Work detail

Bankruptcy prediction models in the Czech economy: New specification using Bayesian model averaging and logistic regression on the latest data

Author: Mgr. Jiří Kolísko
Year: 2017 - summer
Leaders: PhDr. Michael Princ
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 106
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/174058/
Abstract: The main objective of our research was to develop a new bankruptcy prediction model for the
Czech economy. For that purpose we used the logistic regression and 150,000 financial
statements collected for the 2002—2016 period. We defined 41 explanatory variables (25
financial ratios and 16 dummy variables) and used Bayesian model averaging to select the best
set of explanatory variables. The resulting model has been estimated for three prediction
horizons: one, two, and three years before bankruptcy, so that we could assess the changes in
the importance of explanatory variables and models’ prediction accuracy. To deal with high
skew in our dataset due to small number of bankrupt firms, we applied over- and undersampling
methods on the train sample (80% of data). These methods proved to enhance our
classifier’s accuracy for all specifications and periods. The accuracy of our models has been
evaluated by Receiver operating characteristics curves, Sensitivity-Specificity curves, and
Precision-Recall curves. In comparison with models examined on similar data, our model
performed very well. In addition, we have selected the most powerful predictors for short- and
long-term horizons, which is potentially of high relevance for practice.
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