* (1974)Default Risk in Equity Returns (2004)Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach (2015)Bankruptcy prediction using Partial Least Squares Logistic Regression (2017)A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method (2018)Forecasting Bankruptcy More Accurately: A Simple Hazard Model (2001)A semiparametric method for predicting bankruptcy (2007)The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences (2015)Modelling market implied ratings using LASSO variable selection techniques (2018)The Five Stages of Small Business Growth (2002)Benchmarking state-of-the-art classification algorithms for credit scoring (2003)New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers (2020)Credit scoring based on tree-enhanced gradient boosting decision trees (2021)A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain (2022)Credit Scoring and Its Applications (2002)Modelling Credit Risk for SMEs: Evidence from the U.S. Market (2007)The Credit Ratings Game (2009)Methodological Issues Related to the Estimation of Financial Distress Prediction Models (1984)Managerial incentives and risk-taking☆ (2005)How does financial reporting quality relate to investment efficiency? (2009)
this work references cited by second hop

References

  • 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY14,869 cited
  • 1977ZETATM analysis A new model to identify bankruptcy risk of corporations1,812 cited
  • 2012Credit scoring models for the microfinance industry using neural networks: Evidence from Peru210 cited
  • 2016Technology credit scoring model with fuzzy logistic regression155 cited
  • 2012A hybrid KMV model, random forests and rough set theory approach for credit rating111 cited
  • 2013Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions83 cited
  • 2009Predicting issuer credit ratings using a semiparametric method60 cited
  • 2006Modelling of insurers’ rating determinants. An application of machine learning techniques and statistical models51 cited
  • 2011Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach25 cited

Cited by

  • 2018A credit rating model based on a customer number bell-shaped distribution19 cited

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