Predicting Bankruptcy with Support Vector Machines

SFB 649 Discussion Paper 2005-009

25 Pages Posted: 9 Jan 2017

See all articles by Wolfgang K. Härdle

Wolfgang K. Härdle

Humboldt University of Berlin - Institute for Statistics and Econometrics; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Rouslan Moro

German Institute for Economic Research (DIW Berlin)

Dorothea Schaefer

German Institute for Economic Research (DIW Berlin); JIBS

Date Written: March 1, 2005

Abstract

The purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.

JEL Classification: C40, G10

Suggested Citation

Härdle, Wolfgang K. and Moro, Rouslan and Schaefer, Dorothea, Predicting Bankruptcy with Support Vector Machines (March 1, 2005). SFB 649 Discussion Paper 2005-009, Available at SSRN: https://ssrn.com/abstract=2894426 or http://dx.doi.org/10.2139/ssrn.2894426

Wolfgang K. Härdle (Contact Author)

Humboldt University of Berlin - Institute for Statistics and Econometrics ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Rouslan Moro

German Institute for Economic Research (DIW Berlin)

Mohrenstraße 58
Berlin, 10117
Germany

Dorothea Schaefer

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstraße 58
Berlin, 10117
Germany
+49 30 8978 9162 (Phone)
+49 30 8978 9104 (Fax)

JIBS ( email )

Jönköping, 55111
Sweden

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
41
Abstract Views
461
PlumX Metrics