Company rating with support vector machines

IF 1.3 Q2 STATISTICS & PROBABILITY

Statistics & Risk Modeling Pub Date : 2017-01-01 DOI:10.1515/strm-2012-1141

Russ A. Moro, W. Härdle, Dorothea Schäfer

引用次数: 1

Abstract

Abstract This paper proposes a rating methodology that is based on a non-linear classification method, a support vector machine, and a non-parametric isotonic regression for mapping rating scores into probabilities of default. We also propose a four data set model validation and training procedure that is more appropriate for credit rating data commonly characterised with cyclicality and panel features. Tests on representative data covering fifteen years of quarterly accounts and default events for 10,000 US listed companies confirm superiority of non-linear PD estimation. Our methodology demonstrates the ability to identify companies of diverse credit quality from Aaa to Caa–C.

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用支持向量机对公司进行评级

摘要本文提出了一种基于非线性分类方法、支持向量机和非参数等压回归的评级方法，用于将评级分数映射到违约概率。我们还提出了一个更适合具有周期性和面板特征的信用评级数据的四数据集模型验证和训练程序。通过对1万家美国上市公司15年季度账目和违约事件的代表性数据进行测试，证实了非线性PD估计的优越性。我们的方法证明了识别从Aaa到Caa-C不同信用质量公司的能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics & Risk Modeling STATISTICS & PROBABILITY-

CiteScore

1.80

自引率

6.70%

发文量

期刊介绍： Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.