An Analysis of the Predictors of Financial Distress for Zimbabwe Listed Corporates
Asian Journal of Probability and Statistics,
This study brings novelty to the area of corporate distress modelling in Zimbabwe by exploring company-specific indicators of corporate distress, unlike most of the previous studies, which used financial performance indicators. Using a binary logistic regression on a time series dataset collated between 2010 and 2017, this study establishes book value, book value per share, average debt to equity and equity per share as very signiﬁcant determinants of corporate distress on the Zimbabwe Stock Exchange (ZSE). Future studies incorporating artificial intelligence and a combination of both the traditional financial ratios and market-based indicators is recommended to expand the scope of the study.
- Financial distress
- listed corporates
How to Cite
Available at: http://unctad.org/en/PublicationsLibrary/WFE_UNCTAD_2017_en.pdf.
Chen R, Wong KA. The determinants of financial health of Asian insurance companies.Journal of Risk and Insurance. 2004;469–499.
Murthy BKV. Outward Foreign direct investment and economic development. Transnational Corporations Review. 2015;7(3):279–296.
White MJ. ‘The Corporate Bankruptcy Decision.Journal of Economic Perspectives. 1989;3(2):129–151. DOI: 10.1257/jep.3.2.129.
James S. Ang, Jess H. Chua, John J. McConnell. The administrative costs of corporate bankruptcy. American Finance Association. 2020;219–226.
Latif U. Early warning signs of financial distress in business : manage it before it is too. 2020;2020(8): 1–8.
Anokhina M, Felice VS, Pavia I. Department of Economics and Management DEM Working Paper Series Identifying SIFI Determinants for Global Banks and Insurance Companies : Implications for D-SIFIs in Russia Identifying SIFI Determinants for Global Banks and Insurance Companies : Implicatio.2014;85(September).
African markets. Astra Industries to delist from Zimbabwe Stock Exchange;2015. viewed 12 November 2020.
Altman EI, Marco G, Varetto F. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance. 1994;18(3):505–529.
Lee S, Choi WS. A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Systems with Applications. 2013;40(8):2941–2946.
Wang G,et al. Financial distress prediction: Regularized sparse-based Random Subspace with ER aggregation rule incorporating textual disclosures.Applied Soft Computing Journal. 2020;90:106152.
Mousavi MM, Ouenniche J. Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions.Annals of Operations Research. 2018;271(2):853–886.
Brozyna J, Mentel G, Pisula T. Statistical methods of the bankruptcy prediction in the logistics sector in Poland and Slovakia.Transformations in Business and Economics. 2016;15(1):93–114.
Hauser RP, Booth D. Predicting bankruptcy with robust logistic regression. Journal of Data Science.2011;9:565–584.
Altman EI, Narayanan P. An international survey of business failure classification models.Financial Markets, Institutions and Instruments. 1997;6(2):1–57.
Argenti J. Corporate planning and corporate collapse.Long Range Planning. 1976;9(6):12–17.
Zizi Y, Oudgou M, Moudden A. El. Determinants and predictors of smes’ financial failure: A logistic regression approach.Risks.2020;8(4):1–21.
Abdulkareem H. The revised Altman Z’-score Model Verifying its Validity as a Predictor of Corporate Failure in the Case of UK Private Companies. Conference. (January); 2015.
Shumway T. Forecasting bankruptcy more accurately: A simple hazard model.Journal of Business.2001;74(1):101–124.
Kay R, Little S. Transformations of the explanatory variables in the logistic regression model for binary data’, Biometrika. 1987;74(3):495-501.
Okereke E. Effect of transformation on the parameter estimates of a simple linear regression model: a case study of division of variables by constants. Asian Journal of Mathematics & Statistics. 2011;4.
Sarkar SK, Midi H.Importance of assessing the model adequacy of binary logistic regression. J, Applied Sci.2010;10.
Sweeney R, Ulveling E. A transformation for simplifying the interpretation of the coefficients of binary variables in Regression analysis.The American Statistician. 2012;26.
Stoltzfus J. 'Logistic regression: a brief primer. National Library of Medicine; 2011.
Nehrebecka N. Predicting the default risk of companies. comparison of credit scoring models: Logit Vs support vector machines. Econometrics.2018;22(2):54–73.
Manes Rossi F, Zito M, Costanzo A. ‘How to Prevent Distress in Local Government: A New Model Applied in Italy. 2012;1–5.
Matenda F, Sibanda M, Chikodza E, Gumbo V. Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research. Management Review Quarterly; 2021.
Cortina JJ, Didier T, Schmukler SL. 'Corporate borrowing in emerging markets: fairly long term, but only for a few. Research and Policy Briefs, World Bank Group. 2018;8.
Abstract View: 35 times
PDF Download: 13 times