The article provides a comprehensive review regarding the theoretical approaches, methodologies and empirical researches of corporate bankruptcy prediction, laying emphasis on the 30-year development history of Hungarian empirical results. In ex-socialist countries corporate bankruptcy prediction became possible more than 20 years later compared to the western countries, however, based on the historical development of corporate bankruptcy prediction after the political system change it can be argued that it has already caught up to the level of international best practice. Throughout the development history of Hungarian bankruptcy prediction, it can be tracked how the initial, small, cross-sectional sample and classic methodology-based bankruptcy prediction has evolved to today’s corporate rating systems meeting the requirements of the dynamic, through-the-cycle economic capital calculation models. Contemporary methodological development is characterized by the domination of artificial intelligence, data mining, machine learning, and hybrid modelling. On the basis of empirical results, the article draws several normative proposals how to assemble a bankruptcy prediction database and select the right classification method(s) to accomplish efficient corporate bankruptcy prediction.

In our work, we compare the predictive power of different bankruptcy prediction models built on financial indicators calculable from businesses’ accounting data on the database of the first Hungarian bankruptcy model. For modelling, we use data-mining methods often applied in bankruptcy prediction: neural networks (NN), support vector machines (SVM) and the rough set theory (RST) capable of rule-based classification. The point of departure for our comparative analysis is the practical finding that black-box-type data-mining methods typically show better classification performance than models whose results are easy to interpret, i.e. there seems to be a kind of trade-off between the interpretability and predictive power of bankruptcy models. Empirical results lead us to conclude that the RST approach can be a competitive alternative to black-box-type SVM and NN models. In our research, we did not find any major trade-off between the interpretability and predictive performance of bankruptcy models on the database of the first Hungarian bankruptcy model.

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks.

This article provides an empirical analysis aimed at evaluating the financial trends and disparities at the sector level within the Balkan and Eastern European region. The dataset encompasses a period of nine years and comprises more than 20 million firm-year observations from 24 industries in 21 countries. It uses 19 financial ratios to assess sectoral performance. In the empirical investigation, trend analysis and the two-step cluster analysis methods were used. Following the global financial crisis, a significant proportion of financial ratios exhibited favorable trends, indicating robust business and economic circumstances. Nevertheless, this trajectory was temporarily disrupted in 2020 due to the onset of the COVID-19 pandemic. By 2021, the financial ratios had reverted back to their historical patterns. Country membership, margin, liquidity, trade turnover, profitability, and leverage ratios are the most effective variables for explaining differences in sectoral performance. Sector membership is a comparatively less influential factor. Although this study effectively identified significant disparities in financial ratio profiles, it does not suggest that companies in the most developed countries in the region attain the most favorable financial performance. Stakeholders who have a vested interest in this region should carefully contemplate the ramifications of the findings from this study.

A tanulmány célja nyilvánosan hozzáférhető éves beszámoló adatok alapján az iparági hovatartozást is figyelembe vevő, magas besorolási pontosságú csődelőrejelzési modellek elkészítése. A tanulmány a négy leggyakrabban alkalmazott csődelőrejelzési módszersegítségével, ugyanarra az adatbázisra épít csődmodelleket, elvégzi összehasonlító elemzésüket és értékeli azok megbízhatóságát.