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Financial Distress Models of Determining a Firm’s Bankruptcy

Autor:   •  January 22, 2018  •  1,492 Words (6 Pages)  •  692 Views

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The Logit Model. This model is also one of the models that help to determine bankruptcy. According to Trinity University the application of the logit analysis model requires four steps in order to determine bankruptcy. First step is to take a series of several financial ratios and calculate them. Second is to then take those ratios found and multiply them by the coefficient unique to that particular ratio. The coefficient that is found may either be positive or negative. Third, the resulting values found are then summed together. Finally after all sums are found the probability of bankruptcy for a firm is calculated as the inverse of (1 + ey). Variables that can’t be explained with a negative coefficient increase the probability of bankruptcy because they reduce the ey found to zero, with the result that the bankruptcy probability function approaches 1/1, or 100 percent. If the independent variables with a positive coefficient decrease the probability of bankruptcy.

The H Score Model. This model can be compared to the performance analysis score ranks firms percentile score between 0 and 100. The minimum score a company should receive is a 25 which is described as being the warning area. The score reflects that only 25% of companies have characteristics even more indicative of the failing of a company so therefore corporate failure is a real concern.

Zeta Model. The Zeta Model addresses some of the problems that are associated with the Z score model. The mathematical formula was created by Edward Altman in the 1960’s. The Zeta Model helps to express the chance of a public company going bankrupt within a two year time period. The number that is produced from the following formula (Z=1.2A+1.4B+3.3C+0.6D+1.0E) is referred to the z-score which is the predictor of future bankruptcy. Each coefficient represents: Z reflects the score, A the working Capital/total Assets, B retained Earnings/total Assets, C earnings Before Interest & tax/total Assets, D market Value of equity/total Liabilities and E the sales/total Assets. After plugging in all the required information the z-score will be either low or high. For instance if the z-score is 1.8 or lower the company is more likely to go bankrupt. A score greater than 3.0 indicate bankruptcy is unlikely to happen in the next two years. The companies with z-scores between 1.8 & 3.0 are in the gray area which means bankruptcy is not easily predicted one way or the other.

Taffler and Tishaw ‘s model. This model also helps to predict bankruptcy created in 1977. This model developed its own version of the Z-score using a combination of four ratios. It uses financial ratios weighted in order to solve for the predictive power of the model. This model also produces a z-score sum. As explained in Taffler (1983), the first stage in building this model was to compute over 80 carefully selected ratios from the accounts of all listed industrial firms failing between 1968 and 1976 and 46 randomly selected solvent industrial firms. Using, inter alia, the z-score model was derived by determining the best set of ratios which, when taken together and appropriately weighted, distinguished optimally between the two samples that are tested.

The Performance analysis. The score ranks all companies Z scores within a percentile terms measuring relative performance from 0 to 100. Any downward trend overtime should be investigated.

The Beaver’s Univariate Model. This model assess the financial status of a company reviewing one ratio at a time. The model uses a single variable and financial ratios to forecast financial failure created in 1966. This is a simple but flawed model that asses the financial status of a company by steadily reviewing one ratio at a time.

Sources:

http://what-when-how.com/finance/corporate-failure-definitions-methods-and-failure-prediction-models-finance/

http://www.psychstat.missouristate.edu/multibook/mlt03.htm

B. Yildiz, Prediction of Financial Failure with Artificial Neural Network Technology and an Emprical Application on Publicly Held Companies, ISE Review, 17, (2001), 47-62

http://www.investopedia.com/terms/z/zeta_model.asp

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