The authors explore the question of bankruptcy in public companies, trying to come up with ways of predicting the looming bankruptcy. Pointing to the growing scale of this harmful phenomenon with a greater number of larger companies going bankrupt, Chuvakhin & Gertmenian are trying to present businessmen with a framework for analysing the performance of business companies so as to receive indication of their problems before they are forced into bankruptcy.
To arrive at this understanding, they utilise Z-score model constructed by Edward Altman in 1968.
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The attempts to arrive at a ratio that could serve as a bona fide predictor of the upcoming bankruptcies have been undertaken for years, including a study by William Beaver. The critical breakthrough came when Edward Altman “built a comprehensive, statistical model using a technique called multiple discriminant analysis (MDA)” (Chuvakhin & Gertmenian, n.d.). The model relies on the combination of five different ratios that can later be summarised into a so-called Z-score.
Altman indicated that a company with a Z-score above 2.675 could be considered solvent, that with a score under 1.81 was liable to go bankrupt, and companies with Z-scores in the range of 1.81-2.675 fell into “gray area” or “ignorance zone”, which meant that they could escape bankruptcy, but with difficulty.
The legal issue explored in the articles refers to companies that forge numbers in their books, deceiving investors, as in the case of Enron and WorldCom. The authors ask: Is it possible to predict bankruptcy if the company’s management is cooking the books?
Their answer is yes since the Z-score model would avoid these accounting irregularities. For example, in the case of WorldCom that overstated both assets and earnings, the combination of ratios used by the model would overlook it, since a rise in earning would increase the first three ratios, but a rise in assets would decrease the last two, with the impact offsetting each other.
The model outlined in the article is of great value to managers of different companies. From the managerial perspective, it is extremely important which of the firm’s customers are likely to go bankrupt. If the bankruptcy of a large client comes a like a bolt of lightning, totally sudden and unanticipated, the firm can end with a large amount of bad debt in its accounts receivable account.
In 2001 alone, bankruptcy affected 257 public companies with combined assets of $256 billion (Chuvakhin & Gertmenian, n.d.). In the light of this fact, effective methods for bankruptcy prediction become a serious concern for managers.
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