A comparison study of computational methods of Kolmogorov–Smirnov statistic in credit scoring. Issue 10 (26th November 2017)

• Record Type: Journal Article
• Authors: Zeng, Guoping
• Is Part Of: Communications in statistics. Volume 46:Issue 10(2017)
• Page Start: 7744
• Page End: 7760
• Publication Date: 2017-11-26
• Subjects: Credit scoring -- Equal-width binning -- Equal-size binning -- Kolmogorov–Smirnov statistic -- Rank ordering -- Lift table; Primary 62-07 -- Secondary 62G08; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1249883
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Kolmogorov–Smirnov statistic (KS) is a standard measure in credit scoring. Currently, there are three computational methods of KS: method with equal-width binning, method with equal-size binning and method without binning. This paper compares the three methods in three aspects: Values, Rank Ordering of Scores and Geometrical Way. The computational results on the German Credit Data show that only the method without binning can produce a unique value of KS. It is further proved analytically that the method without binning yields the maximum value of KS among the three methods. The computational results also show that only the method with equal-size binning can be used to evaluate rank ordering of scores. Moreover, it is proved that all the three methods can be used to calculate KS in a geometric way.