Preeti

Roll no- 13

MBA (3rd Sem)

Beaver Model

Altman’s Z Score Model

Wilcox Model

Blum Marc’s Failing Company Model

L.C Gupta Model

Five Models

The Failing Company Model (FCM) was developed by

Marc Blum to assess the probability of business failure.

The operational definition of failure used in this study was an in ability to pay debts as they become due, entrance into the bankruptcy or explicit agreement with creditors to reduce debts.

Analysis was applied to a paired sample of 115 failed and 115 non-failed firms to evaluate the predictive accuracy of the model.

Sampling was based on four criteria, utilised in following order – industry, sales, employees, and fiscal year.

Marc’s Blum Failing Company Model

Data drawn from balance sheets, income statement and stock market prices for

the consecutive prices of at least three years were collected for 115 companies which failed during the years 1954-1968 and matching 115 non-failed companies.

He treated the business firm as reservoir of financial resources and described its probability of failure in terms of expected flow of these resources.

According to this cash flow framework, other things being equal, the probability of failure is more likely:

- The smaller the reservoir- The smaller the inflow of resources from operations in both the short

run and long run- Larger the claims on resources by creditors- Greater the outflow of resources required by operations of business- The more ‘failure prone’ the industry location of the firm’s business

activities are expected to be.

Conti…

The FCM Model Prediction result

The model predicted the corporate failure with an accuracy of

approximately 94%, when failure occurred with in one year from the

date of prediction.

Accuracy of 80% for failure two years into the future.

Accuracy of 70% for failure three, four and five years distant.

Conti..

This study is done with Indian data, attempted to distinguish sick

and non-sick companies on the basis of financial ratios. The test was based on taking sample of sick and non-sick

companies, arraying them by the magnitude of each ratio to be tested, selecting a cut off point which will divide array into two classes with a minimum possible number of misclassification, then computing the percentage classification error.

The ratio which showed the least “ percentage classification error” at the earliest possible time was deemed to have the highest predictive power.

The sample included 41 textiles companies of which 21 were non-sick and 20 were sick to test the predictive power of 56 financial ratios.

The financial ratio computed for each of the company in sample for each year of 13 year period i.e 1964-74

L.C Gupta’s Work

The computation of “ percentage classification error”

In a mixed sample of 4 sick (S) and 4 Non-Sick (N) firms with their ROI (for a given year) arranged in an ascending order as follows:

-5 -2 3 4 6 8 10 12S S (N) S N N (S) N

The cut off point (arrow) is chosen in such a way that the number of misclassification (shown in parenthesis) is minimized.

The percentage classification error is 25% (2/8).

Conti…

L. C Gupta’s study revealed that EBDIT/ Sales OCF / Sales are the best ratios in predicting future bankruptcies.

These ratio had the least classification error among all the profitability ratios for the textile companies sample during 1962-64 .

* OCF = operating cash flow

Conti…

Thankyou