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2020 | nr 1 (44) | 5--17
Tytuł artykułu

Study of the Classification Accuracy Measures for Predicting Corporate Bankruptcy Taking Into Account Changes in the Economic Environment

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Many types of methods for predicting corporate bankruptcy have been formulated by business theory and practice. Among them, an extensive group is composed of classification methods, which can divide companies into two groups: bankrupt and financially sound companies. The aim of the paper is to present the outcomes of the comparative analysis of classification accuracy for selected kinds of corporate bankruptcy prediction methods. While building the models, both the financial ratios of companies and the variables which reflect changes in the economic environment were taken into account. The analysis is based on data concerning companies operating in the industrial processing sector in Poland. The following four types of bankruptcy prediction methods were employed: linear discriminant analysis, logistic regression, classification tree and neural network. In order to assess the classification accuracy of a model for a training set and test set, three measures were used: sensitivity, specificity and overall accuracy. The results of the conducted empirical studies confirm the hypothesis that changes in the economic environment of companies affect their financial situation and risk of bankruptcy. The indicators of economic growth, the labour market, inflation and the economic situation were useful in bankruptcy prediction of companies operating in the industrial processing sector in Poland.(original abstract)
Rocznik
Numer
Strony
5--17
Opis fizyczny
Twórcy
  • Cracow University of Economics, Poland
  • Cracow University of Economics, Poland
  • Cracow University of Economics, Poland
Bibliografia
  • Acosta-González, E., Fernández-Rodríguez, F., Ganga, H., Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data, "Computational Economics", Vol. 53(1), pp. 227-257, 2019. https://doi.org/10.1007/s10614-017-9737-x
  • Altman, E.I., Financial Ratios, Discriminant Analysis and Prediction of Corporate Ban-kruptcy, "The Journal of Finance", Vol. 23(4), pp. 589-609, 1968.
  • Baryła, M., Pawełek, B., Pociecha, J., Comparison of Classification Accuracy for Corporate Bankruptcy Prediction Models Including Changes in Economic Environment, [in:] "Conference Program and Book of Abstracts, Conference of the International Federation of Classification Societies (IFCS 2015)", 6-8 July 2015, Bologna, Italy, pp. 55-56, 2015. http://www.ub.edu/wamyc/SEIO/IFCS2015_BookOfAbstracts.pdf
  • Baryła, M., Pawełek, B., Pociecha, J., Selection of Balanced Structure Samples in Corporate Bankruptcy Prediction, [in:] Wilhelm, A., Kestler, H. (eds.) "Analysis of Large and Complex Data", Studies in Classification, Data Analysis, and Knowledge Organization, Springer International Publishing Switzerland, Cham, pp. 345-355, 2016. https://doi.org/ 10.1007/ 978-3-319-25226-1_30
  • Beaver, W.H., McNichols, M.F., Rhie, J.-W., Have financial statements become less infor-mative? Evidence from the ability of financial ratios to predict bankruptcy, "Review of Accounting Studies", Vol. 10(1), pp. 93-122, 2005. https://doi.org/10.1007/s11142-004-6341-9
  • Blanchard, O., Johnson, D.H., Macroeconomics, 6th Edition, Pearson Education, Inc., New Jersey, 2013.
  • Bonfim, D., Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics, "Journal of Banking & Finance", Vol. 33(2), pp. 281-299, 2009. https://doi.org/10.1016/j.jbankfin.2008.08.006
  • Carling, K., Jacobson, T., Lindé, J., Roszbach, K., Corporate credit risk modeling and the macroeconomy, "Journal of Banking & Finance", Vol. 31(3), pp. 845-868, 2007. https://doi.org/10.1016/j.jbankfin.2006.06.012
  • Chava, S., Jarrow, R.A., Bankruptcy Prediction with Industry Effects, "Review of Finance", Vol. 8(4), pp. 537-569, 2004. https://doi.org/10.1093/rof/8.4.537
  • Crook, J., Bellotti, T., Time varying and dynamic models for default risk in consumer loans, "Journal of the Royal Statistical Society: Series A (Statistics in Society)", Vol. 173(2), pp. 283-305, 2010. https://doi.org/10.1111/j.1467-985X.2009.00617.x
  • De Leonardis, D., Rocci, R., Assessing the Default Risk by means of a Discrete-time Survival Analysis Approach, "Applied Stochastic Models in Business and Industry", Vol. 24, pp. 291-306, 2008. https://doi.org/10.1002/asmb.705
  • De Leonardis, D., Rocci, R., Default Risk Analysis via a Discrete-time Cure Rate Model, "Applied Stochastic Models in Business and Industry", Vol. 30(5), pp. 529-543, 2014. https://doi.org/10.1002/asmb.1998
  • Hwang, R.-C., Chu, C.-K., Forecasting Forward Defaults with the Discrete-Time Hazard Model, "Journal of Forecasting", Vol. 33(2), pp. 108-123, 2014. https://doi.org/10.1002/ for.2278
  • Korol, T., Korodi, A., Predicting Bankruptcy with the Use of Macroeconomic Variables, "Journal of Economic Computation and Economic Cybernetics Studies and Research", Vol. 44(1), pp. 201-220, 2010.
  • Nouri, B.A., Soltani, M., Designing a Bankruptcy Prediction Model based on Account, Market and Macroeconomic Variables (Case Study: Cyprus Stock Exchange), "Iranian Journal of Management Studies", Vol. 9(1), pp. 125-147, 2016.
  • Pawełek, B., Pociecha, J., Baryła, M., Dynamic Aspects of Bankruptcy Prediction Logit Model for Manufacturing Firms in Poland, [in:] Wilhelm, A., Kestler, H. (eds.) "Analysis of Large and Complex Data", Studies in Classification, Data Analysis, and Knowledge Organization, Springer International Publishing Switzerland, Cham, pp. 369-382, 2016. https://doi.org/10.1007/978-3-319-25226-1_32
  • Shumway, T., Forecasting Bankruptcy More Accurately: A Simple Hazard Model, "The Journal of Business", Vol. 74(1), pp. 101-124, 2001. The University of Chicago Press. DOI: 10.1086/209665
  • Tinoco, M.H., Wilson, N., Financial Distress and Bankruptcy Prediction among listed Companies using Accounting, Market and Macroeconomic Variables, "International Review of Financial Analysis", Vol. 30, pp. 394-419, 2013.
  • Trabelsi, S., He, R., He, L., Kusy, M., A Comparison of Bayesian, Hazard, and Mixed Logit Model of Bankruptcy Prediction, "Computational Management Science", Vol. 12(1), pp. 81-97, 2015. https://doi.org/10.1007/s10287-013-0200-8
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171597027

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