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Assessment applicability of selected models of multiple discriminant analyses to forecast financial situation of Polish wood sector enterprises

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Abellán J., Mantas C.J. 2014. Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 41 (8), 3825-3830. http://doi.org/10.1016/j.eswa.2013.12.003.10.1016/j.eswa.2013.12.003Search in Google Scholar

Adamowicz K., Noga T. 2014. Multivariate analysis of bankruptcy in companies in the wood sector. Sylwan, 156 (9), 643-650.Search in Google Scholar

Adamowicz K., Szramka H., Starosta-Grala M., Szczypa P. 2016. Export and import of timber in selected member states of the European Union. Sylwan, 160 (3), 179-186.Search in Google Scholar

Afik Z., Arad O., Galil K. 2016. Using Merton model for default prediction: An empirical assessment of selected alternatives. Journal of Empirical Finance, 35, 43-67. http://doi.org/10.1016/j.jempfin.2015.09.004.10.1016/j.jempfin.2015.09.004Search in Google Scholar

Agarwal V., Taffler R.J. 2007. Twenty-five years of the Taffler z-score model: Does it really have predictive ability? Accounting and Business Research, 37 (4), 285-300.Search in Google Scholar

Altman E.I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23 (4), 589-609. http://doi.org/10.1111/j.1540-6261.1968.tb00843.x.10.1111/j.1540-6261.1968.tb00843.xSearch in Google Scholar

Altman E.I., Narayanan P. 1997. An international survey of business failure classification models. Financial Markets, Institutions and Instruments, 6 (2), 1-57.Search in Google Scholar

Balcaen S., Ooghe H. 2006. 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38 (1), 63-93. http://doi.org/10.1016/j.bar.2005.09.001.10.1016/j.bar.2005.09.001Search in Google Scholar

Brezigar-Masten A., Masten I. 2012. CART-based selection of bankruptcy predictors for the logit model. Expert Systems with Applications, 39 (11), 10153-10159. http://doi.org/10.1016/j.eswa.2012.02.125.10.1016/j.eswa.2012.02.125Search in Google Scholar

Card D.H. 1982. Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogrammetric Engineering and Remote Sensing, 48, 431-439.Search in Google Scholar

Chen N., Ribeiro B., Vieira A., Chen A. 2013. Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Systems with Applications, 40 (1), 385-393. http://doi.org/10.1016/j.eswa.2012.07.047.10.1016/j.eswa.2012.07.047Search in Google Scholar

Delen D., Kuzey C., Uyar A. 2013. Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40 (10), 3970-3983. http://doi.org/10.1016/j.eswa.2013.01.012.10.1016/j.eswa.2013.01.012Search in Google Scholar

Dimitras A.I., Zanakis S.H., Zopounidis C. 1996. A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90 (3), 487-513.Search in Google Scholar

du Jardin P. 2016. A two-stage classification technique for bankruptcy prediction. European Journal of Operational Research, 254 (1), 236-252. http://doi.org/10.1016/j.ejor.2016.03.008.10.1016/j.ejor.2016.03.008Search in Google Scholar

Grice J.S., Dugan M.T. 2001. The limitations of bankruptcy prediction models: Some cautions for the researcher. Review of Quantitative Finance and Accounting, 17 (2), 151-166.Search in Google Scholar

Gruszczynski M. 2005. Strengths and Weaknesses of Bankruptcy Models. Materialy i Prace Instytutu Funkcjonowania Gospodarki Narodowej, 93, 185-187.Search in Google Scholar

Hołda A. 2001. Prognozowanie bankructwa jednostki w warunkach gospodarki polskiej z wykorzystaniem funkcji dyskryminacyjnej ZH. Rachunkowość, 5, 306-310.Search in Google Scholar

Juszczyk S., Balina R. 2014. Prognozowanie zagrożenia bankructwem przedsiębiorstw w wybranych branżach. Ekonomista, 1, 67-95.Search in Google Scholar

Kocel J. 2010. Methodological foundations of financial and economic forecast for the State Forests National Forest Holding. Sylwan, 154 (1), 41-51.Search in Google Scholar

Matuszyk A. 2003. Modele scoringowe-pojęcie, etapy budowy, rodzaje. Studia i Prace Kolegium Zarządzania i Finansów Szkoła Główna Handlowa, 40, 9-21.Search in Google Scholar

Obermann L., Waack S. 2015. Demonstrating noninferiority of easy interpretable methods for insolvency prediction. Expert Systems with Applications, 42 (23), 9117-9128. http://doi.org/10.1016/j.eswa.2015.08.009.10.1016/j.eswa.2015.08.009Search in Google Scholar

Ozgulbas N., Koyuncugil A.S. 2010. Financial early warning system for risk detection and prevention from financial crisis. Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection. Idea Group Inc, New York, 76-108.Search in Google Scholar

Prusak B. 2004. Metody wykorzystywane w analizie porownawczej modeli oceny zagrożonych przedsiębiorstw upadłoscia. Retrieved September 15, 2016, from http://www1.zie.pg.gda.pl/~pb/ap.pdf.Search in Google Scholar

Ribeiro B., Silva C., Chen N., Vieira A., Carvalho das Neves J. 2012. Enhanced default risk models with SVM+. Expert Systems with Applications, 39 (11), 10140-10152. http://doi.org/10.1016/j.eswa.2012.02.142.10.1016/j.eswa.2012.02.142Search in Google Scholar

Stawicki J., Sojak S. 2001. Wykorzystanie metod taksonomicznych do oceny kondycji ekonomicznej przedsiębiorstw. Zeszyty Teoretyczne Rachunkowości, 3 , 55-66.Search in Google Scholar

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Sujets de la revue:
Life Sciences, Plant Science, Medicine, Veterinary Medicine