Learning Machines Supporting Bankruptcy Prediction

SFB 649 Discussion Paper 2010-032

28 Pages Posted: 9 Jan 2017

See all articles by Wolfgang Karl Härdle

Wolfgang Karl Härdle

Blockchain Research Center Humboldt-Universität zu Berlin; Charles University; National Yang Ming Chiao Tung University; Asian Competitiveness Institute

Rouslan Moro

German Institute for Economic Research (DIW Berlin)

Linda Hoffman

Humboldt University of Berlin

Date Written: June 7, 2010

Abstract

In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification rule called a score or an SVM. Companies with scores above zero belong to one group and the rest to another group. Estimation of the probability of default (PD) values can be calculated from the scores provided by an SVM. The transformation used in this paper is a combination of weighting ranks and of smoothing the results using the PAV algorithm. The conversion is then monotone. This discussion paper is based on the Credit reform database from 1997 to 2002. The indicator variables were converted to financial ratios; it transpired out that eight of the 25 were useful for the training of the SVM. The results showed that those ratios belong to activity, profitability, liquidity and leverage. Finally, we conclude that SVMs are capable of extracting the necessary information from financial balance sheets and then to predict the future solvency or insolvent of a company. Banks in particular will benefit from these results by allowing them to be more aware of their risk when lending money.

Keywords: Support Vector Machine, Bankruptcy, Default Probabilities Prediction, Profitability

JEL Classification: C14, G33, C45

Suggested Citation

Härdle, Wolfgang Karl and Moro, Rouslan and Hoffman, Linda, Learning Machines Supporting Bankruptcy Prediction (June 7, 2010). SFB 649 Discussion Paper 2010-032, Available at SSRN: https://ssrn.com/abstract=2894236 or http://dx.doi.org/10.2139/ssrn.2894236

Wolfgang Karl Härdle (Contact Author)

Blockchain Research Center Humboldt-Universität zu Berlin ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Charles University ( email )

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

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Asian Competitiveness Institute ( email )

Singapore

Rouslan Moro

German Institute for Economic Research (DIW Berlin)

Mohrenstraße 58
Berlin, 10117
Germany

Linda Hoffman

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

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