Modelling bankruptcy prediction models in Slovak companies
An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regressi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2017-01-01
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| Series: | SHS Web of Conferences |
| Subjects: | |
| Online Access: | https://doi.org/10.1051/shsconf/20173901013 |
| _version_ | 1818650673236934656 |
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| author | Kovacova Maria Kliestikova Jana |
| author_facet | Kovacova Maria Kliestikova Jana |
| author_sort | Kovacova Maria |
| collection | DOAJ |
| description | An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks), there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models. |
| first_indexed | 2024-12-17T01:53:57Z |
| format | Article |
| id | doaj.art-bab00052d9db458f823d219615e0074b |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-12-17T01:53:57Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-bab00052d9db458f823d219615e0074b2022-12-21T22:07:59ZengEDP SciencesSHS Web of Conferences2261-24242017-01-01390101310.1051/shsconf/20173901013shsconf_ies2017_01013Modelling bankruptcy prediction models in Slovak companiesKovacova MariaKliestikova JanaAn intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks), there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.https://doi.org/10.1051/shsconf/20173901013bankruptcyprediction modelscompany |
| spellingShingle | Kovacova Maria Kliestikova Jana Modelling bankruptcy prediction models in Slovak companies SHS Web of Conferences bankruptcy prediction models company |
| title | Modelling bankruptcy prediction models in Slovak companies |
| title_full | Modelling bankruptcy prediction models in Slovak companies |
| title_fullStr | Modelling bankruptcy prediction models in Slovak companies |
| title_full_unstemmed | Modelling bankruptcy prediction models in Slovak companies |
| title_short | Modelling bankruptcy prediction models in Slovak companies |
| title_sort | modelling bankruptcy prediction models in slovak companies |
| topic | bankruptcy prediction models company |
| url | https://doi.org/10.1051/shsconf/20173901013 |
| work_keys_str_mv | AT kovacovamaria modellingbankruptcypredictionmodelsinslovakcompanies AT kliestikovajana modellingbankruptcypredictionmodelsinslovakcompanies |