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Linear versus nonlinear dimensionality reduction for banks’ credit rating prediction
9 references·3 citing·46 second hop
Linear versus nonlinear dimensionality reduction for banks’ credit rating prediction (2013)Linear versus nonlinear dimensionality re…Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry (2012)Hybrid models based on rough …A logical analysis of banks’ financial strength ratings (2012)A logical analysis of banks’ …A note comparing support vector machines and ordered choice models’ predictions of international banks’ ratings (2011)A note comparing support vect…The use of hybrid manifold learning and support vector machines in the prediction of business failure (2010)The use of hybrid manifold le…Assessing bank soundness with classification techniques (2009)Assessing bank soundness with…Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review (2006)Bankruptcy prediction in bank…The role of non-financial factors in internal credit ratings (2004)The role of non-financial fac…Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)Credit rating analysis with s…A multivariate analysis of the determinants of Moody’s bank financial strength ratings (1999)A multivariate analysis of th…Real-time inflation forecasting using non-linear dimension reduction techniques (2022)Real-time inflation forecasti…Predicting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems—A cross-region analysis (2017)Predicting corporate investme…Dimensionality and data reduction in telecom churn prediction (2014)Dimensionality and data reduc…When is a correlation matrix appropriate for factor analysis? Some decision rules. (1974)Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)Diversification, Size, and Risk at Bank Holding Companies (1997)Balancing accuracy, complexity and interpretability in consumer credit decision making: A C-TOPSIS classification approach (2013)Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach (2015)A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria (2022)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Bank regulation and supervision: what works best? 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(2022)Financial Intermediation and Delegated Monitoring (1984)Credit Line Usage, Checking Account Activity, and Default Risk of Bank Borrowers (2010)The value of non-financial information in SME risk management (2010)The Effects of Corporate and Country Sustainability Characteristics on The Cost of Debt: An International Investigation (2016)
this work references cited by second hop

References

  • 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review1,068 cited
  • 2003Credit rating analysis with support vector machines and neural networks: a market comparative study1,017 cited
  • 2004The role of non-financial factors in internal credit ratings438 cited
  • 1999A multivariate analysis of the determinants of Moody’s bank financial strength ratings108 cited
  • 2009Assessing bank soundness with classification techniques91 cited
  • 2010The use of hybrid manifold learning and support vector machines in the prediction of business failure83 cited
  • 2012Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry79 cited
  • 2011A note comparing support vector machines and ordered choice models’ predictions of international banks’ ratings58 cited
  • 2012A logical analysis of banks’ financial strength ratings49 cited

Cited by

  • 2022Real-time inflation forecasting using non-linear dimension reduction techniques42 cited
  • 2017Predicting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems—A cross-region analysis27 cited
  • 2014Dimensionality and data reduction in telecom churn prediction14 cited

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