Random Survival Forests Models for SME Credit Risk Measurement

Methodology and Computing in Applied Probability, Forthcoming

23 Pages Posted: 31 Jan 2009

See all articles by Silvia Figini

Silvia Figini

University of Pavia

Dean Fantazzini

Moscow School of Economics, Moscow State University; National Research University Higher School of Economics

Date Written: January 31, 2009

Abstract

This paper extends the existing literature on empirical research in the field of credit risk default for Small Medium Enterprizes (SMEs). We propose a non-parametric approach based on Random Survival Forests (RSF) and we compare its performance with a standard logit model. To the authors' knowledge, no studies in the area of credit risk default for SMEs have used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance against one another. As for the in-sample results, we find that our non-parametric model performs much better that the classical logit model. As for the out-of-sample performances, the evidence is just the opposite, and the logit performs better than the RSF model. We explain this evidence by showing how error in the estimates of default probabilities can affect classification error when the estimates are used in a classification rule.

Keywords: Random Survival Trees, Credit risk, Default probability, Loss functions, Classification

JEL Classification: G17, G32

Suggested Citation

Figini, Silvia and Fantazzini, Dean, Random Survival Forests Models for SME Credit Risk Measurement (January 31, 2009). Methodology and Computing in Applied Probability, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1335856

Silvia Figini

University of Pavia ( email )

Corso Strada Nuova, 65
27100 Pavia, 27100
Italy

Dean Fantazzini (Contact Author)

Moscow School of Economics, Moscow State University ( email )

GSP-2, Leninskie Gory
Moscow, 119992
Russia
+7 495 5105256 (Phone)
+7 495 5105267 (Fax)

HOME PAGE: https://sites.google.com/site/deanfantazzini/

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

HOME PAGE: http://www.hse.ru/org/persons/11532644

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