Failure prediction from the investors’ view by using financial ratios. Lesson from Romania

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Show simple item record Achim, Monica Violeta Borlea, Sorin Nicolae Găban, Lucian Vasile
dc.contributor.other Ekonomická fakulta cs 2016-12-05 2016-12-05 2016-12-05
dc.identifier.issn 1212-3609
dc.description.abstract The purpose of our study is to identify which financial indicators have a significant impact on the probability of Romanian companies’ bankruptcy risk from the investors’ point of view by studying the impact on the probability of shares delisting from the stock exchange. The research is conducted on a sample of 16 failed and 21 non-failed non-financial companies listed on the Bucharest Stock Exchange between 2002 and 2012. The Logit analysis is used for identifying the variables that are significant and have predictive power on distress likelihood. By using 12 main financial ratios, we estimate three alternative Logit models for determining their signs, significance, predictive power, efficiency of fit tests. The first model provides the highest explanatory power. Three variables such as Flexibility ratio (FLEX), Assets turnover (ASTU) and Current assets turnover (CASTU) are found to be significant determinants for stock exchange delisting. These three variables provide 52.59% of correct prediction of bankruptcy risk. The percentage for correctly classified observations for the fitted Logit model is of 83.33%. Moreover, this research attempts to reveal the changes that may appear among bankruptcy predictors given that the bankruptcy risk model is developed from the investors’ point of view and not from that of a simple decision-making person. For a stock market investor, bankruptcy already starts at the stage of delisting the company because the investment was strongly compromised, whether or it continues its activity or not. Orientation towards investors when predicting bankruptcy risk is the main element of originality that our research adds to the scientific achievements in bankruptcy, until this moment. en
dc.format.extent 117-133 s. cs
dc.language.iso en
dc.publisher Technická Univerzita v Liberci cs
dc.publisher Technical university of Liberec, Czech Republic en
dc.relation.ispartof Ekonomie a Management cs
dc.relation.ispartof Economics and Management en
dc.relation.isbasedon Achim, M. V., & Borlea, N. S. (2014). Environmental performances – way to boost up financial performances of companies. Environmental engineering and management journal, 13(4), 991-1004.
dc.relation.isbasedon Achim, M. V., & Borlea, N. S. (2013). Corporate governance and business performances. LAP Lambert Academic Publishing Germany.
dc.relation.isbasedon Achim, M. V., & Borlea, N. S. (2012). Consideration on business risk bankruptcy. Review of Economic Studies and Research Virgil Madgearu, 5(2), 5-29.
dc.relation.isbasedon Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609. doi:10.2307/2978933.
dc.relation.isbasedon Altman, E. I. (2005). An Emerging Market Credit Scoring System for Corporate Bonds. Emerging Markets Review, 6(4), 311-323. doi:10.1016/j.ememar.2005.09.007.
dc.relation.isbasedon Altman, E. I., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyse and Invest in Distress Debt (3rd ed.). John Wiley and Sons.
dc.relation.isbasedon Agraval, 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. doi:10.1080/00014788.2007.9663313.
dc.relation.isbasedon Anghel, I. (2002). Bankruptcy Radiography and Predilection. Bucureşti: Economic Publishing House.
dc.relation.isbasedon Aziz, M. A., & Dar, H. A. (2006). Predicting Corporate Bankruptcy: Where We Stand? Corporate Governance, 6(1), 18-33. doi:10.1108/14720700610649436.
dc.relation.isbasedon 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/
dc.relation.isbasedon Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4(7), 1-111. doi:10.2307/2490171.
dc.relation.isbasedon Beaver, W. H., McNichols, M. F., & Rhie, J. W. (2005). Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy. Review of Accounting Studies, 10(1), 93-122. doi:10.1007/s11142-004-6341-9.
dc.relation.isbasedon Beaver, W. H., Correia, W. H., & McNichols, M. F. (2010). Financial statement analysis and the prediction of financial distress. Foundations & Trends in Accounting, 5(2), 99-102. doi:10.1561/1400000018.
dc.relation.isbasedon Brendea, G. (2014). Financing Behaviour of Romanian Listed Firms in Adjusting to the Target Capital Structure. Czech Journal of Economics and Finance, 64(4), 312-329.
dc.relation.isbasedon Bucharest Stock Exchange. (2010). Issuer’s guide of Stock and bonds. Retrieved from
dc.relation.isbasedon Cameron, A. C., & Trivery, P. K. (2009). Microeconometrics Using Stata. College Station, TX: A Stata Press Publication, StataCorp LP.
dc.relation.isbasedon Chen, K. H., & Shimerda, T. A. (1981). An Empirical Analysis of Useful Financial Ratios. Financial Management, 10(1), 51-60.
dc.relation.isbasedon Chi, L. C., & Tang, T. C. (2006). Bankruptcy Prediction: Application of Logit Analysis in Export Credit Risks. Australian Journal of Management, 31(1), 17-27. doi:10.1177/031289620603100102.
dc.relation.isbasedon Christidis, A. C., & Gregory, A. (2010). Some New Models for Financial Distress Prediction in the UK (Xfi - Centre for Finance and Investment Discussion Paper No. 10). Exeter: University of Exeter, Business school. doi:10.2139/ssrn.1687166.
dc.relation.isbasedon Circiumaru, D. (2011). The score models for analysing the bankruptcy risk. Some specific features for the case of Romania. The Young Economic Journal, 1(16), 153-160.
dc.relation.isbasedon Darayseh, M., Waples, E., & Tsoukalas, D. (2003). Corporate Failure for Manufacturing Industries Using Firms Specifics and Economic Environment with Logit Analysis. Managerial Finance, 29(8), 23-36. doi:10.1108/03074350310768409.
dc.relation.isbasedon Elenkov, D., & Fileva, T. (2006). Anatomy of a business failure: Accepting the "bad luck" explanation vs. proactively learning in international business. Cross Cultural Management, 13(2), 132-141. doi:10.1108/13527600610662311.
dc.relation.isbasedon Karas, M., & Reznakova, M. A. (2014). Parametric or nonparametric approach for creating a new bankruptcy prediction model: The evidence from the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences, 8(1), 214-223.
dc.relation.isbasedon Lee, M. C. (2014). Business Bankruptcy Prediction Based on Survival Analysis Approach. International Journal of Computer Science and Information Technology, 6(2), 103-119. doi:10.5121/ijcsit.2014.6207.
dc.relation.isbasedon Lee, W. C. (2007). Improving Financial Distress Prediction via Genetic Programming Decision Tree-Evidences form Taiwan. Journal of Statistics and Management Systems, 6(12), 1129-1149. doi:10.1080/09720510.2009.10701447.
dc.relation.isbasedon Laitinen, K., & Suvas, A. (2013). International Applicability of Corporate Failure Risk Models Based on Financial Statement Information: Comparisons across European Countries. Journal of Finance and Economics, 1(3). 1-26. doi:10.12735/jfe.v1i3p01.
dc.relation.isbasedon Marqués, A., & García, V., & Sánchez, J. S. (2013). On the suitability of resampling techniques for the class imbalance problem in credit scoring, Journal of the Operational Research Society, 64(7), 1060-1070. doi:10.1057/jors.2012.120.
dc.relation.isbasedon Ohlson, 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.isbasedon Platt, H. D., & Platt, M. B. (2008). Financial distress comparison across three global regions. Journal of Risk and Financial Management, 1(1), 129-162. doi:10.3390/jrfm1010129.
dc.relation.isbasedon Pitrová, K. (2011). Possibilities of the Altman Zeta model application to Czech firms. E&M Ekonomie a Management, 14(3), 66-76.
dc.relation.isbasedon Robu, M., Mironiuc, M., & Robu, I. B. (2012). A practical model for testing the hypothesis of "going-concern" in the financial audit mission for Romanian listed companies. Revista Audit Financiar, 2, 13-23.
dc.relation.isbasedon Šarlija, N., & Jeger, M. (2011). Comparing financial distress prediction models before and during recession. Croatian Operational Research Review, 2(1), 133-142.
dc.relation.isbasedon Siminica, M. (2010). Financial diagnosis. Publishing House .
dc.relation.isbasedon Shumway, T. (2001), Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 74(1), 101-124. doi:10.1086/209665.
dc.relation.isbasedon Szeverin, E. K., & Laszlo, K. (2014). The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector. Journal of Competitiveness, 6(2), 56-73. doi:10.7441/joc.2014.02.05.
dc.relation.isbasedon Todea, A., & Lazar, D. (2012). Global Crisis and Relative Efficiency: empirical evidence from Central and Eastern European Stock Markets. The Review of Finance and Banking, 4(1), 45-52.
dc.relation.isbasedon Tuvadaratragool, S. (2013). The role of financial ratios in signalling financial distress: evidence from Thai listed companies (Doctoral dissertation). Lismore: Southern Cross University (
dc.relation.isbasedon Ugurlu, M., & Aksoy, H. (2006). Prediction of corporate financial distress in an emerging market: the case of Turkey. Cross Cultural Management: An International Journal, 13(4), 277-295. doi:10.1108/13527600610713396.
dc.relation.isbasedon Wang, Y., & Campbell, M. (2010). Financial ratios and the prediction of bankruptcy: The Ohlson model applied to Chinese publicly traded companies. The Journal of Organizational Leadership and Business, 17(1), 334-338.
dc.rights CC BY-NC
dc.subject delisting en
dc.subject failure prediction en
dc.subject financial ratios en
dc.subject Logit model en
dc.subject.classification C25
dc.subject.classification C52
dc.subject.classification G33
dc.title Failure prediction from the investors’ view by using financial ratios. Lesson from Romania en
dc.type Article en
dc.publisher.abbreviation TUL
dc.relation.isrefereed true
dc.identifier.doi 10.15240/tul/001/2016-4-009
dc.identifier.eissn 2336-5604
local.relation.volume 19
local.relation.issue 4
local.relation.abbreviation E&M en
local.relation.abbreviation E+M cs
local.faculty Faculty of Economics
local.citation.spage 117
local.citation.epage 133
local.access open
local.fulltext yes

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