A Framework for Financial Statement Analysis Part 5: Prediction of Financial Distress.
By GALLINGER, GEORGE W.
Publication: Business Credit
Date: Sept 2000
Subject: Credit ratings (Methods), Credit management (Methods), Financial analysis (Methods), Business losses (Forecasts and trends)
Product: Credit Management
Location: United States
Sorry I missed getting this segment of the financial analysis series into the June and July/August editions of Business credit. Given the three-month lapse, hopefully, you've been able to practice your analytical and interpretative skills. Honestly, the skills you've been learning from the prior articles are more important than the topic I'm going to examine here--financial distress scores. In my opinion, too many credit professionals use scoring techniques of one sort or another in place of meaningful analysis. The common approach is to look at the financial distress (or credit) score, see what range it is in--good, bad or indifferent--and then decide on an appropriate action. You need to keep in mind that most scoring models are based on statistical relationships and they may have a significant level of error associated with them.
Let me give you an analogy. I live in Phoenix, Arizona. When I go to bed, I can predict with a high degree of accuracy that the sun will shine tomorrow. I could use historical weather data to derive a statistical model that would tell me the probability of the sun shining on any particular day in Phoenix. And rest assured that the model would do a very good job of predicting another glorious day. However, can I use the same model to predict whether the sun will shine in Seattle, Chicago, New York or Washington? Of course I can't.
The problem I have in taking my sunshine forecasting model to other parts of the country also applies to scoring trade credit accounts. What are some of the important factors causing a credit scoring model to lose, at least, some of its predictive credibility? For...