MODERN APPROACHES TO FORECASTING FIRM DEFAULT RATES OVER THE SHORT TO MEDIUM TERM: AN APPLICATION TO A PANEL OF POLISH COMPANIES

Anton Gerunov

Yearbook of the Faculty of Economics and Business Administration, Sofia University, 2023, vol. 22, issue 1, 5-15

Abstract: This paper models Polish companies default rates over the period 2000 to 2013, using eight leading classification algorithms – logistic regression, linear discriminant analysis, neural network, k-Nearest neighbours, naive Bayes classifier, random forest, support vector machine and soft independent modelling of class analogies. Each of the eight methods is applied to five subsamples of data to forecast default rates in a period of one to five years, using 64 financial indicators. Results show that longer term forecasting remains a challenge for all methods, but a random forest algorithm is able to produce satisfactory results for forecast horizons of one or two years. The key drivers behind bankruptcy are then elicited using the relative contribution of variables to the accuracy of the best performing model.

Keywords: forecasting; default rate; bankruptcy; machine learning. (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.feba.uni-sofia.bg/sko/yrbook/Yearbook22-01.pdf (application/pdf)

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:sko:yrbook:v:22:y:2023:i:1:p:5-15

Access Statistics for this article

More articles in Yearbook of the Faculty of Economics and Business Administration, Sofia University from Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria Contact information at EDIRC.
Bibliographic data for series maintained by Prof. Teodor Sedlarski ().