The application of KMV-Merton model and Altman Z-Score model in determining credit score of firms / 'Afifah Filza Ahmad Rubaa'i, Nurul Atiqah Syazwani Samsudin and Humairah 'Aisyah Azmi

• Main Authors: Ahmad Rubaa'i, 'Afifah Filza, Samsudin, Nurul Atiqah Syazwani, Azmi, Humairah 'Aisyah
• Format: Student Project
• Language: English
• Published: 2018
• Subjects: Mathematical statistics. Probabilities; Data processing; Analytical methods used in the solution of physical problems
• Online Access: https://ir.uitm.edu.my/id/eprint/49501/1/49501.pdf; https://ir.uitm.edu.my/id/eprint/49501/

Most of the firms' managers believe that their firms are in a good condition as they are able to generate profits. They are not aware that the obligations holds on debts or liabilities will probably cause them to default. Default is an element of the credit risk where it is defined as a risk that arises due to the failure to make required payments. Identifying the possibility of a firm to default is significant in maintaining good financial health of firms. Default will likely fall into bankruptcy. Bankruptcy may highly occur when the firms continuously suffer from losses. In specific, the default and bankruptcy are related as the higher default will results in the higher probability of bankruptcy. Therefore, default prediction is extremely needed to avoid the firm's bankruptcy. KMV-Merton model and Altman Z-Score model are introduced in this study as the default predictive models. The models are implemented in this study to predict the probability of default alongside to predict the probability of bankruptcy. Data of the 16 selected firms is collected within 2 years, which is from 2016 to 2017. The predicted probability of default and the predicted probability of bankruptcy are analysed its compatibility by comparing the results. The firms' credit scores are also determined by applying the FRISK® score developed by CreditRiskMonitor. The result of this study is that the probability of default and the probability of bankruptcy for the 16 firms for the year 2017 are clearly predicted where 56.25% of the firms are in the best conditions while 18.75% of the firms are worst. Besides, the credit score of the firms are determined from the range of 1 to 10. These predictive models are significance to be applied to estimate the future financial conditions of the firm's. This study gives opportunity to the firms to secure their financial condition.