A Comparison of Variables Selection Methods and their Sequential Application: A Case Study of the Bankruptcy of Polish Companies

Alaka, H.A., Oyedele, O.L. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164–184. DOI: 10.1016/j.eswa.2017.10.040.10.1016/j.eswa.2017.10.040Search in Google Scholar

Altman, E.I. (1968). Financial Ratios, Discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23 (4).10.1111/j.1540-6261.1968.tb00843.xSearch in Google Scholar

Barboza, F., Kimura, H., Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. DOI: 10.1016/j.eswa.2017.04.006.10.1016/j.eswa.2017.04.006Search in Google Scholar

Fleiss, J.L., Cohen, J. (1973). The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and Psychological Measurement, 33, 613–619.10.1177/001316447303300309Search in Google Scholar

Friedman, J., Hastie, T., Tibshirani, R. (2013). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. The Mathematical Intelligencer, 27 (2), 83–85. DOI: 10.1007/BF02985802.10.1007/BF02985802Search in Google Scholar

Greenberg, E. (2012). Introduction to Bayesian Econometrics. New York, NY: Cambridge University Press. DOI: 10.1017/CBO9781139058414.10.1017/CBO9781139058414Search in Google Scholar

Gruszczyński, M. (ed.) (2012). Mikroekonometria. Modele i metody analizy danych indywidualnych. Warszawa: Wolters Kluwer Polska.Search in Google Scholar

Gruszczyński, M. (2003). Modele mikroekonometrii w analizie i prognozowaniu zagrożenia finansowego przedsiębiorstw. Zeszyty Polskiej Akademii Nauk, 34.Search in Google Scholar

Hołda, A. (2000). Optymalizacja i model zastosowania procedur analitycznych w rewizji sprawozdań finansowych. Kraków: Akademia Ekonomiczna.Search in Google Scholar

Höglund, H. (2017). Tax payment default prediction using genetic algorithm-based variable selection. Expert Systems with Applications, 88, 368–375. DOI: 10.1016/j.eswa.2017.07.027.10.1016/j.eswa.2017.07.027Search in Google Scholar

Iturriaga, F.L., Sanz, I.P. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42 (6), 2857–2869. DOI: 10.1016/j.eswa.2014.11.025.10.1016/j.eswa.2014.11.025Search in Google Scholar

Jolliffe, I. (2011). Principal Component Analysis. In: M., Lovric (ed.), International Encyclopedia of Statistical Science. Berlin–Heidelberg: Springer. DOI: 10.1007/978-3-642-04898-2_455.10.1007/978-3-642-04898-2_455Search in Google Scholar

Karr, C. (1995). Adaptive control of an exothermic chemical reaction system using fuzzy logic and genetic algorithms. In: L.R., Medsker (ed.), Hybrid intelligent systems (pp. 187–203). New York, NY: Springer US.Search in Google Scholar

Mączyńska, E., Zawadzki, M. (2006). Dyskryminacyjne modele predykcji bankructwa przedsiębiorstw. Ekonomista, 2, 205–235.Search in Google Scholar

McFadden, D.L. (1974). Conditional logit analysis of qualitative choice behaviour. Frontiers in econometrics (pp. 105–142). New York: Academic Press.Search in Google Scholar

Merton, R. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. Journal of Finance, 29, 449–470.Search in Google Scholar

Merwin, C. (1942). Financing small corporations in five manufacturing industries, 1926–1936. New York: National Bureau of Economic Research.Search in Google Scholar

Obermann, L., Waack, S. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42 (23), 9117–9128. DOI: 10.1016/j.eswa.2015.08.009.10.1016/j.eswa.2015.08.009Search in Google Scholar

Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18 (1), 109–131. DOI: 10.2307/2490395.10.2307/2490395Search in Google Scholar

Pereira, J., Basto, M., Ferreira, A. (2016). The Logistic Lasso and Ridge Regression in Predicting Corporate Failure. Procedia Economics and Finance, 39, 634–641. DOI: 10.1016/S2212-5671(16)30310-0.10.1016/S2212-5671(16)30310-0Search in Google Scholar

Prusak, B. (2005). Nowoczesne metody prognozowania zagrożenia finansowego przedsiębiorstwa. Warszawa: Difin.Search in Google Scholar

Rutenbar, R.A. (1989). Simulated annealing algorithms: an overview. IEEE Circuits and Devices Magazine, 5 (1), 19–26. DOI: 10.1109/101.17235.10.1109/101.17235Search in Google Scholar

Smith, R., Winakor, A. (1935). Changes in financial structure of unsuccessful industrial corporations. The Accounting Review, 10 (2), 174–184.Search in Google Scholar

Shin, K., Lee, Y. (2002). A genetic algorithm application in bankruptcy prediction modelling. Expert Systems with Applications, 23 (3), 321–328. DOI: 10.1016/S0957-4174(02)00051-9.10.1016/S0957-4174(02)00051-9Search in Google Scholar

Sun, J., Hui, L. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56. DOI: 10.1016/j.knosys.2013.12.006.10.1016/j.knosys.2013.12.006Search in Google Scholar

Tian, S. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52, 89–100. DOI: 10.1016/j.jbankfin.2014.12.003.10.1016/j.jbankfin.2014.12.003Search in Google Scholar

Traczynski, J. (2017). Firm Default Prediction: A Bayesian Model-Averaging Approach. Journal of Financial and Quantitative Analysis, 52 (3), 1211–1245. DOI: 10.1017/S002210901700031X.10.1017/S002210901700031XSearch in Google Scholar

Zieba, M., Tomczak, S.K., Tomczak, J.M. (2016). Ensemble Boosted Trees with Synthetic Features Generation in Application to Bankruptcy Prediction. Expert Systems with Applications, 58, 93–101. DOI: 10.1016/j.eswa.2016.04.001.10.1016/j.eswa.2016.04.001Search in Google Scholar