Abstract:
One of the major drawbacks for the economy is corporate bankruptcy since it is far considered restricting element for the growth of any economy. In fact, from a very long time, for the prediction of bankruptcy, Z-Score values have been continuously used and it has been considered crucial to the investors and lenders because their returns are primarily based on solvency estimates.
Nowadays, everything has changed and asking for loans from respective banks has taken direction. The fundamental challenge is to consider the conditions of loans from the banks and to make sure safe and secure repayment by them as this model technique has been continuously used as an effective measure to compare and form an opinion about the companies regarding their credibility being up to the mark or not.
Our research project will help us to find the Z-Score values for the PSX’s 100 index banks in Pakistan which include Habib Bank Limited, Meezan Bank Limited, Allied Bank Limited, Askari Bank Limited, Bank A1 Habib, Bank Alfalah, Bank of Punjab, Faysal Bank Limited, Habib Metropolitan Bank Limited, MCB Bank Limited, Standard Chartered Bank Limited, National Bank of Pakistan and United Bank Limited.
When these banks ask for loans from the State Bank of Pakistan or from any other funding agency, the worth of Z-Score cannot be undermined. Even the use of other parameters for checking the network which is neutral can be used further to forecast the undefined internal areas of Z-Score and then for forecasting the Z-Score values of the above mentioned thirteen banks, their internal parameters are used.
The target population of this project is PSX’s 100 index banks in Pakistan. We extracted the secondary data from the latest financial statements and audited annual reports of 2018 and then also went through the respective websites and related articles for the defined research period. The annual financial statements of all the banks included the balance sheet and income statement. In this project, advance excel program is used for analyzing the collected data.
