Evaluation of Financial Distress

Beaver Model:

William H. Beaver proposed this model (Beaver Model) in 1966. Beaver demonstrated that financial ratios can be useful in the prediction of an individual firm failure, financial distress and bankruptcy prediction models. Bankruptcies, bond defaults, overdrawn bank accounts, and firms that omitted payment of preferred stock dividend are failed firms. Failure is the inability of a firm to pay its financial obligations as they mature.

In this model, the seventy-nine failed firms were identified from Moody’s Industrial Manual during the time period of 1954 to 1964.The failed firms had their asset size range from $ 0.6 million to $ 45 million with a mean of approximately $ 6 millions. Beaver matched samples of ‘failed firms’ and ‘non-failed’ firms and found that the majority of the seventy-nine failed firms operated in the manufacturing type of business. Beaver found that several easily available financial ratios were good predictors of failure while others, probably widely used, were mediocre predictors.

The criterion ratios such as:

  • Cash Flow/Total Assets
  • Net Income/Total Assets
  • Total debt/ Total Assets
  • Cash Flows/Total Debts were good predictors of failure even up to five years of events.

Widely used ratios as the ‘current’ ratio were of only mediocre value until the final year   before failure.

Blum Marc’s Failing Company Model (Failing Company Model)

The Failing Company Model (FCM) was developed by Marc Blum to assess the profitability of business failure. The operational definition of failure used in the study was an inability to pay debts as they become due, entrance into the bankruptcy or explicit agreement with creditors to reduce debts.

The analysis was applied to a paired sample of 115 failed and 115 non-failed firms to evaluate the predictive accuracy of the model. The selection and sample was based on four criteria,   utilized in the following order: Industry, Sales, Employees and Fiscal Year. The data were   drawn from financial statements and stock prices for the consecutive prices of at least   three years were collected for 155 companies which failed during the year 1954-1968    and matching 155 non-failed companies.

Blum Marc treated the business firm as a reservoir of financial resources and described its   probability of failure in terms of expected flow of these resources. From Cash Flow Framework, other 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.

FCM Model predicted that the corporate failure with an

  • Accuracy of approximately 94 % when failure occurred within one year from the date of prediction.
  • 80% for failure within two year from the date of prediction.
  • 70% for failure within three, four and five years distant.

L.C. Gupta Model

L.C. Gupta Model is another model of understanding Financial Distress. This model made an attempt to examine survival strength of the company derived from the concept of marginal firms. This study is based in Indian data, attempting to distinguish sick and non-sick companies on the basis of financial ratios.

The sample taken was of 41 cotton textile companies (20 sick and 21 non-sick) and 39 non-textile companies (18 sick and 21 non-sick) and both type of companies were matched  on the basis of product manufactured, age and size measured in terms of paid-up capital,   assets and sales for the period of 1962-1974. This model  tested 56 financial ratio for making     the prediction of distress.

 L.C. Gupta Model Analysis

  • Take a sample of Sick and Non-Sick companies.
  • Arrange them in the ascending or descending order by the magnitude of the ratio.
  • Select a cut-off point carefully which will divide the array into two classes with a minimum possible number of misclassification.
  • Compute the percentage of classification error.
  • The ratio which shows the least “percentage classification error” at the earliest possible time is deemed to have the highest predictive power.

The model recommended a combination of the following four major ratios in order to minimize the classification error rate:

  • X1 = 𝑬𝑩𝑫𝑰𝑻 𝑺𝒂𝒍𝒆𝒔
  • X2 = 𝑶𝒑𝒆𝒓𝒂𝒕𝒊𝒏𝒈 𝑪𝒂𝒔𝒉 𝑭𝒍𝒐𝒘 𝑺𝒂𝒍𝒆𝒔
  • X3 = 𝑬𝑩𝑫𝑰𝑻 𝑻𝒐𝒕𝒂𝒍 𝑨𝒔𝒔𝒆𝒕
  • X4 = 𝑬𝑩𝑫𝑰𝑻 𝑰𝒏𝒕𝒆𝒓𝒆𝒔𝒕+𝟎.𝟐𝟓 𝑫𝒆𝒃𝒕

 L.C. Gupta Model Observation

  • The ratios relating to net worth were found to be the worst predictor of bankruptcy among profitability ratios.
  • Among balance sheet ratios, the solvency ratios were more reliable indicators of strength than any liquidity ratios.
  • The model also observed that companies with an inadequate equity base are more sickness prone.

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