Comparative analysis of methods for forecasting bankruptcies of Russian construction companies
| Autor: | Alexander M. Karminsky, Roman N. Burekhin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science media_common.quotation_subject 05 social sciences Logit 0211 other engineering and technologies 02 engineering and technology General Medicine Logistic regression Random forest Bankruptcy Probit model 021105 building & construction 0502 economics and business Econometrics Predictive power Quality (business) 050203 business & management media_common |
| Zdroj: | Business Informatics. 13:52-66 |
| ISSN: | 2587-8166 1998-0663 |
| DOI: | 10.17323/1998-0663.2019.3.52.66 |
| Popis: | This paper is devoted to comparison of the capabilities of various methods to predict the bankruptcy of construction industry companies on a one-year horizon. The authors considered the following algorithms: logit and probit models, classification trees, random forests, artificial neural networks. Special attention was paid to the peculiarities of the training machine learning models, the impact of data imbalance on the predictive ability of models, analysis of ways to deal with these imbalances and analysis of the influence of non-financial factors on the predictive ability of models. In their study, the authors used non-financial and financial indicators calculated on the basis of public financial statements of the construction companies for the period from 2011 to 2017. The authors concluded that the models considered show acceptable quality for use in forecasting bankruptcy problems. The Gini or AUC coefficient (area under the ROC curve) was used as the quality markers of the model. It was revealed that neural networks outperform other methods in predictive power, while logistic regression models in combination with discretization follow them closely. It was found that the effective way to deal with the imbalance data depends on the type of model used. However, no significant impact on the imbalance in the training set predictive ability of the model was identified. The significant impact of non-financial indicators on the likelihood of bankruptcy was not confirmed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|