The stability of bankruptcy predictors in the construction and manufacturing industries at various times before bankruptcy

dc.contributor.authorKaras, Michal
dc.contributor.authorRežňáková, Mária
dc.contributor.otherEkonomická fakultacs
dc.description.abstractThis article focuses on the design of bankruptcy models, specifically the selection of suitable predictors. Previous research has drawn mainly on data concerning manufacturing companies one year before bankruptcy. Our research examines financial ratios that are suitable bankruptcy indicators in two different industries (the construction and manufacturing industries) over a period of five years prior to bankruptcy. Our main objective is to verify whether bankruptcy predictors are industry-specific. Another objective was to determine which indicators can detect signs of bankruptcy earlier than one period before bankruptcy. We presume that the application of industry-specific indicators can help increase the predictive accuracy of bankruptcy models when applied to a particular industry. Per analogiam, we assume that the inclusion of indicators capable of detecting signs of bankruptcy more than a year before its occurrence will increase their predictive capacity. Significant predictors were first identified on a linear basis using the parametric t-test or F-test; for the sake of comparison, a non-linear non-parametric Boosted Trees method was also applied. Data for a total of 34,229 active companies and 304 companies that went bankrupt during the relevant period was analyzed. The research confirmed our presumption that bankruptcy predictors are both industry and time specific. Four years before bankruptcy, the indicators return on assets, inventory turnover and asset structure are important predictors in both the manufacturing and construction industries. The net working capital to total assets ratio is a specific predictor for manufacturing companies in the third year before bankruptcy, as is the short-term indebtedness indicator. In the construction industry, specific predictors are the net working capital to sales ratio in the third and first years before bankruptcy, and the interest coverage indicator in all four years preceding bankruptcy. Were these indicators to be included in a model for an alternative industry, they would be likely to reduce its accuracy.en
dc.format.extent116-133 strancs
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisherTechnická Univerzita v Libercics
dc.relation.isbasedonAltman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. doi:10.1111/j.1540-6261.1968.tb00843.x.
dc.relation.isbasedonAltman, E. I. (1983). Corporate Financial Distress: A Complete Guide to Predicting, Avoiding and Dealing with Bankruptcy. New York: John Wiley and Sons.
dc.relation.isbasedonBányiová, T., Bieliková, T., & Piterková, A. (2014). Prediction of Agricultural Enterprises Distress Using Data Envelopment Analysis. In O. Deev, V. Kajurová, J. Krajíček (Eds.), Proceedings of the 11th International Scientific Conference European Financial Systems 2014 (pp. 18-25). Brno: Masaryk University.
dc.relation.isbasedonBarnes, P. (1982). Methodological implications of non-normally distributed financial ratios. Journal of Business Finance and Accounting, 9(1), 51-62. doi:10.1111/j.1468-5957.1982.tb00972.x.
dc.relation.isbasedonBeaver, W. H. (1966). Financial Ratios as predictors of Failure. Journal of Accounting Research. Empirical Research in Accounting: Selected Studies, 4(1), 71-111. doi:10.2307/2490171.
dc.relation.isbasedonBraun, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446-3453. doi:10.1016/j.eswa.2011.09.033.
dc.relation.isbasedonBreiman, L., Friedman, J. H., Olshen, R., & Stone, C. (1983). Classification and Regression Trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.
dc.relation.isbasedonCarling, K., Jacobson, T., Lindé, J., & Rozsbach, K. (2007). Corporate credit risk modelling and the macroeconomy. Journal of Banking & Finance, 31(3), 845-868. doi:10.1016/j.jbankfin.2006.06.012.
dc.relation.isbasedonCox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society. Series B (Methodological), 34(2), 187-220. doi:10.2307/2985181.
dc.relation.isbasedonCút, S. (2014). Prediction of Company Financial Distress Using Neural Network Based on the Radial Basis Function. In Y. Zhang (Ed.), Lecture Notes in Management Science (pp. 45-51). Singapore: Singapore Management and Sports Science Institute.
dc.relation.isbasedonDeakin, E. B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), 167-179, doi:10.2307/2490225.
dc.relation.isbasedonDelina, R., & Pácková, M. (2013). Validácia predikčných bankrotových modelov v podmienkach SR. E&M Ekonomie a Management, 16(3), 101-112.
dc.relation.isbasedonFaltus, S. (2014). Prediction of Agricultural Enterprises Distress Using Data Envelopment Analysis. In O. Deev, V. Kajurová, & J. Krajíček (Eds.), Proceedings of the 11th International Scientific Conference European Financial Systems 2014 (pp. 173-177). Brno: Masaryk University.
dc.relation.isbasedonFreund, Y., & Schapire, R. (1997). A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139. doi:10.1006/jcss.1997.1504.
dc.relation.isbasedonFriedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189-1232. doi:10.1214/aos/1013203451.
dc.relation.isbasedonGertler, Ľ. (2015). Interactions of Unconventional Monetary Policy Measures with the Euro Area Yield Curve. Czech Journal of Economics and Finance, 65(2), 106-126.
dc.relation.isbasedonGordini, N. (2014). A genetic algorithm approach for SMEs bankruptcy prediction: Empirical evidence from Italy. Expert Systems with Applications, 41(14), 6433-6445. doi:10.1016/j.eswa.2014.04.026.
dc.relation.isbasedonGilson, S. C. (1989). Management turnover and financial distress. Journal of Financial Economics, 25(2), 241-262. doi:10.1016/0304-405X(89)90083-4.
dc.relation.isbasedonGrice, J. S., & Dugan, M. T. (2001). The limitations of bankruptcy prediction models: Some cautions for the researchers. Review of Quantitative Finance and Accounting, 17(2), 151-166. doi:10.1023/A:1017973604789.
dc.relation.isbasedonGuelman, L. (2012). Gradient boosting trees for auto insurance loss cost modelling and prediction. Expert Systems with Applications, 39(3), 3659-3667. doi:10.1016/j.eswa.2011.09.058.
dc.relation.isbasedonHastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag.
dc.relation.isbasedonHenerby, K. L. (1996). Do Cash Flows Variables Improve the Prediction Accuracy of a Cox Proportional Hazards Model for Bank Failure? The Quarterly Review of Economics and Finance, 36(3), 395-409. doi:10.1016/S1062-9769(96)90023-X.
dc.relation.isbasedonHomolka, L., Knápková, A., & Pavelková, D. (2015). Plastics Cluster Members and Their competitors – DEA benchmarking study. In E. Pastuszková, Z. Crhová, J. Vychytilová, B .Vytrhlíková, & A. Knápková (Eds.), Proceedings of the 7th International Scientific Conference Finance and Performance of Firms in Science, Education and Practice (pp. 409-415). Zlín: Tomas Bata University in Zlín.
dc.relation.isbasedonChen, L. H., & Hsiao, H. D. (2008). Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study. Expert Systems with Applications, 35(3). doi:10.1016/j.eswa.2007.08.010.
dc.relation.isbasedonKaras, M., & Režňáková, M. (2013). Bankruptcy Prediction Model of Industrial Enterprises in the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 7(5), 519-531.
dc.relation.isbasedonKapliński, O. (2008). Usefulness and credibility of scoring methods in construction industry. Journal of civil engineering and management, 14(1), 21-28. doi:10.3846/1392-3730.2008.14.21-28.
dc.relation.isbasedonKim, M. J., & Kang, D. K. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37(4), 3373-3379. doi:10.1016/j.eswa.2007.05.019.
dc.relation.isbasedonKwak, W., Cheng, Y., Ni, J., Shi, Y., Gong, G., & Yan, N. (2014). Bankruptcy Prediction for Chinese Firms: Comparing Data Mining Tools with Logit Analysis. Journal of Modern Accounting and Auditing, 10(10), 1548-6583.
dc.relation.isbasedonLaitinen, E. K., Lukason, O., & Suvas, A. (2014). Behaviour of Financial Ratios in Firm Failure Process: An International Comparison. International Journal of Finance and Accounting, 3(2), 122-131. doi:10.5923/j.ijfa.20140302.09.
dc.relation.isbasedonLin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094-15102. doi:10.1016/j.eswa.2011.05.035.
dc.relation.isbasedonMeloun, M., & Militký, J. (1994). Statistické zpracování experimentálních dat. Praha: Plus.
dc.relation.isbasedonNiemann, M., Schmidt, J. H., & Neukirchen, M. (2008). Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity. Journal of Banking & Finance, 32(3), 434-446. doi:10.1016/j.jbankfin.2007.05.015.
dc.relation.isbasedonNg, S. T., Wong, J. M. W., & Zhang, J. (2011). Applying Z-score model to distinguish insolvent construction companies in China. Habitat International, 35(1), 599-607. doi:10.1016/j.habitatint.2011.03.008.
dc.relation.isbasedonOhlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109-131. doi:10.2307/2490395.
dc.relation.isbasedonPerry, L., Henderson, G., & Cronan, T. (1984). Multivariate analysis of corporate bond ratings and industry classification. Journal of Financial Research, 7(1), 27-36. doi:10.1111/j.1475-6803.1984.tb00351.x.
dc.relation.isbasedonPlatt, D. H., & Platt, M. B. (1990). Development of a Class of Stable Predictive Variables: The Case of Bankruptcy Prediction. Journal of Business Finance & Accounting, 17(1), 31-51. doi:10.1111/j.1468-5957.1990.tb00548.x.
dc.relation.isbasedonPeel, M. J., & Peel, D. A. (1987). Some further empirical evidence on predicting private company failure. Accounting and Business Research, 18(69), 57-66. doi:10.1080/00014788.1987.9729348.
dc.relation.isbasedonShumway, T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 74(1), 101-124. doi:10.2139/ssrn.171436.
dc.relation.isbasedonTian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52(1), 90-100. doi:10.1016/j.jbankfin.2014.12.003.
dc.relation.isbasedonTwala, B. (2010). Multiple classifier application to credit risk assessment. Expert Systems with Applications, 37(4), 3326-3336. doi:10.1016/j.eswa.2009.10.018.
dc.relation.isbasedonWu, W. (2010). Beyond business failure prediction. Expert Systems with Applications, 37(3), 2371-2376. doi:10.1016/j.eswa.2009.07.056.
dc.relation.isbasedonWu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6(1), 34-45. doi:10.1016/j.jcae.2010.04.002.
dc.relation.isbasedonZmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(1), 59-82. doi:10.2307/2490859.
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.rightsCC BY-NC
dc.subjectfinancial ratios, bankruptcy prediction models, time-specific predictors, branch-specific predictors, manufacturing, construction, boosted treesen
dc.titleThe stability of bankruptcy predictors in the construction and manufacturing industries at various times before bankruptcyen
local.facultyFaculty of Economics
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