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Predicting Bankruptcy of Public and Private Companies in Mauritius


Affiliations
1 Department of Mathematics, University of Mauritius, Mauritius
2 University of Mauritius, Mauritius
     

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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.
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  • Predicting Bankruptcy of Public and Private Companies in Mauritius

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Authors

Jayrani Cheeneebash
Department of Mathematics, University of Mauritius, Mauritius
Nicholas Khim On Cheong Foo
University of Mauritius, Mauritius
Ashvin Gopaul
Department of Mathematics, University of Mauritius, Mauritius

Abstract


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