The main characteristic of this model is its reliance on financial ratios. A statistical technique is used in order to assign risk weights to several financial ratios that differentiate between defaulting and successful companies. For example, 22 financial ratios were tested while developing the Altman Model (1968), which is widely used both in the academic literature and in practice.
A logit model is a popular statistical model, which is used widely for the measurement of PD for corporate customers, mainly for two reasons. First, the output from the logit model can be directly interpreted as the probability of default. Second, this model can be verified easily. Hence, recommendation is to use a logistic model.
The event of default must be clearly defined. Historically, the definition used for rating models was bankruptcy, as this information was readily available and this type of model is powerful in predicting. However, the definition of default may include delays in payments and other situations in which the bank does not receive full payment.
Ratios are calculated to standardize the available information. For example, the ratio “Earnings per Total Assets” enables to compare the profitability of firms of different sizes. In addition to calculating ratios that reflect different financial aspects of the borrowers, dynamic ratios that compare current and past levels of particular balance sheet items can be extremely useful in predicting the event of default. Input ratios represent the most important credit risk factors (leverage, liquidity, productivity, turnover, level of activity, profitability, firm size, growth rates and leverage development).
Total Liabilities/Total Assets
Bank Debt/Total Assets
Short Term Debt/Total Assets
Current Assets/Curent Liabilities
Accounts Receivable/Net Sales
Accounts Payable/Net Sales
(Net Sales-Material Costs)/Person Costs
Net Sales/Total Assets
Ordinary Business Income/Total Assets
Net Sales/Net Sales Last Year
Total Liabilities/Liabilities Last Year