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Cheeneebash, Jayrani
- Predicting Bankruptcy of Public and Private Companies in Mauritius
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Authors
Affiliations
1 Department of Mathematics, University of Mauritius, MU
2 University of Mauritius, MU
1 Department of Mathematics, University of Mauritius, MU
2 University of Mauritius, MU
Source
International Journal of Banking, Risk and Insurance, Vol 9, No 1 (2021), Pagination: 1-11Abstract
During the recent years, many incentives have been given to encourage people to become entrepreneurs and set up their own businesses or companies in Mauritius. This has contributed to an increase in the number of companies listed on the Stock Exchange of Mauritius. The aim of this paper is to provide prospective investors a means to determine the tendency of companies to go bankrupt. A comparison is made of the performance of the artificial neural networks method with the Altman Z-Score and the Taffler models on their ability to predict whether companies listed in Mauritius are prone to bankruptcy. The Z-Score and Taffler models use five and four financial ratios as variables, respectively, whereas the simulation of the ANN is carried out with only two different sets of inputs. After testing the three methods, their respective accuracy is recorded; it is found that ANN produced better results than both the Z-Score and Taffler Models.Keywords
Bankruptcy, Altman Model, Taffler Model, Artificial Neural Networks, Prediction.
References
- Agarwal, V., & Taffler, R. (2007). Twenty-five years of the taffler Z-Score model: Does it really have predictive ability? Accounting and Business Research, 37(4), 285-300.
- Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
- Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.
- Coats, P. K., & Fant, L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 22(3), 142-155.
- Conan, J., & Holder, M. (1979). Variables explicatives de performances et controle de gestion dans les P.M.I. Université Paris Dauphine.
- Fletcher, D., & Goss, E., (1993). Forecasting with neural networks: An application using bankruptcy data. Information & Management, 24, 159-167.
- Fulmer, J., Moon, J., Gavin, T., & Erwin, J. (1984). A bankruptcy classification model for small firms. The Journal of Commercial Bank Lending, 66(11), 25-37.
- McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133.
- Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131.
- Shumway, T. (2001). Forecasting bankruptcy more accurate: A simple hazard model. The Journal of Business, 74(1), 101-124.
- Springate, G. L. V. (1978). Predicting the possibility of failure in a Canadian firm: A discriminant analysis (Unpublished M.B.A. research project). Simon Fraser University.
- Taffler, R. J. (1984). Empirical models for the monitoring of UK corporations. Journal of Banking and Finance, 8, 199-227.
- Vassalou, M., & Xing, Y. (2004). Default risk in equity returns. The Journal of Finance, 59(2), 831-868.
- Wruck, K. H. (1990). Financial distress, reorganization, and organizational efficiency. Journal of Financial Economics, 27(2), 419-444.
- Zmijewski, M. E. (1984). Methodological issues related to the estimation if financial distress prediction models. Journal of Accounting Research, 22, 59-82.