Bankruptcy Prediction Models: Can the prediction power of the models be improved by using dynamic indicators?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F14%3APU110422" target="_blank" >RIV/00216305:26510/14:PU110422 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2212567114003803" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2212567114003803</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/S2212-5671(14)00380-3" target="_blank" >10.1016/S2212-5671(14)00380-3</a>

Result language

angličtina

Original language name

Bankruptcy Prediction Models: Can the prediction power of the models be improved by using dynamic indicators?

Original language description

The present approach to developing bankruptcy prediction models uses financial ratios related to the time of one year before bankruptcy. Some authors try to improve the prediction accuracy of the models by using averaged ratios involving several years before bankruptcy. This of course assumes that a bankruptcy can be predicted several years ahead. This idea led us to investigating the differences between the dynamics of the financial ratios developments. Here we assume that the dynamics of the values of some indicators in a group of prospering companies may be different from that of those facing bankruptcy threats. The indicators that showed a significant difference in the development dynamics were used to develop a bankruptcy prediction model. The research was carried out using data of the Czech manufacturing industries obtained from the AMADEUS database for years 2002 to 2012, with each company providing data for up to five years prior to the bankruptcy. Along with investigating the different approach to the selection of indicators for the development of a bankruptcy model, we were also concerned with the selection of a method to develop it. Researching the literature, we found that the most commonly used method is one of linear discrimination analysis, whose precision is improved if applied to normally distributed data without outliers. With financial data, however, these assumptions are difficult to meet. Therefore, a non-parametric Boosted-Trees method was used to select the predictors and develop the bankruptcy models.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

50602 - Public administration

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2014

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Procedia Economics and Finance

ISBN

—

ISSN

2212-5671

e-ISSN

—

Number of pages

9

Pages from-to

565-574

Publisher name

Elsevier

Place of publication

Neuveden

Event location

Brno

Event date

Mar 6, 2014

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

000345439100067