A Parametric or Nonparametric Approach for Creating a new Bankruptcy Prediction Model: The Evidence from the Czech Republic
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F14%3APU109289" target="_blank" >RIV/00216305:26510/14:PU109289 - isvavai.cz</a>
Výsledek na webu
—
DOI - Digital Object Identifier
—
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Parametric or Nonparametric Approach for Creating a new Bankruptcy Prediction Model: The Evidence from the Czech Republic
Popis výsledku v původním jazyce
For many years now the development of models capable of predicting company bankruptcy has aimed at increasing their accuracy. Among the decisive factors determining the accuracy of the bankruptcy model have been the choice of variable models and applied classification algorithms. The prevailing opinion in literature is that the accuracy of bankruptcy models cannot be appreciably improved by the choice of classification algorithm. A reflection of this assertion is the frequent usage of parametric methods. In particular this involves the method of linear discrimination analysis. This method formed the basis of the first bankruptcy model and continues to be the most frequently applied classification algorithm. However, it requires the fulfilment of assumptions which financial data does not provide and therefore limits the improvement of the models predictive capabilities. This led the authors to the idea of testing the possibility of improving the bankruptcy models predictive capabilities by using non-traditional approaches. Using the example of companies from the Czech Republic it was discovered that a nonparametric method, when used for the selection of model variables as well as the actual classification, can yield significantly better results than the traditional parametric approach.
Název v anglickém jazyce
A Parametric or Nonparametric Approach for Creating a new Bankruptcy Prediction Model: The Evidence from the Czech Republic
Popis výsledku anglicky
For many years now the development of models capable of predicting company bankruptcy has aimed at increasing their accuracy. Among the decisive factors determining the accuracy of the bankruptcy model have been the choice of variable models and applied classification algorithms. The prevailing opinion in literature is that the accuracy of bankruptcy models cannot be appreciably improved by the choice of classification algorithm. A reflection of this assertion is the frequent usage of parametric methods. In particular this involves the method of linear discrimination analysis. This method formed the basis of the first bankruptcy model and continues to be the most frequently applied classification algorithm. However, it requires the fulfilment of assumptions which financial data does not provide and therefore limits the improvement of the models predictive capabilities. This led the authors to the idea of testing the possibility of improving the bankruptcy models predictive capabilities by using non-traditional approaches. Using the example of companies from the Czech Republic it was discovered that a nonparametric method, when used for the selection of model variables as well as the actual classification, can yield significantly better results than the traditional parametric approach.
Klasifikace
Druh
J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS
CEP obor
—
OECD FORD obor
50602 - Public administration
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2014
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
INTERNATIONAL JOURNAL of MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES
ISSN
1998-0140
e-ISSN
—
Svazek periodika
8
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
10
Strana od-do
214-223
Kód UT WoS článku
—
EID výsledku v databázi Scopus
2-s2.0-84902459281