Distressed Company Prediction using Logistic Regression: Tunisians Case
Keywords:
distressed firms, forecasting model, logistic regression model
Abstract
In this study, we try to develop a model for predicting corporate default based on a logistic regression (logit) and applied to the case of Tunisia. Our sample consists of 212 companies in the various industries (106 companies 'healthy' and 106 companies "distressed") over the period 2005-2010. The results of the use of a battery of 87 ratios showed that 12 ratios can build the model and that liquidity and solvency have more weight than profitability and management in predicting the distress. Both on the original sample and the control one, these results are good either in terms of correct percentage of classification or in terms of stability of discriminating power over time (on, two and three years before the distress) and space.
Downloads
- Article PDF
- TEI XML Kaleidoscope (download in zip)* (Beta by AI)
- Lens* NISO JATS XML (Beta by AI)
- HTML Kaleidoscope* (Beta by AI)
- DBK XML Kaleidoscope (download in zip)* (Beta by AI)
- LaTeX pdf Kaleidoscope* (Beta by AI)
- EPUB Kaleidoscope* (Beta by AI)
- MD Kaleidoscope* (Beta by AI)
- FO Kaleidoscope* (Beta by AI)
- BIB Kaleidoscope* (Beta by AI)
- LaTeX Kaleidoscope* (Beta by AI)
How to Cite
References
J. Ahn, al (2011) Trade Finance and the Great Trade Collapse. 101(3), 298-302. https://doi.org/10.1257/aer.101.3.298
A. Albert, E. Lesaffre (1986) Multiple group logistic discrimination. 12(2), 209-224. https://doi.org/10.1016/0898-1221(86)90074-x
Edward Altman (1968) FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY. 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
E. Altman, al (1977) Zeta Analyses : A new model to identify bankruptcy risk of corporations. 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
E. Altman, al (1994) Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). 18(3), 505-529. https://doi.org/10.1016/0378-4266(94)90007-8
E. Altman, al (2005) The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications*. 78(6), 2203-2228. https://doi.org/10.1086/497044
J. Anderson, V. Blair (1982) Penalized maximum likelihood estimation in logistic regression and discrimination. 69(1), 123-136. https://doi.org/10.1093/biomet/69.1.123
J. Anderson (1972) Separate sample logistic discrimination. 59(1), 19-35. https://doi.org/10.1093/biomet/59.1.19
A. Aziz, al (1988) BANKRUPTCY PREDICTION ‐ AN INVESTIGATION OF CASH FLOW BASED MODELS [1]. 25(5), 419-437. https://doi.org/10.1111/j.1467-6486.1988.tb00708.x
B. Back (1996) Neural networks and genetic algorithms for bankruptcy predictions. 11(4), 407-413. https://doi.org/10.1016/s0957-4174(96)00055-3
M. Bardos (1984) « Le risque de défaillance d'entreprise » Cahiers économiques et monétaires de la banque de France, p. 190.
Mireille Bardos (1998) Detecting the risk of company failure at the Banque de France. 22(10-11), 1405-1419. https://doi.org/10.1016/s0378-4266(98)00062-4
M. Bardos, W. Zhu (1997) Comparaison de l'analyse discriminante linéaire et des réseaux de neurones: Application à la détection de défaillance d'entreprises. 45(4), 65-92.
M. Bardos (1989) Trois méthodes d'analyse discriminante. 33, 151-190.
Ran Barniv, James Mcdonald (1992) Identifying financial distress in the insurance industry: A synthesis of m & hodological and empirical issues. 59(4), 543-543. https://doi.org/10.2307/253344
Ran Barniv, Robert Hershbarger (1990) Classifying Financial Distress in the Life Insurance Industry. 57(1), 110. https://doi.org/10.2307/252927
William Beaver (1966) Financial Ratios As Predictors of Failure. 4, 71. https://doi.org/10.2307/2490171
M. Blum (1974) Failing Company Discriminant Analysis, Journal of Accounting Research. 12, 1.
M. Boyacioglu, al (2009) Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. 36(2), 3355-3366. https://doi.org/10.1016/j.eswa.2008.01.003
D. Burgstahler, al (1989) Changes in the probability of bankruptcy and equity value. 11(2-3), 207-224. https://doi.org/10.1016/0165-4101(89)90006-2
C. Charalambous, al (2000) Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction. 99(1-4), 403-425. https://doi.org/10.1023/a:1019292321322
A. Charitou, al (2004) Predicting corporate failure: empirical evidence for the UK. 13(3), 465-497. https://doi.org/10.1080/0963818042000216811
J. Chen, al (2006) Financial Distress Prediction in China. 09(02), 317-336. https://doi.org/10.1142/s0219091506000744
J. Conan, M. Holder (1979) Variables explicatives de performances & contrôle de gestion dans les PMI.
D. Cox (1970) Analysis of binary data, 1970. 103-108.
N. Day, D. Kerridge (1967) A General Maximum Likelihood Discriminant. 23(2), 313. https://doi.org/10.2307/2528164
Edward Deakin (1972) A Discriminant Analysis of Predictors of Business Failure. 10(1), 167. https://doi.org/10.2307/2490225
A. Dimitras, al (1999) Business failure prediction using rough sets. 114(2), 263-280. https://doi.org/10.1016/s0377-2217(98)00255-0
Dwyer J (1992) Changes in the Helping Behaviors of Adult Children as Caregivers. 14(3), 351-375. https://doi.org/10.1177/0164027592143004
Robert Edmister (1972) An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction. 7(2), 1477. https://doi.org/10.2307/2329929
P. Fitzpatrick (1932) A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firms. 1932, 598-605.
J. Flagg, al (1991) PREDICTING CORPORATE BANKRUPTCY USING FAILING FIRMS. 1(1), 67-78. https://doi.org/10.1002/j.1873-5924.1991.tb00543.x
G. Gloubos, Grammatikos Success of bankruptcy prediction models in Greece. Studies in Banking and Finance: International business failure prediction models. 7, 37-46.
J. Haehl (1981) « Les techniques de renflouement des entreprises en difficulté », Librairies techniques.
M. Holder (1984) Le score de l'entreprise.
Keith Houghton, Ratna Sengupta (1984) The Effect of Prior Probability Disclosure and Information Set Construction on Bankers' Ability to Predict Failure. 22(2), 768. https://doi.org/10.2307/2490679
S. Jones, D. Hensher (2007) Forecasting corporate bankruptcy: optimizing the performance of the mixed logit model. 43(3), 241-264.
Stewart Jones, David Hensher (2007) Modelling corporate failure: A multinomial nested logit analysis for unordered outcomes. 39(1), 89-107. https://doi.org/10.1016/j.bar.2006.12.003
Stewart Jones, David Hensher (2004) Predicting Firm Financial Distress: A Mixed Logit Model. 79(4), 1011-1038. https://doi.org/10.2308/accr.2004.79.4.1011
Myoung-Jong Kim, Dae-Ki Kang (2012) Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction. 39(10), 9308-9314. https://doi.org/10.1016/j.eswa.2012.02.072
Kira, Doreen Ngnyen, D. (1997) « An application of artificial neutral networks and statistical methods in qualitative evaluation of small business loans » ASMD Summer, April.
Erkki Laitinen, Teija Laitinen (1998) Cash Management Behavior and Failure Prediction. 25(7-8), 893-919. https://doi.org/10.1111/1468-5957.00218
E. Laitinen, T. Laitinen (2001) Bankruptcy prediction: application of the Taylor's expansion in logistic regression. 9(4), 327-349.
Amy Lau, -L (1987) A Five-State Financial Distress Prediction Model. 25(1), 127. https://doi.org/10.2307/2491262
Hui Li, Jie Sun (2011) Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction. 38(5), 6244-6253. https://doi.org/10.1016/j.eswa.2010.11.043
Mo. Mahmood, Edwared Lawrence (1987) A PREFORMANCE ANALYSIS OF PARAMETRIC AND NONPARAMETRIC DISCRIMINANT APPROACHES TO BUSINESS DECISION MAKING. 18(2), 308-326. https://doi.org/10.1111/j.1540-5915.1987.tb01524.x
D. Martin (1977) Early warning of bank failure: A logit regression approach. 1(3), 249-276.
Yaw Mensah (1984) An Examination of the Stationarity of Multivariate Bankruptcy Prediction Models: A Methodological Study. 22(1), 380. https://doi.org/10.2307/2490719
C. Merwin (1942) Financing Small Corporations in Five Manufacturing Industries. 605, 727-731.
J. Min, Y.-C Lee (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. 28(4), 603-614. https://doi.org/10.1016/j.eswa.2004.12.008
C. Mossman, al (1998) An empirical comparison of bankruptcy models. 33(2), 35-54. https://doi.org/10.1111/j.1540-6288.1998.tb01367.x
Joo‐ha Nam, Taehong Jinn (2000) Bankruptcy Prediction: Evidence from Korean Listed Companies during the IMF Crisis. 11(3), 178-197. https://doi.org/10.1111/1467-646x.00061
James Ohlson (1980) Financial Ratios and the Probabilistic Prediction of Bankruptcy. 18(1), 109. https://doi.org/10.2307/2490395
D. Olson, al (2012) Comparative analysis of data mining methods for bankruptcy prediction. 52(2), 464-473. https://doi.org/10.1016/j.dss.2011.10.007
M. Peel, D. Peel (1987) Some Further Empirical Evidence on Predicting Private Company Failure. 18(69), 57-66. https://doi.org/10.1080/00014788.1987.9729348
Harlan Platt, Marjorie Platt (1991) A note on the use of industry-relative ratios in bankruptcy prediction. 15(6), 1183-1194. https://doi.org/10.1016/0378-4266(91)90057-s
J. Ramser, L. Foster (1931) A Demonstration of Ratio Analysis. 52.
P. Rose, G. Giroux (1984) Predicting Corporate Bankruptcy: An Analytical and Empirical Evaluation. 2, 1-12.
Carlos Serrano-Cinca, Begoña Gutiérrez-Nieto (2013) Partial Least Square Discriminant Analysis for bankruptcy prediction. 54(3), 1245-1255. https://doi.org/10.1016/j.dss.2012.11.015
R. Taffler (1982) Forecasting Company Failure in the UK Using Discriminant Analysis and Financial Ratio Data. 145(3), 342. https://doi.org/10.2307/2981867
R. Taffler (1983) The Assessment of Company Solvency and Performance Using a Statistical Model, Accounting and Business Research. 13.
Kar Tam, Melody Kiang (1992) Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. 38(7), 926-947. https://doi.org/10.1287/mnsc.38.7.926
H. Tserng, al (2011) An enforced support vector machine model for construction contractor default prediction. 20(8), 1242-1249. https://doi.org/10.1016/j.autcon.2011.05.007
Varetto F (1998) Genetic algorithms applications in the analysis of insolvency risk. 22.
Gang Wang, Jian Ma, Shanlin Yang (2014) An improved boosting based on feature selection for corporate bankruptcy prediction. 41(5), 2353-2361. https://doi.org/10.1016/j.eswa.2013.09.033
Jarrod Wilcox (1973) A Prediction of Business Failure Using Accounting Data. 11, 163. https://doi.org/10.2307/2490035
A. Winakor, R. Smith (1935) Changes in the Financial Structure of Unsuccessful Industrial Corporations. 44.
C. Wu, al (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. 32(2), 397-408. https://doi.org/10.1016/j.eswa.2005.12.008
Q. Yu, al (2014) Bankruptcy prediction using Extreme Learning Machine and financial expertise. 128, 296-302. https://doi.org/10.1016/j.neucom.2013.01.063
Christine Zavgren (1985) ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS. 12(1), 19-45. https://doi.org/10.1111/j.1468-5957.1985.tb00077.x
R. Zeitun (2007) Default probability for the Jordanian companies: A test of cash flow theory. 8, 147-162.
C. Zopounidis (1995) Evaluation du risque de défaillance de l'entreprise: Méthodes et cas d'application.
Published
2015-03-10
Issue
Section
License
Copyright (c) 2015 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.