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Empirical Study on Indicators Selection Model Based on Nonparametric -Nearest Neighbor Identification and R Clustering Analysis

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  • Yan Liu
  • Zhan-jiang Li
  • Xue-jun Zhen

Abstract

The combination of the nonparametric -nearest neighbor discriminant method and R cluster analysis is used to construct a double-combination index screening model. The characteristics of the article are as follows: firstly, the nonparametric -nearest neighbor discriminant method is used to select the indicators which have significant ability to discriminate the default loss rate, which makes up the shortcomings of the previous research that only focuses on the indicators with significant ability to discriminate default state. Additionally, the R cluster analysis applied in this paper sorts the indicators by criterion class, rather than sorting the indicator by the whole index system. This approach ensures that indicators which are clustered in one class have the same economic implications and data characteristics. This approach avoids the situation where indicators that are clustered in one class only have the same data characteristics but have different economic implications.

Suggested Citation

  • Yan Liu & Zhan-jiang Li & Xue-jun Zhen, 2018. "Empirical Study on Indicators Selection Model Based on Nonparametric -Nearest Neighbor Identification and R Clustering Analysis," Complexity, Hindawi, vol. 2018, pages 1-9, April.
    • Handle: RePEc:hin:complx:2067065
    DOI: 10.1155/2018/2067065
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    1. Reusens, Peter & Croux, Christophe, 2017. "Sovereign credit rating determinants: A comparison before and after the European debt crisis," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 108-121.
    2. Siavash Fakhimi Derakhshan & Alireza Fatehi, 2015. "Non-monotonic robust H fuzzy observer-based control for discrete time nonlinear systems with parametric uncertainties," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(12), pages 2134-2149, September.
    3. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    4. Katarzyna Bijak & Lyn C Thomas, 2015. "Modelling LGD for unsecured retail loans using Bayesian methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 342-352, February.
    5. Doumpos, Michael & Niklis, Dimitrios & Zopounidis, Constantin & Andriosopoulos, Kostas, 2015. "Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 599-607.
    6. Vivake Anand & Kamran Ahmed Soomro & Suneel Kumar Solanki, 2016. "Determinants of Credit Rating and Optimal Capital Structure among Pakistani Banks," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 19(60), pages 169-182, June.
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