Models For Predicting Corporate Financial Distress Essay - 2,464 words

... ny, logit analysis has been compared to a more advanced analytical tool, neural networks. Research has found that the approaches perform similarly and should be used in combination (Altman, Marco, and Varetto 1994). Based on multiple discriminate analysis (MDA), the model predicts a company's financial health based on a discriminant function of the form: Z = 0. 012 X 1 + 0. 014 X 2 + 0. 033 X 3 + 0. 006 X 4 + 0. 999 X 5 X 3 = earnings before interest and taxes / total assets X 4 = market value of equity / book value of total liabilities The Z-Score model (developed in 1968) was based on a sample composed of 66 manufacturing companies with 33 firms in each of two matched-pair groups. The bankruptcy group consisted of companies that filed a bankruptcy petition under Chapter 11 of the United States bankruptcy act from 1946 through 1965. Based on the sample, all firms having a Z-Score greater than 2. 99 clearly fell into the non-bankruptcy sector, while those firms having a Z-Score below 1. 81 were bankrupt.


Altman subsequently developed a revised Z-Score model (with revised coefficients and Z-Score cut-offs) which dropped variables X 4 and X 5 (above) and replaced them with a new variable X 4 = net worth (book value) /total liabilities. The X 5 variable was dropped to minimise potential industry effects related to asset turnover. Around 1977, Altman developed jointly with a private financial firm (ZETA Services, Inc. ) a revised seven-variable ZETA model based on a combined sample of 113 manufacturers and retailers. The ZETA model is allegedly "far more accurate in bankruptcy classification in years 2 through 5 with the initial year's accuracy about equal. " However, the coefficients of the model are not specified (without retaining ZETA Services). The ZETA model is based on the following variables: &# 61623; capitalisation (five year average of total market value) &# 61623; size (total tangible assets) Application of the logit model requires four steps. 1. a series of seven financial ratios are calculated. 2.


each ratio is multiplied by a coefficient unique to that ratio. This coefficient can be either positive or negative. 3. the resulting values are summed together (y). 4. the probability of bankruptcy for a firm is calculated as the inverse of (1 + ey). "Explanatory variables with a negative coefficient increase the probability of bankruptcy because they reduce ey toward zero, with the result that the bankruptcy probability function approaches 1 / 1, or 100 percent.


Likewise, independent variables with a positive coefficient decrease the probability of bankruptcy" (Stickney 1996). Table 1 shows the financial ratios used in the logit model and their respective coefficients. TABLE 1 Financial Ratios used in Logit Model Average Receivables/Average Inventories - 1. 583 (Cash + Marketable Securities) /Total Assets - 10. 78 Quick Assets/Current Liabilities + 3. 074 Income from Continuing Operations/ (Total Assets - Current Liabilities) + 0. 486 Long-Term Debt/ (Total Assets - Current Liabilities) - 4. 35 Sales/ (Net Working Capital + Fixed Assets) + 0. 11 Probability of Bankruptcy = 1 / (1 + ey) Other Statistical Failure Prediction Models Many additional bankruptcy prediction models have been developed since the work of Beaver and Altman. Lev (1974), Deakin (1977), Ohlson (1980), Taffler (1980), Platt & Platt (1990), Gilbert, Menon, and Schwartz (1990), and Koh and Killough (1990) amongst others have continued to refine the development of multivariate statistical models. Almost all of these traditional models have been either matched-pair multi-discriminate models or logit models. A 1997 study by Begley, Ming and Watts concludes: Given that Ohlson's original model is frequently used in academic research as an indicator of financial distress, its strong performance in this study supports its use as a preferred model.


Wilcox (1971 and 1976), Santomero (1977), Vinso (1979) and others have adapted a gambler's ruin approach to bankruptcy prediction. Under this approach, bankruptcy is probable when a company's net liquidation value (NLV) becomes negative. Net liquidation value is defined as total asset liquidation value less total liabilities. From one period to the next, a company's NLV is increased by cash inflows and decreased by cash outflows during the period. Wilcox combined the cash inflows and outflows and defined them as "adjusted cash flow. " All other things being equal, the probability of a company's failure increases, the smaller the company's beginning NLV, the smaller the company's adjusted (net) cash flow, and the larger the variation of the company's adjusted cash flow over time. Wilcox uses the gambler's ruin formula (Feller, 1968) to show that a company's risk of failure is dependent on; 2) the size of the company's adjusted cash flow "at risk" each period (ie.


the size of the company's bet). Using a more robust statistical technique, Vinso (1979) extended Wilcox's gambler's ruin model to develop a safety index. Based on input concerning the variability of "expected contribution margin amounts, " the index can be used to predict the point in time when a company's ruin is most likely to occur (called first passage time). The statistics used in gambler's ruin approaches are somewhat formidable (especially to the average reader).


However, both Wilcox and Vinso richly describe some of the factors which most affect business failure. For example, Wilcox states: The (cash) inflow rate... can be increased through higher average return on investment. However, having a major impact here usually requires long-term changes in strategic position. This is difficult to control over a short time period except by divestitures of peripheral unprofitable businesses... The average outflow rate is controlled by managing the average growth rate of corporate assets.


Effective capital budgeting... requires resource allocation emphasising those business units, which have the highest future payoff. The size of the bet is the least understood factor in financial risk. Yet management has substantial control over it. Variability in liquidity flows governs the size of the bet. This variability can be managed through dividend policy, through limiting earning variability and investment variability, and through controlling the co-variation between profits and investments...


True earnings smoothing is attained by control of exposure to volatile industries, diversification, and improved strategic position. Vinso supports Wilcox's emphasis on cash flow processes and stresses the importance of debt capacity: Before deriving a mathematical model for determining the risk of ruin, it is necessary to describe the process. First, a firm has some pool of resources at time = 0 of some size U 0, which are available to prevent ruin (similar to Wilcox's beginning NAV). Then, earnings come to the firm from revenue (s)... less the costs incurred in producing the revenues. There are two types of costs to be considered: variable, which change according to the stochastic nature of the revenue sources, and fixed costs, which do not vary with revenue but are a function of the period.


So, revenue less variable costs... can be defined as variable profit (which is available to pay fixed costs). If Ut is less than zero, ruin occurs because no funds are available to meet unpaid fixed costs... These definitions, however, ignore debt capacity, if available, which must be included as the firm can use this source without being forced to confront shareholders, creditors or bankruptcy, ... debt holders or other creditors will force reorganisation if a firm is unable to meet contractual obligations because working capital is too low and the firm cannot obtain more debt. Alternative Models - Artificial Neural Networks Since 1990, another promising approach to bank ...................................................................................................................................................................................................................................................................................................................................................................

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Essay Tags: fixed costs, strategic position, cash inflows, neural networks, cash flow

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