Read carefully the case notes overleaf. Consider the information shown in the appendix.
Determine how this information can be used to shed light on the usefulness of firm variables on evaluating the firm's credit worthiness.
Then write a report to the Directors of NewCredit which addresses these issues.
Firm specific information on the evaluation of credit worthiness
You have recently been appointed as an analyst within PMC Inc. PMC is a UK consultancy company that undertakes independent research for client organisations.
Your first client is a building society (NewCredit) which provides loans and financial services to both individuals and companies.
NewCredit provides loans to firms which are not necessarily sufficiently large to have a credit rating from one of the large credit rating agencies. NewCredit relies on its own credit experts which use their extensive experience to evaluate the credit worthiness of these firms. When a firm asks for a loan from NewCredit, a credit expert gives a credit score to that firm. Based on that credit score NewCredit decides whether or not to provide the loan to the firm. The credit score translates into a probability that the firm will not pay back the loan - the firm's probability of default. NewCredit chooses the interest rate to charge for the loan based on this default risk probability.
The problem of evaluating a firm's credit worthiness is very important in the finance sector. A good assessment of the firm's credit score is essential for the risk management of NewCredit's portfolio of clients. NewCredit is interested in identifying historical factors which have information about the credit worthiness of firms.
You have been asked to undertake some quantitative analysis looking at this issue. While you are familiar with statistics and several statistical packages you have not undertaken a project of this nature before. Hence you start by conducting a literature search.
This search proves beneficial and you find that there are a number of existing studies which look at credit scoring, but they do not use the NewCredit experts' knowledge. The best known studies estimate probabilities of default from the observation of the number of defaulted firms in large databases with long time series. NewCredit credit experts know well the particular type of firms which NewCredit usually works with and so their input might reveal different conclusions.
In previous empirical studies a number of factors have been identified as possible determinants of the probability of default, the most common being firm variables as: age, size, region, industry sector, debt to assets ratio, and market price to book value ratio.
From the material you have identified you draw up a list of variables which could influence a firm's probability of default. You then collect numerical data on each of these variables for a set of 300 firms randomly chosen from NewCredit's portfolio (details of the data can be found in the Appendix).
You now need to consider how you will analyse this information. In addition you need to consider how you will explain the approach(es) you have adopted and the implication of your analysis given that the Directors of NewCredit are not experts in quantitative or statistical methods.
Download:- Appendix.xlsx