Learning machines supporting bankruptcy prediction

Wolfgang Karl Härdle, Linda Hoffmann and Rouslan Moro
Additional contact information
Wolfgang Karl Härdle: Humboldt Universität zu Berlin and National Central University, Center for Applied Statistics and Economics
Linda Hoffmann: Humboldt Universität zu Berlin, Center for Applied Statistics and Economics
Rouslan Moro: Brunel University

Chapter 7 in Statistical Tools for Finance and Insurance, 2011, pp 225-250 from Springer

Abstract: Abstract This work presents one of the more recent and efficient learning systems – support vector machines (SVMs). SVMs are mainly used to classify various specialized categories such as object recognition (Schölkopf (1997)), optical character recognition (Vapnik (1995)), electric load prediction (Eunite (2001)), management fraud detection (Rätsch and Müller (2004)), and early medical diagnostics. It is also used to predict the solvency or insolvency of companies or banks, which is the focus of this work. In other words, SVMs are capable of extracting useful information from financial data and then label companies by giving them score values. Furthermore, probability of default (PD) values for companies can be calculated from those score values. The method is explained later.

Keywords: Support Vector Machine; Optical Character Recognition; High Dimensional Feature Space; Rate Class; Account Research (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-18062-0_7

Ordering information: This item can be ordered from
http://www.springer.com/9783642180620

DOI: 10.1007/978-3-642-18062-0_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().