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... 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 bankruptcy prediction, based on the use of neural networks, has evolved.
Artificial Neural Networks (ANN) are computers constructed to process information, in parallel, similar to the human brain. ANN's store information in the form of patterns and are able to learn from their processing experience. Unlike MDA and logit analyses, ANN's impose less restrictive data requirements (the requirement for linearity, for example) and are especially useful in recognising and learning complex data relationships. Recent ANN bankruptcy prediction studies include those of Bell, et al. (1990), Hansen & Messier (1991), Chung & Tam (1992), Liang, et al. (1992), Tam & Kiang (1992), Salchenberger (1993), Coats & Fant (1993), Fanning & Cogger (1994), Brockett, et al. (1994), Boritz, et al. (1995), and Etheridge & Stream (1995 and 1997).
Research has shown that ANNs offer a viable alternative to other more traditional methods of bankruptcy prediction. The ability of the model to learn allows for the constant re-calibration and validation of the model, which helps increase classification rates. From a theoretical perspective, ANNs are more desirable because they make fewer assumptions about the data normality and linear separability. One of the main disadvantages of ANNs is the inability to assign intuition the network weights. Another disadvantage is that the model might simply memorise the data as opposed to forming a general set of classification rules, which can cause estimates on future samples to be less reliable. Future research in bankruptcy prediction should analyse the economic and institutional factors that can impact the reasons for bankruptcy.
Jones (1987) indicated that the lack of homogeneity in the motivation for a bankruptcy filing might complicate the modelling effort. Although normally motivated by an effort to resolve severe financial problems, a firm may file for bankruptcy primarily to void a union contract or for other legal reasons (Jones 1987). Another area where models can be improved is in catering for predictor variables other than financial ratios may prove beneficial. For example, measures of management experience, management expertise, or other behavioural aspects that impact the operations of the firm could be significant in a bankruptcy prediction model.
Additionally, including variables that control for a changing economic environment may provide valuable insights for predicting bankruptcy. Bibliography: References Altman, Edward I. Corporate Financial Distress. New York, NY: John Wiley and Sons, 1983.
Altman, Edward I. (1968) "Financial Ratios, Discriminate Analysis and the Prediction of Corporate Bankruptcy, " The Journal of Finance. Altman, Edward I. Homepage of Professor Edward I. Altman, New York, NY: Stern School of Business.
Available at web Altman, Edward I, Giancarlo Marco, and Franco Varetto (1994) "Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience), " The Journal of Banking and Finance. Altman, Edward I. and Thomas P. McGough (1974) "Evaluation of a Company as a Going Concern, " The Journal of Accountancy. Beaver, W. , 1967, "Financial Ratios as Predictors of Failures, " in Empirical Research in Accounting, Journal of Accounting Research. Begley, J. , Ming, J. , Watts, S. , 1997, "Bankruptcy Classification Errors in the 1980 s: An Empirical Analysis of Altman's and Ohlson's Models, " Review of Accounting Studies.
Bell, T. B. , G. S. River and J.
Verchio, 1990, "Neural Nets Versus Logistic Regression: A Comparison of Each Model's Ability to Predict Commercial Bank Failures, Boritz, J. E. , D. B. Kennedy and A.
M. Albuquerque, 1995, Predicting Corporate Failure Using a Neural Network Approach, Intelligent Systems in Accounting, Finance and Management. Brockett, P. L. , W. W. Cooper, L.
L. Golden and U. Pitaktong, 1994, "A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency. " The Journal of Risk and Insurance. Buzzell, R. D. , Gale, B.
T. , 1987, The PIMS Principles Linking Strategy to Performance, New York: The Free Press. Chung, H. M. and K.
Y. Tam, 1992, "A Comparative Analysis of Inductive-Learning Algorithms, " Intelligent Systems in Accounting, Finance and Management. Coats, P. K. and L. F.
Fant, 1993, "Recognizing Financial Distress Patterns Using a Neural Network Tool, " Financial Management. Cook, Roy A. and Jury L. Nelson. "A Conspectus of Business Failure Forecasting, " Available at web Deakin, E. , Business Failure Prediction: An Empirical Analysis, ", 1977, in E.
Altman and A. Same, eds. , Financial Crises: Institutions and Markets in a Fragile Environment, New York: Wiley. Etheridge, H. L. and R. S.
Sriram, 1995, "A Neural Network Approach to Financial Distress Analysis, " Advances in Accounting Information Systems. Fanning, K. and K. O.
Cogger, 1994, "A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction, " Intelligent Systems in Accounting, Finance and Management. Gilbert, L. R. , Menon, K. , and Schwartz, K. B. , 1990, "Predicting Bankruptcy for Firms in Financial Distress, " Journal of Business Finance and Accounting. Hansen, J. V.
and W. F. Messier, 1991, "Artificial Neural Networks: Foundations and Application to a Decision Problem, " Expert Systems with Applications. Jones, F. L. 1987. "Current Techniques in Bankruptcy Prediction. " Journal of Accounting Literature.
Koh, H. C. and Killough, L. N. , 1990, "The Use of Multiple Discriminant Analysis in the Assessment of the Going-concern Status of an Audit Client, " Journal of Business Finance and Accounting. Lev, B. , 1974, Financial Statement Analysis, A New Approach. Englewood Cliffs, N.
J. : Prentice-Hall. Liang, T. P. , J. S.
Chandler, I. Han and J. Roan, 1992, "An Empirical Investigation of Some Data Effects on the Classification Accuracy of Probit, ID 3, and Neural Networks, " Contemporary Accounting Research. Lo, Andrew W. , 1986 "Logit Versus Discriminant Analysis: A Specification Test and Application to Corporate Bankruptcies, " Journal of Econometrics Ohlson, J. A. , 1980 "Financial Ratios and the Probabilistic Prediction of Bankruptcy, " Journal of Accounting Research.
Platt, J. D. and Platt, M. B. , 1990, "Development of a Class of Stable Predictive Variables: The Case of Bankruptcy Prediction, " Journal of Business Finance and Accounting. Salchenberger, L. M. , E.
M. Car and N. A. Lash, 1992 "Neural Networks: A New Tool for Prediction Thrift Failures, " Decision Sciences. Santomero, A.
M. and J. D. Vinso, 1977, "Estimating the Probability of Failure for Commercial Banks and the Banking System, " Journal of Banking and Finance. Sheppard, Jerry Paul. , 1994 "The Dilemma of Matched Pairs and Diversified Firms in Bankruptcy Prediction Models, " The Mid-Atlantic Journal of Business Stickney, Claude P. , 1996 Financial Reporting and Statement Analysis. 3 rd Edition. Ft.
Worth, TX: The Dryden Press. Taffler, R. , and Houston, 1980, "How to Identify Failing Companies Before It Is Too Late, " Professional Administration. Tam, K. Y. and M. Y.
Kiang, July 1992, "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions, " Management Science... Vinso, J. D. , 1979, "A Determination of the Risk of Ruin, " Journal of Financial and Quantitative Analysis. Wilcox, J.
W. , 1971, "A Gamblers Ruin Prediction of Business Failure Using Accounting Data, " Sloan Management Review, Vol. 12. Wilcox, J. W. , 1976, "The Gamblers Ruin Approach to Business Risk, " Sloan Management Review. Wilcox, J.
W. , 1973, "A Prediction of Business Failure Using Accounting Data, " Journal of Accounting Research, Vol. 11. Zavgren, Christine V. , 1985 "Assessing the Vulnerability to Failure of American Industrial Firms: A Logistic Analysis, " Journal of Business Finance and Accounting.
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Research essay sample on Models For Predicting Corporate Financial Distress
