Identification of financial ratios applicable in the construction of a prediction model for bankruptcy of wood industry enterprises

Abumustafa, N.I. 2006. Development of an early warning model for currency crises in emerging economies: An empirical study among Middle Eastern countries. International Journal of Management, 23 (3), 403–411. http://search.proquest.com/openview/3c1199229c95d431c9308af57c319e2a/1?pqorigsite=gscholarSearch in Google Scholar

Adamowicz, K. 2010. Price elasticity of demand for timber on primary local wood market in Poland. Sylwan, 154 (2), 130–138. http://sylwan.ibles.waw.pl/pls/apex/f?p=105:10:248927025306901::NO::P10_NAZWA_PLIKU,P10_ARTYKUL:F1164236155%2F2010_02_130au.pdf%2C2009018Search in Google Scholar

Adamowicz, K., Noga, T. 2014. Multivariate analysis of bankruptcy in companies in the wood sector. Sylwan, 1 58 (9), 6 43–650. http://sylwan.ibles.waw.pl/pls/apex/f?p=105:10:248927025306901::NO::P10_NAZWA_PLIKU,P10_ARTYKUL:F2046347597%2F2014_09_643au.pdf%2C2014003Search in Google Scholar

Adamowicz, K., Noga, T. 2017. Assessment applicability of selected models of multiple discriminant analyses to forecast financial situation of Polish wood sector enterprises. Folia Forestalia Polonica, Series A – Forestry, 59 (1), 59–67. https://depot.ceon.pl/bitstream/handle/123456789/11697/Journal_10340-Volume59_1-06article_ffp-59-6.pdf?sequence=1.10.1515/ffp-2017-0006Search 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. http://sylwan.ibles.waw.pl/pls/apex/f?p=105:10:248927025306901::NO::P10_NAZWA_PLIKU,P10_ARTYKUL:swiezy_plik%2C2015093.Search in Google Scholar

Ahn, B.S., Cho, S.S., Kim, C.Y. 2000. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18 (2), 65–74. DOI: 10,1016/S0957-4174(99)00053-6.10.1016/S0957-4174(99)00053-6Search in Google Scholar

Altman, E.I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, September, 589–609. DOI: 10.1111/j.1540-6261.1968.tb00843.x.10.1111/j.1540-6261.1968.tb00843.xOpen DOISearch in Google Scholar

Altman, E.I., Haldeman, G., Narayanan, P. 1977. Zeta analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, June, 29–54. DOI:10.1016/0378-4266(77)90017-6.10.1016/0378-4266(77)90017-6Open DOISearch in Google Scholar

Argenti, J. 1976. Corporate Collapse: The Causes and Symptoms. Mc Graw-Hill, London.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. DOI: 10.1016/j.bar.2005.09.00110.1016/j.bar.2005.09.001Open DOISearch in Google Scholar

Barniv, R., Hathorn, J. 1997. The merger or insolvency alternative in the insurance industry. Journal of Risk and Insurance, 64 (1), 89–113. DOI: 10.2307/253913.10.2307/253913Open DOISearch in Google Scholar

Barniv, R., McDonald, J.B. 1992. Identifying financial distress in the insurance industry: A synthesis of methodological and empirical issues. Journal of Risk and Insurance, 59, 543–573. DOI: 10.2307/253344.10.2307/253344Open DOISearch in Google Scholar

Beaver, W. 1966. Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. DOI: 10.2307/2490171.10.2307/2490171Open DOISearch in Google Scholar

Becchetti, L., Sierra, J. 2003. Bankruptcy risk and productive efficiency in manufacturing firms. Journal of Banking & Finance, 27 (11), 2099–2120. DOI:10.1016/S0378-4266(02)00319-9.10.1016/S0378-4266(02)00319-9Open DOISearch in Google Scholar

Berg, A., Borensztein, E., Pattillo, C. 2004. Assessing early warning systems: How have they worked in practice? IMF Working Paper, March 2004. Retrieved April 2009. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.494.2583&rep=rep1&type=pdf.Search in Google Scholar

Bilderbeek, J. 1978. Het voorspellen van falingen. Financiele kengetallen als thermometer voor de ondernemingsdoorlichting. Economisch en Sociaal Tijdschrift, 32 (1), 5–25.Search in Google Scholar

Branch, B. 2002. The costs of bankruptcy: A review. International Review of Financial Analysis, 11 (1), 39–57. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.463.5060&rep=rep1&type=pdf.10.1016/S1057-5219(01)00068-0Search in Google Scholar

Bredart, X. 2014. Financial distress and corporate governance: The impact of board configuration. International Business Research, 7 (3), 72–80. https://scholar.google.pl/scholar?q=Bredart%2C+X.+%282014%29.+Financial+distress+and+corporate+governance%3A+The+impact+of+board+configura-tion.+International+Business+Research%2C+7+%283%29%2C+72-80&btnG=&hl=pl&as_sdt=0%2C5.Search in Google Scholar

Bris, A., Welch, I., Zhu, N. 2006. The Costs of Bankruptcy: Chapter 7 liquidation versus chapter 11 reorganization. Journal of Finance, 61 (3), 1253–1303. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.365.4931&rep=rep1&type=pdf10.1111/j.1540-6261.2006.00872.xSearch in Google Scholar

Brockett, P.L., Cooper, W.W. 1990. Report to the State Auditor and the State Board of Insurance on Early Warning Systems to Monitor the Performance of Insurance Companies in Texas. Office of the State Auditor, Austin, TX.Search in Google Scholar

Brockett, P.L., Golden, L.L., Jang, J., Yang, C. 2006. A comparison of neural network, statistical methods and variable. Journal of Risk and Insurance, 73 (3), 397–419. https://www.researchgate.net/profile/Patrick_Brockett/publication/23690716_A_Comparison_of_Neural_Network_Statistical_Methods_and_Variable_Choice_for_Life_Insurers’_Financial_Distress_Prediction/links/02e7e518c174b1972a000000.pdf10.1111/j.1539-6975.2006.00181.xSearch in Google Scholar

Chen, I.J. 2014. Financial crisis and the dynamics of corporate governance: Evidence from Taiwan’s listed firms. International Review of Economics & Finance, 32, 3–28. DOI:10.1016/j.iref.2014.01.00410.1016/j.iref.2014.01.004Open DOISearch in Google Scholar

Daubie, M., Meskens, N. 2002. Business failure prediction: a review and analysis of the literature. Working Paper, Department of Productions and Operations Management, Catholic University of Mons, Belgium, 1–15. http://link.springer.com/chapter/10.1007/978-3-642-57478-8_5#page-1Search in Google Scholar

Davis, E.P., Karim, D. 2008a. Comparing early warning systems for banking crises. Journal of Financial Stability, 4 (2), 89–120. DOI: 10.1016/j.jfs.2007.12.00410.1016/j.jfs.2007.12.004Open DOISearch in Google Scholar

Davydenko, S.A., Strebulaev, I.A., Zhao, X. 2012. A market-based study of the cost of default. Review of Financial Studies, 25 (10), 2955–2999. http://rfs.oxfordjournals.org/content/25/10/295910.1093/rfs/hhs091Search in Google Scholar

Dąbrowski, B.J., Boratyńska, K. 2011. Using discriminant analysis models for insolvency predictions on a case of stock market index WIG-Spożywczy companies. Zeszyty Naukowe SGGW w Warszawie. Ekonomika i Organizacja Gospodarki Żywnościowej, 89, 163–173. http://www.wne.sggw.pl/czasopisma/pdf/EIOGZ_2011_nr89_s163.pdfSearch in Google Scholar

Deakin, E.B. 1972. A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10 (1), 167–179. http://www.jstor.org/stable/2490225?seq=1#page_scan_tab_contents10.2307/2490225Open DOISearch in Google Scholar

Divišová, P. 2013. The Use of Methods for Evaluation of Financial Health of Companies Operating in Chemical Industry. Crisis, 2 (3), 4. http://www.wseas.us/e-library/conferences/2013/Chania/AEBDa/AEBDa-41.pdfSearch in Google Scholar

Doumpos, M., Zopoudinis, C. 1999. A multicriteria discrimination method for the prediction of financial distress: the case of Greece. Multinational Finance Journal, 3 (2), 71–101. http://www.mfsociety.org/modules/modDashboard/uploadFiles/journals/MJ~644~p16stg5a3c17bbkm51a4m1gmkc5e4.pdf10.17578/3-2-1Search in Google Scholar

Edison, H.J. 2003. Do indicators of financial crises work? An evaluation of an early warning system. International Journal of Finance and Economics, 8 (1), 11–53.10.1002/ijfe.197Open DOISearch in Google Scholar

Elkamhi, R., Ericsson, J., Parsons, C.A. 2012. The cost and timing of financial distress. Journal of Financial Economics, 105 (1), 62–81. DOI: 10.1016/j.jfineco.2012.02.00510.1016/j.jfineco.2012.02.005Open DOISearch in Google Scholar

El-Shazly, A. 2003. Early warning of currency crises: An econometric analysis for Egypt. The Middle East Business and Economic Review, 18 (1), 34–48. http://search.proquest.com/openview/d1aa55f64ab9b658bb9b99aec005b261/1?pq-origsite=gscholarSearch in Google Scholar

Firlej, K., Bargieł, A., Szymański, M. 2014. The risk of failure of the food producing companies in Poland – on the example of companies listed on wig-food industry index. Folia Pomeranae Universitatis Technologiae Stetinensis, Oeconomica, 74 (1), 63–72. http://krzysztoffirlej.pl/nauka-i-organizacja/zagrozenie-upadloscia-przedsiebiorstw-przemyslu-spozywczego-w-polsce-na-przykladzie-spolek-z-indeksuwig-spozywczy/Search in Google Scholar

Godlewska, S. 2010. The Effectiveness of Polish Bankruptcy Prediction Models in Identifying the Insolvent Threat of Incorporated Enterprises. Annales Universitatis Mariae Curie-Skłodowska. Sectio H, Oeconomia, 44, 701–714. http://bazekon.icm.edu.pl/bazekon/element/bwmeta1.element.ekon-element-000171257421Search in Google Scholar

Gruszczyński, M. 2005. Strengths and Weaknesses of Bankruptcy Models. Materiały i Prace Instytutu Funkcjonowania Gospodarki Narodowej, 93, 185–187. http://bazekon.icm.edu.pl/bazekon/element/bwmeta1.element.ekon-element-000171282751Search in Google Scholar

Grzegorzewska, E. 2008. The bankruptcy of companies in Poland and other EU countries. Ekonomi ka i Organizacja Gospodarki Żywnościowej, 68, 51–63. http://www.wne.sggw.pl/czasopisma/pdf/EIOGZ_2008_nr68.pdf#page=48Search in Google Scholar

Hadasik, D. 1998. Upadłość przedsiębiorstw w Polsce i metody jej prognozowania. Zeszyty Naukowe. Seria 2, Prace Habilitacyjne / Akademia Ekonomiczna w Poznaniu, 153, 1–198. http://bazekon.icm.edu.pl/bazekon/element/bwmeta1.element.ekonelement-000072854733Search in Google Scholar

Hamrol, M., Czajka, B., Piechocki, M. 2004. Analiza dyskryminacyjna. Przegląd najważniejszych modeli. Przegląd Organizacji, 4, 34 -38. http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ekon-element-000000120372?q=bwmeta1.element.ekon-element-f52cfc2e-6523-37ed-adb2-1738ca76a3f4;7&qt=CHILDREN-STATELESSSearch in Google Scholar

Hill, N.T., Perry, S.E., Andes, S. 1996. Evaluating firms in financial distress: An event history analysis. Journal of Applied Business Research, 12 (3), 60–71. http://cluteinstitute.com/ojs/index.php/JABR/article/viewFile/5804/588610.19030/jabr.v12i3.5804Search 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

Jagiełło R. 2013. Analiza dyskryminacyjna i regresja logistyczna w procesie oceny zdolności kredytowej przedsiębiorstw. Materiały i Studia NBP, 286, 71–72.Search in Google Scholar

Katz, M. 2006. Multivariable analysis: A practical guide for clinicians. New York: Churchill-Livingstone.10.1017/CBO9780511811692Search in Google Scholar

Keasey, K., Watson, R. 1987. Non-financial symptoms and the prediction of small company failure: a test of Argenti’s hypotheses. Journal of Business Finance & Accounting, 14 (3), 335–354.10.1111/j.1468-5957.1987.tb00099.xOpen DOISearch in Google Scholar

Kisielińska, J., Waszkowski, A. 2010. The financial liquidity of agriculture farms situated in Lubelskie voivodeship. Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego, Ekonomika i Organizacja Gospodarki Żywnościowej, 82, 17–31. http://www.wne.sggw.pl/czasopisma/pdf/EIOGZ_2010_nr82.pdf#page=17Search 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. http://sylwan.ibles.waw.pl/pls/apex/f?p=105:10:0::NO::P10_NAZWA_PLIKU,P10_ARTYKUL:F546833311%2F2010_01_041au.pdf%2C2009025Search in Google Scholar

Koyuncugil, A.S., Ozgulbas, N. 2007. Developing financial early warning system via data mining. In: Proceedings Book of 4th Congress of SMEs and Productivity, Istanbul, 153–166.Search in Google Scholar

Koyuncugil, A.S., Ozgulbas, N. 2008. Early warning system for SMEs as a financial risk detector. In: Data mining applications for empowering knowledge societies (ed.: Hakikur Rahman), Idea Group Inc., New York, 221–240.10.4018/978-1-59904-657-0.ch012Search in Google Scholar

Koyuncugil, A.S., Ozgulbas, N. 2009a. Measuring and hedging operational risk by data mining. In: Proceedings Book of World Summit on Economic-Financial Crisis and International Business, Washington, 1–6.Search in Google Scholar

Koyuncugil, A.S., Ozgulbas, N. 2009b. An intelligent financial early warning system model based on data mining for SMEs. In: Proceedings of the International Conference on Future Computer and Communication, Kuala Lumpur, Malaysia, 662–666. DOI: 10.1109/ICFCC.2009.11810.1109/ICFCC.2009.118Open DOISearch in Google Scholar

Koyuncugil, A.S., Ozgulbas, N. 2012. Financial early warning system model and data mining application for risk detection. Expert Systems with Applications, 39 (6), 6238–6253. DOI: 10.1016/j.eswa.2011.12.02110.1016/j.eswa.2011.12.021Open DOISearch in Google Scholar

Kyong, J.O., Tae, Y.K, Chiho, K., Suk, J.L. 2006. Using neural networks to tune the fluctuation of daily financial condition indicator for financial crisis forecasting. Advances in Artificial Intelligence, 4304, 607–616. DOI: 10.1016/j.eswa.2011.12.02110.1016/j.eswa.2011.12.021Open DOISearch in Google Scholar

Laitinen, E.K. 1992. Prediction of failure of a newly founded firm. Journal of Business, 7, 323–340. DOI: 10.1016/0883-9026(92)90005-C10.1016/0883-9026(92)90005-Open DOISearch in Google Scholar

Laitinen, E.K., Chong, H.G. 1999. Early warning system for crisis in SMEs: Preliminary evidence from Finland and the UK. Journal of Small Business and Enterprise Development, 6 (1), 89–102. https://www.researchgate.net/profile/H_Gin_Chong/publication/242337131_Early-warning_system_for_crisis_in_SMEs_preliminary_evidence_from_Finland_and_the_UK/links/5520a0e10cf2a2d9e1434cbe.pdf10.1108/EUM0000000006665Search in Google Scholar

Lee, S.H., Urrutia, J.L. 1996. Analysis of insolvency prediction in the property liability insurance industry: A comparison of logit and hazard models. Journal of Risk and Insurance, 63, 121–130.10.2307/253520Search in Google Scholar

Lehmann, B. 2003. Is it worth the while? The relevance of qualitative information in credit rating, Working Paper presented at the EFMA 2003 Meetings, Helsinki, Finland, 25–29 June 2003, 1–25. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410186Search in Google Scholar

Lestano, L., Jacobs, L.J. Kuper, G.H. 2004. Indicators of financial crises do work! An early warning system for six Asian countries. CCSO Working Paper 13. Department of Economics, University of Groningen, the Netherlands. http://econpapers.repec.org/paper/wpawuwpif/0409004.htm10.2139/ssrn.480021Search in Google Scholar

Li, S., Wang, S. 2014. A financial early warning logit model and its efficiency verification approach. Knowledge-Based Systems, 70, 78–87. DOI: 10.1016/j.knosys.2014.03.01710.1016/j.knosys.2014.03.017Open DOISearch in Google Scholar

Liang, D., Lu, C.-C., Tsai, C.-F., Shih, G.-A. 2016. Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252 (2), 561–572. DOI: 10.1016/j.ejor.2016.01.01210.1016/j.ejor.2016.01.012Open DOISearch in Google Scholar

Lin, W.-Y., Hu, Y.-H., Tsai, C.-F. 2012. Machine learning in financial crisis prediction: A survey IEEE Transactions on Systems, Man and Cybernetics – Part C: Applications and Reviews, 42 (4), 421–436. 10.1109/TSMCC.2011.217042010.1109/TSMCC.2011.2170420Search in Google Scholar

Lin, F.-Y., Liang, D., Chu, W.-S. 2010. The role of nonfinancial features related to corporate governance in business crisis prediction. Journal of Marine Science and Technology, 18 (4), 504–513. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.463.9163&rep=rep1&type=pdf10.51400/2709-6998.1901Search in Google Scholar

Lussier, R.N. 1995. A nonfinancial business success versus failure prediction model for young firms. Journal of Small Business Management, 33 (1), 8–20. http://search.proquest.com/openview/88a9dbbdad41d0e9af99d92b78fbb1d6/1?pqorigsite=gscholarSearch in Google Scholar

Lussier, R.N., Corman, J. 1994. A success vs. failure prediction model of the manufacturing industry, Paper presented at the Conference of the Small Business Institute Director’s Association, San Antonio, Texas, February 1994, 48, 1–5.Search in Google Scholar

Maltz, A.C., Shenhar, A.J., Reilly, R.R. 2003. Beyond the balanced scorecard: refining the search for organizational success measures. Long Range Planning, 36, 187–204. DOI: 10.1016/S0024-6301(02)00165-610.1016/S0024-6301(02)00165-6Open DOISearch in Google Scholar

Mączyńska, E. 1994. Ocena kondycji przedsiębiorstwa. Uproszczone metody. Życie Gospodarcze, 38, 42–45. http://bazekon.icm.edu.pl/bazekon/element/bwmeta1.element.ekon-element-000000100966Search in Google Scholar

Mączyńska, E., Zawadzki, M. 2006. Dyskryminacyjne modele predykcji upadłości przedsiębiorstw. Ekonomista, 2, 205–217. http://www.pte.pl/pliki/2/12/Ekonomista%2025%2002%2006ostfragment.pdfSearch in Google Scholar

Mensah, Y.M. 1984. An examination of the stationarity of multivariate bankruptcy prediction models: a methodological study. Journal of Accounting Research, 22 (1), 380–395. https://www.researchgate.net/profile/Yaw_Mensah/publication/259673939_An_Examination_of_the_Stationarity_of_Multivariate_Bankruptcy_Prediction_Models_A_Methodological_Study/links/570b167908ae2eb9422004a7.pdf10.2307/2490719Search in Google Scholar

Nehrebecka, N., Dzik, A.M. 2012. Konstrukcja miernika szans na bankructwo firmy. Narodowy Bank Polski. Departament Edukacji i Wydawnictw. http://lodz.stat.gov.pl/gfx/lodz/userfiles/_public/pliki/inne/201311_d_konf_swr_nehrebecka.pdfSearch in Google Scholar

Noga, T., Adamowicz, K., Jakubowski, J. 2014. Discriminating methods in the assessment of financial situation in timber industry enterprises. Acta Sci. Pol., Silv. Colendar. Rat. Ind. Lignar., 13 (1), 25–35. http://www.forestry.actapol.net/pub/3_1_2014.pdfSearch in Google Scholar

Ohlson, J. 1980. Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18 (1), 109–131. DOI: 10.2307/249039510.2307/2490395Open DOISearch in Google Scholar

Ooghe, H., Camerlynck, J., Balcaen, S. 2003. The Ooghe-Joos-De Vos failure prediction models: a cross-industry validation. Brussels Economic Review, 46 (1), 39–70.Search in Google Scholar

Ozgulbas, N., Koyuncugil, A.S. 2010. Financial early warning system for risk detection and prevention from financial crisis. In: Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection (eds.: A.S. Koyuncugil, N. Ozgulbas). Idea Group Inc., New York, 76–108.10.4018/978-1-61692-865-0.ch005Search in Google Scholar

Pantalone, C., Platt, M. 1987. Predicting failures of savings and loan associations. Areuea Journal, 15, 46–64.10.1111/1540-6229.00418Search in Google Scholar

Platt, H., Platt, M. 2002. Predicting Corporate Financial Distress: Reflections on Choice-Based Sample Bias. Journal of Economics and Finance, 2. DOI: 10.1007/BF0275598510.1007/BF02755985Open DOISearch in Google Scholar

Prusak, B. 2004. Metody wykorzystywane w analizie porównawczej modeli oceny zagrożenia przedsiębiorstw upadłością. Wydział Zarządzania i Ekonomiki Politechniki Gdańskiej, 1–5. http://www1.zie.pg.gda.pl/~pb/ap.pdfSearch in Google Scholar

Prusak, B. 2012. Zalety i ograniczenia modeli prognozowania zagrożenia przedsiębiorstw upadłością. Wyd. Oficyna wydawnicza SGH, Warszawa.Search in Google Scholar

Salas, V., Saurina, J. 2002. Credit risk in two institutional regimes: Spanish commercial and savings banks. Journal of Financial Services Research, 22 (3), 203–224. DOI:10.1023/A:101978110967610.1023/A:1019781109676Open DOISearch in Google Scholar

Sheppard, J.P. 1994. Strategy and bankruptcy: An exploration into organizational death. Journal of Management, 20 (4), 795–833. http://www.sfu.ca/~sheppard/papers/JPS94c_old.pdf10.1016/0149-2063(94)90031-0Search in Google Scholar

Slowinski, R., Zopounidis, C. 1995. Application of the Rough Set Approach to Evaluation of Bankruptcy Risk. Int. J. Intell. Syst. Acc. Fin. Mgmt., 4, 27–41. DOI: 10.1002/j.1099–1174.1995.tb00078.x10.1002/j.10991174.1995.tb00078.xOpen DOISearch in Google Scholar

Stanisz, A. 2007. Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny (3). Analizy wielowymiarowe. StatSoft Polska, Kraków.Search in Google Scholar

Taffler, R.J. 1984. Empirical models for the monitoring of UK corporations. Journal of Banking and Finance, 8, 199–227. DOI: 10.1016/0378-4266(84)90004-910.1016/0378-4266(84)90004-9Open DOISearch in Google Scholar

Taffler, R.J., Tisshaw, H. 1977. Going, going, gone-four factors which factors which predict. Accountancy, March, 50–54.Search in Google Scholar

Trieschmann, J.S., Pinches, G.E. 1973. A multivariate model for predicting financially distressed property-liability insurers. Journal of Risk and Insurance, 40, 327–338. DOI:10.2307/25222210.2307/252222Open DOISearch in Google Scholar

Tymoszczuk, M. 2013. Skuteczność modeli prognozowania upadłości przedsiębiorstw a upływ czasu-porównanie popularnych Polskich modeli wielowymiarowej analizy dyskryminacyjnej z modelem zbudowanym przez autorkę. In: Upadłości, bankructwa i naprawa przedsiębiorstw – Wybrane zagadnienia (eds.: A. Adamska., E. Mączyńska). SGH, 12, 193–194.Search in Google Scholar

Wardzińska, K. 2012. Przykład zastosowania analizy dyskryminacyjnej do oceny sytuacji finansowej przedsiębiorstw. Ekonomia i Zarządzanie, 4 (3), 197–208. http://zneiz.pb.edu.pl/data/magazine/article/118/3.4_wardzinska.pdfSearch in Google Scholar

Wędzki, D. 2005. Wielowymiarowa analiza bankructwa na przykładzie budownictwa. Badania Operacyjne i Decyzyjne, 2, 59–81.Search in Google Scholar

Wierzba, D. 2000. Wczesne wykrywanie przedsiębiorstw zagrożonych upadłością na podstawie analizy wskaźników finansowych – teoria i badania empiryczne. Zeszyty Naukowe Wyższej Szkoły Ekonomiczno-Informatycznej w Warszawie, 9, 79–105.Search in Google Scholar

Wojnar, J. 2014. Ocena skuteczności modeli analizy dyskryminacyjnej do prognozowania zagrożenia finansowego spółek giełdowych. Zeszyty Naukowe Małopolskiej Wyższej Szkoły Ekonomicznej w Tarnowie, 1 (24), 221–231. http://zn.mwse.edu.pl/wojnar-jolanta-ocena-skutecznosci-modeli-analizydyskryminacyjnej-do-prognozowania-zagrozeniafinansowego-spolek-gieldowych/Search in Google Scholar

Wu, J.L. 2007. Do Corporate Governance Factors Matter for Financial Distress Prediction of Firms? Evidence from Taiwan (Doctoral dissertation, University of Nottingham).Search in Google Scholar

Xu, K., Zhao, Q., Bao, X. 2015. Study on early warning of enterprise financial distress – based on partial leastsquares logistic regression. Acta Oeconomica, 65, 3–16. DOI: http://dx.doi.org/10.1556/032.65.2015.S2.210.1556/032.65.2015.S2.2Open DOISearch in Google Scholar

Yang, B., Ling, X.L., Hai, J., Jing, X. 2001. An early warning system for loan risk assessment using artificial neural networks. Knowledge Based Systems, 14 (5/6), 303–306. DOI: 10.1016/S0950-7051(01)00110-110.1016/S0950-7051(01)00110-1Open DOISearch in Google Scholar

Zavgren, C. 1985. Assessing the vulnerability to failure of American industrial firms: A logistics analysis. Journal of Accounting Research, 22, 59–82. DOI: 10.1111/j.1468-5957.1985.tb00077.x10.1111/j.1468-5957.1985.tb00077.xOpen DOISearch in Google Scholar

Zmijewski, M.E. 1984. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, Supplement, 59.10.2307/2490859Open DOISearch in Google Scholar