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

Simple item page
dc.contributor.authorAchim, Monica Violeta
dc.contributor.authorBorlea, Sorin Nicolae
dc.contributor.authorGăban, Lucian Vasile
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2016-12-05
dc.date.available2016-12-05
dc.date.issued2016-12-05
dc.description.abstractThe 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.extent117-133 s.cs
dc.identifier.doi10.15240/tul/001/2016-4-009
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/19273
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonAchim, 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.isbasedonAchim, M. V., & Borlea, N. S. (2013). Corporate governance and business performances. LAP Lambert Academic Publishing Germany.
dc.relation.isbasedonAchim, 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.isbasedonAltman, 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.isbasedonAltman, 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.isbasedonAltman, 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.isbasedonAgraval, 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.isbasedonAnghel, I. (2002). Bankruptcy Radiography and Predilection. Bucureşti: Economic Publishing House.
dc.relation.isbasedonAziz, M. A., & Dar, H. A. (2006). Predicting Corporate Bankruptcy: Where We Stand? Corporate Governance, 6(1), 18-33. doi:10.1108/14720700610649436.
dc.relation.isbasedonBalcaen, 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.2005.09.001.
dc.relation.isbasedonBeaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4(7), 1-111. doi:10.2307/2490171.
dc.relation.isbasedonBeaver, 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.isbasedonBeaver, 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.isbasedonBrendea, 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.isbasedonBucharest Stock Exchange. (2010). Issuer’s guide of Stock and bonds. Retrieved from http://www.bvb.ro/info/Ghid%20admitere%20BVB%20iunie%202010.pdf.
dc.relation.isbasedonCameron, A. C., & Trivery, P. K. (2009). Microeconometrics Using Stata. College Station, TX: A Stata Press Publication, StataCorp LP.
dc.relation.isbasedonChen, K. H., & Shimerda, T. A. (1981). An Empirical Analysis of Useful Financial Ratios. Financial Management, 10(1), 51-60.
dc.relation.isbasedonChi, 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.isbasedonChristidis, 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.isbasedonCirciumaru, 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.isbasedonDarayseh, 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.isbasedonElenkov, 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.isbasedonKaras, 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.isbasedonLee, 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.isbasedonLee, 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.isbasedonLaitinen, 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.isbasedonMarqués, 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.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.isbasedonPlatt, 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.isbasedonPitrová, K. (2011). Possibilities of the Altman Zeta model application to Czech firms. E&M Ekonomie a Management, 14(3), 66-76.
dc.relation.isbasedonRobu, 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.isbasedonŠarlija, N., & Jeger, M. (2011). Comparing financial distress prediction models before and during recession. Croatian Operational Research Review, 2(1), 133-142.
dc.relation.isbasedonSiminica, M. (2010). Financial diagnosis. Publishing House .
dc.relation.isbasedonShumway, T. (2001), Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 74(1), 101-124. doi:10.1086/209665.
dc.relation.isbasedonSzeverin, 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.isbasedonTodea, 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.isbasedonTuvadaratragool, S. (2013). The role of financial ratios in signalling financial distress: evidence from Thai listed companies (Doctoral dissertation). Lismore: Southern Cross University (http://epubs.scu.edu.au/cgi/viewcontent.cgi?article=1291&context=theses).
dc.relation.isbasedonUgurlu, 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.isbasedonWang, 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.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectdelistingen
dc.subjectfailure predictionen
dc.subjectfinancial ratiosen
dc.subjectLogit modelen
dc.subject.classificationC25
dc.subject.classificationC52
dc.subject.classificationG33
dc.titleFailure prediction from the investors’ view by using financial ratios. Lesson from Romaniaen
dc.typeArticleen
local.accessopen
local.citation.epage133
local.citation.spage117
local.facultyFaculty of Economics
local.fulltextyes
local.relation.abbreviationE&Men
local.relation.abbreviationE+Mcs
local.relation.issue4
local.relation.volume19
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
EM_4_2016_9.pdf
Size:
1.2 MB
Format:
Adobe Portable Document Format
Description:
článek
Download
Collections
Číslo 4