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Logit and Probit application for the prediction of bankruptcy in Slovak companies

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Kovacova M., Kliestik T.
Equilibrium. Quarterly Journal of Economics and Economic Policy
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2017
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vol. 12
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issue 4
775-791
EN
Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.
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Effect of Non-Investment Measures upon the Stability of Agricultural Enterprises in the South Bohemian Region

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Jarmila S., Pártlová P., Jan V.
Littera Scripta
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2016
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issue 2
123-139
EN
The contribution deals with parametrization of multifunctional potential of enterprises with non-investment measures, including basic subsidy programs applicable in the programming period 2007 – 2013, using a set of agricultural enterprises in the South Bohemian region. Models of non-investment measures have been proposed, including basic subsidy programs, which have been, due to the expected increase of subsidies in 2014 – 2020, increased by coefficients in the interval 1.1 – 1.3. At the same time, we applied degression of the payments in three variants and based on the size category of the agricultural enterprise. The proposed models of non-investment measures investigated changes in solvency or financial health of the agricultural enterprises in 3 production categories typical for the South Bohemian region (mountain type, potato-oats type, potato type).
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Discriminatory Power of the Altman Z-Score Model

100%
Kateřina M.
Littera Scripta
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2013
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vol. 1
95-106
EN
This article aims to assess the discriminatory power of one of the most famous and most discussed corporate predictive models, the Altman ZScore. This model ranks among the bankruptcy models, whose main purpose is to detect the impending bankruptcy in good time. The research focuses on three main areas of assessing the discriminatory power of the model. The first part deals with the overall discriminatory power of the model; the second part is aimed at quantifying the impact of individual variables on misclassification of enterprises in bankruptcy. The last part quantifies the discriminatory power of individual variables of the model. The results are compared with the findings of the author of the model. The empirical research is based on the accounting data of Czech companies from the manufacturing industry. Both thriving and bankrupt companies are included in the research.
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