CORPORATE DISTRESS ANALYSIS

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2 CORPORATE DISTRESS ANALYSIS Lecture Outline Introduction Learning Objectives Prescribed Readings Models for Prediction and Classification of Distressed Firms Firm Distress and Capital Markets The Impact of Accounting Alternatives Predictions Based on Liquidity Measures Financial Distress and Bankruptcy Seven Methodological Issues Activity Problems Solutions to Activity Problems Introduction Predicting the financial distress of companies and other entities has been a subject of much research over the past 30 years. Both this topic and Topic 10 outline the role that financial statement and other information can play in the prediction of financial distress. User groups that can benefit from distress prediction models include lenders, investors, regulatory agencies, auditors and management. Research on financial distress prediction has relevance to lending institutions, both in deciding whether to grant a loan (and its conditions) and in devising policies to monitor existing loans. Distress prediction models can be of assistance to investors in debt securities when assessing the likelihood of a company experiencing problems in making interest or principal repayments. In certain industries, regulatory bodies have the responsibility of monitoring the solvency and stability of individual companies. For instance, financial institutions such as banks, building societies and insurance companies are subject to overview by regulatory bodies in many countries. One judgment auditors must make is whether a firm is a going concern. This judgment affects the asset and liability valuation methods that are deemed appropriate for financial reporting. Financial distress prediction models can be a useful aid to the auditor in making a going concern judgment. Management can also benefit from distress prediction models, especially in relation to minimising the many potential costs associated with financial distress and bankruptcy. 2

3 Corporate Distress Analysis Learning Objectives Students will learn whether the current literature in distress prediction has produced useful results that can be applied to actual business situations. Research on financial distress has benefited considerably from continued interaction between the academic and the business community. The interaction has been motivated, in part, by the many decisions in which information on financial distress likelihood is relevant. Most progress has been made in documenting empirical regularities in the profiles of financially distressed and non-distressed firms. Considerable refinement had also been made over time in the statistical techniques used and the research designs employed in model developments. Prescribed Readings Course Notes Course Notes Models for Prediction and Classification of Distressed Firms The empirical literature on the prediction of financial distress falls into two main schools. The first, which is primarily descriptive, compares the financial characteristics, such as accounting-based financial ratios, of a sample of failed firms with those of a sample of non-failed firms. Beaver (1966) compares financial ratios of 79 failed firms with the ratios of 79 matched firms up to 5 years before the 79 firms actually failed. "Cash flow to total debt" had the highest discriminatory power of the ratios examined. Five years before failure, an optimal prediction criterion, called a cut-off value, based on that single accounting ratio misclassified only 22 percent of the validation; 1 year prior to failure the criterion misclassified only 13 percent of the validation sample. However, Beaver uses a frequency rate for failed firms that is substantially higher than one would observe in reality, a problem of using paired matching procedures. Beaver (1968a) examines those results further and reports that non-liquid-assets measures, such as cash flow to total debt, net income to total assets, and total debt to total assets, seem to perform better than liquid-asset measures, apparently because they represent more "permanent aspects" of the firm. Security prices also convey information about financial distress. Beaver (1968b) reports that, on average, common stock return data have a lead-time of about two and one-half years in discerning failure versus non-failure status. More recently, Aharony, Jones, and Swary (1980) evaluate a rule that estimates bankruptcy probabilities using quarterly security return data. Consistent with Beaver's, 3

4 their results show "that a solvency deterioration signal using capital market data is available some two years before the bankruptcy event". Most of the recent studies have adopted a multivariate approach to the prediction of financial distress by combining accounting and non-accounting data in a variety of statistical formulas. Altman's (1968) model is perhaps the best known of the early studies. Altman develops an equation that optimally combines five ratios reflecting accounting and market data, namely liquidity, profitability, financial leverage, solvency, and sales. The discriminant-function criterion, commonly known as a Z-score, predicted 24 of 25 failed firms not used in developing the model (the validation sample), 1 year ahead of the event. For a second sample of 66 non-failed firms with temporary earnings difficulties, the Altman Z-score criterion was in error in only 14 of 66 cases. Early studies using multivariate statistical techniques subsequent to Altman (1968) include Deakin (1972) and Blum (1974). Ratios based on accounting earnings, reported cash flow, and book debt prominently appear in the various statistical formulas, especially those that apply to the industrial sector. Another study of interest is Altman, Haldeman, and Narayanan (1977). This study apparently forms the underpinnings of the credit risk reports by Zeta Services, Inc. The variables identified in the Zeta model are retained earnings to total assets, leverage (based on market values), earnings variability, return on total assets, fixed charge coverage current ratio, and asset size. The model improves upon the Z-score model classifying 91 percent of a validation sample one year before filing for bankruptcy. When used five years before filing for bankruptcy, 77 percent of the validation sample is classified correctly. Having greatest weight in the equation are the variables "retained earnings to assets", which explain 25 percent of the difference between failed and non-failed firms, and "stability of earnings", which explains 20 percent of the difference. Since the seminal works of Altman and Beaver, Z-score models have been developed by researchers in different countries around the world, including the UK, Australia, Canada, New Zealand, Greece, Brazil and the Netherlands. Several financial distress prediction studies attempt to empirically compare the forecast accuracy of models already in the literature (Moyer 1977; Collins 1980; Hamer 1983; Zmijewski 1983). Zmijewski provides a comprehensive analysis, though all the studies contain similar results. Zmijewski empirically compares 13 financial distress models. The 13 models are tested on a sample of firms that have been traded on either the AMEX or NYSE. The sample consists of 72 bankrupt and 3,573 non-bankrupt firms. An analysis of the variables, one at a time, indicates that accounting rate of return measures are most useful in classifying bankruptcy; they are followed by the financial leverage and fixed payment coverage measures. 4

5 Corporate Distress Analysis The single-variable analysis indicates that, on average, bankrupt firms have lower rates of return, lower liquid-asset composition, lower liquidity position, and lower fixed payment coverage than do non-bankrupt firms. However, the degree of financial leverage was greater for bankrupt firms. Finally, the dispersion of those characteristics tends to be higher for the bankrupt firms than for non-bankrupt firms, in part due to the fact that as firms move closer to bankruptcy they take on more unusual characteristics. This could be due in part to the choice of accounting techniques. Firm Distress and Capital Markets Evidence that the capital market does not fully anticipate the event of failure is further reflected in studies of the stock market's reaction to bankruptcy declaration announcements. Clark and Weinstein (1983), for example, observe a sharp drop in price one day before and on the day of announcement, despite a general deterioration up to four years earlier (Aharony, Jones, and Swary 1980). The Impact of Accounting Alternatives Elam (1975), Ketz (1978) and Norton and Smith (1979) have investigated the impact of accounting alternatives in the context of financial distress. Elam (1975) explored the proposition that predictive ability should improve when off-balance-sheet lease data are incorporated into the ratio calculations. He concludes that predictive power is not improved. Altman (1976), however, raises several issues of research design that question the validity of this finding. Norton and Smith (1979) find little difference in price-level-based predictions of failure versus predictions based on historical cost numbers. In short, it appears that the test methodologies used in the research thus far have not been sufficiently exacting to detect an improvement in bankruptcy prediction rules due to an accounting change. Additional research is obviously required if the predictive value studies reviewed here are to be potentially useful for accounting policy. 5

6 Predictions Based on Liquidity Measures Another stream of research in the empirical literature emphasises the use of a "theory" of bankruptcy based on a stochastic model, such as the Markov process. Initially, researchers used a "first passage time" or "gambler's ruin" approach. Such an approach is best visualised as a game played by the firm against the environment. Starting with an initial balance of liquid resources, the firm (the gambler) wins one dollar from the opponent (the economic environment) with a given probability p of success and loses a dollar with the probability of 1 minus p. The game continues until the firm loses all its liquid resources (goes bankrupt). There are statistical expressions for the probability of losing all the liquid resources (ruin), the expected time lapse until ruin, and the expected gain or loss during a period. Wilcox (1971, 1973) presents estimates of the probability of ruin that are based on measures for the firm's adjusted cash position (liquidation value) and adjusted cash flow. 1 While Wilcox's results show an improvement over the various financial ratios tested by Beaver (1966, 1968a, 1968b). Kinney (1973) notes that this approach is not directly applicable for roughly one half of the sample of firms studied. Kinney also notes that on an adjusted basis, the Wilcox criterion is no more accurate than one based on a single cash-flow-to-total-debt ratio. Even extensions of the model conducted by Wilcox (1976) render no significant improvement in the accuracy of the first-passage time model. Financial Distress and Bankruptcy The possibility that a firm will fail financially is clearly a prime consideration in investment and lending decisions. The scale of possible contingencies is commonly denoted as a degree of "financial distress." Numerous interest groups utilise models to assess if financial distress exists. Auditors need to assess whether the going-concern assumption is satisfied. Regulatory agencies, such as the Federal Deposit Insurance Corporation, often determine whether a regulated company is in danger of failing. Labour negotiators can be affected by the survival of a company. Legislators occasionally pass judgment on whether a company satisfies the "failing company doctrine." The failing company doctrine is an argument available to companies to support mergers that are likely to reduce competition. The defence, which is the bidding firm in the merger, must establish that the firm subject to acquisition is in "imminent danger" of failing. Portfolio managers must periodically screen their investments and loans for signs of possible weakness. Finally, all lending and credit decisions are, of course, concerned with expected loss due to financial distress. 1 Wilcox (1973) defines adjusted cash position noncash current assets long-term assets liabilities. Adjusted cash flow is defined as net income + stock issued in merger - dividends -0.3 increase in noncurrent assets increase in long-term assets. 6

7 Corporate Distress Analysis Seven Methodological Issues Before summarising the different results on bankruptcy prediction, some methodological issues need attention. As in any research approach, the quality of the results and conclusions greatly depends on the decisions that the researcher makes regarding the methodology. These issues are summarised in what follows. (1) The research process is one of inference. The studies examine observable events such as legal bankruptcy or loan default to infer something that cannot be observed, namely, the degree of financial distress, probability of bankruptcy, or some similar uncertain event that is the object of analysis for predictive purposes. For some, it may be difficult to comprehend that the object of prediction is a conceptual rather than a tangible or physical phenomenon. Some services (e.g., Zeta Services, Inc.), however, are quick to point out that their models do not forecast failure but, instead, compare existing companies' financial and operating characteristics to those that have already failed. Doubtless, claims against the "forecasting" service by companies covered by the service are minimised by such a position. (2) The usefulness of financial-distress models for users hinges implicitly on an assessment of the costs associated with impending failure (i.e., the expected losses associated with each possible action available to the decision maker). The issues involve not only the loss to the security holder given legal bankruptcy but also the opportunity costs of mergers and other forms of feasible reorganisation such as divestment and asset or equity restructuring. For example, in the context of a commercial loan decision, the expected losses are a function of the chances that the loan is granted to a company that defaults (Type 1 error) and a firm whose loan is rejected turns out to have been a successful credit risk (Type 2 error). Altman (1977b) estimates the average loan loss for a commercial bank to be in the vicinity of 70 percent of the loan (Type 1 error); Altman (1984) further estimates that bankruptcy can cost company management and stockholders roughly percent of a firm's worth, based on its value 3 years earlier. (3) Financial distress has different shades of meaning to researchers and analysts. This requires a careful definition of the event of interest. Beaver (1966), for example, defines failed firms broadly to include bankruptcies, bond defaults, overdrawn bank accounts, and firms that omit preferred dividend payments. 7

8 Subsequent researchers tend to restrict failure to legal bankruptcy. For example, Altman, Haldeman, and Narayanan (1977) base their analysis only on those firms that filed bankruptcy petitions. 2 Problems of definition of financial distress can be even more severe in the noncorporate sector, such as in universities (Schipper 1977) and municipalities (Raman 1982a). (4) The manner in which the sample is collected, that is sample selection and data collection procedures. Certainly, the relatively small population of failed firms for which the required data are available constrains the sample selection procedure. The implications are that these firms do not represent a random sample. The population of failed firms can be easily identified from public sources if failure is defined as legal bankruptcy. However, if more comprehensive definitions of failure are adopted, it becomes increasingly difficult to identify the relevant population of firms. The unknown number of firms for which failure was imminent but averted by appropriate financial restructuring might also be considered. 3 The next step in the sample selection process is the selection of the non-failed firms. Many researchers use a matching process. For example, Blum (1974) selects one non-failed firm for every failed firm - matching the firms on criteria such as industry classification, size, the year of the failed firm's data, and the number of employees. One purpose of this matching process is to compare the underlying characteristics of failed and non-failed firms in the development of predictive models. Comparison of the underlying financial characteristics of failed and non-failed firms is the foundation of Multiple Discriminant Analysis (MDA) and logistic regression models, widely used to model corporate financial distress. With respect to the size of the non-failed sample, studies that use a matched-pair sample selection procedure use a sample size equal to the size of the non-failed sample. Consequently, the studies assume a bankruptcy rate of 50 percent, because failed firms are matched with non-failed firms. Unrealistically high rates bias the predictive ability tests of the models unless appropriate statistical methods are used. 2 Five non-bankruptcy petition firms were also included. The firms had substantial governmental support, were forced to merge, or had their business taken over by banks. 3 Casey, McGee, and Stickney (1986) show that the financial characteristics of liquidated and successfully reorganised firms differ on the basis of the free assets ratio (defined as noncollateralized tangible assets divided by total assets). A statistical model using this and other variables (e.g., firm size, cumulative profits) applied to a holdout sample was able to predict liquidated and reorganised firms with 59 percent accuracy. Also, for a study that attempts to identify the financial characteristics of troubled firms that merged versus troubled firms that entered bankruptcy, refer to Pastena and Ruland (1986). 8

9 Corporate Distress Analysis Please, note here that assuming an actual fail rate of 50% of firms is clearly unrealistic. Other researchers, such as Ohlson (1980) and Zmijewski (1983), use bankruptcy frequency rates closer to that of the population for the sample analysis. Dun & Bradreet report actual U.S. bankruptcy frequency rates over the past two decades from 0.25 to just over 1.00 percent of all companies. Zmijewski (1984) examines the effects of (1) sample selection bias (i.e. imposing the complete data criterion: this is where the lack of available data on which firms actually failed results in the final sample of failed firms being the population of firms with complete data) and (2) failed-firm sample frequency rates that are not representative of the population (of firms that actually failed) on bankruptcy prediction. The results indicate that imposing a complete-data criterion, while having some effect on the estimated parameters, does not affect the results qualitatively. However, using a failed-firm sample frequency rate much larger than the population's actual frequency rate (or the actual bankruptcy rates) results in biased classification and prediction rates. The group with a sample frequency rate larger than its rate in the population (usually the failed group) is predicted to be bankrupt too often, thus implying that the model is better than it actually would be in practice. (5) The fifth methodological issue concerns the sources of data. Many studies collect data from Moody's Industrial Manual for both the failed and non-failed samples. Other studies use annual report data, Compustat tapes, and SEC Form 10-K reports. Ohlson (1980) and Zmijewski (1983) provide evidence indicating that the lag time between a bankrupt firm's fiscal year-end and the release of its Form 10-K report is excessively long (e.g., a median lag of over 13 months). Therefore, when collecting data for bankrupt firms, precautions must be taken to ensure that the data for those firms are actually available prior to the date the firms filed for bankruptcy. Ohlson (1980) argues that the Form 10-K reports are the appropriate source of data for those firms and discuss the potential problems of using other data sources. For the non-failed sample, the use of Compustat tapes as a source of data can also result in an inappropriate sample. The degree to which this could cause a problem depends on the definition of failure that has been adopted: the more comprehensive the definition, the more serious the problem. Zmijewski (1983) reports that some firms listed in the 1979 Compustat Annual Industrial File had filed petitions for bankruptcy. 9

10 Deakin (1977) reports an even number of firms that are in default on debt payments, in arrears of preferred stock dividends, and so forth, may still be listed on the Compustat tape as non-failed firms. Thus, one cannot assume that all firms on the tape should be classified as non-failed. (6) The sixth methodological issue is one of measuring predictive ability. Actual predictive ability is the ability of a forecaster, or a forecasting model, to predict an event that has not occurred. Therefore, an appropriate method of measuring predictive ability is to generate estimates for a model in one chronological period and use the predictions from that model to forecast a future chronological period. In practice, if sufficient data are not available to use that approach, researchers employ alternative approaches to estimate the predictive ability of a forecasting model. The least appropriate of the alternative approaches is to classify the sample using the same data used to estimate the model. Another approach, which is often employed, is to randomly partition the sample into an estimation sample. However, this approach does not measure actual predictive ability because the sample is not partitioned into chronological sub-samples. Some statistical techniques attempt to estimate actual predictive ability by iteratively predicting one "holdout" observation. However, such an approach also does not measure actual predictive ability on a chronological basis. If there is any non-stationarity in the relationships over time, then the techniques for measuring predictive ability that do not use chronological sub-periods provide prediction success rates that are too high. (7) The final issue is the choice of a statistical methodology. Initially, researchers used an individual variable, that is a univariate test, to discriminate between failed and non-failed firms. Later studies adopted multivariate statistics to examine the predictive ability of multiple variables simultaneously. Four such statistical procedures dominate the literature: linear discriminant analysis, quadratic discriminant analysis, logit analysis, and probit analysis. Although an in-depth discussion of those procedures is beyond the scope of this lecture, it should be noted that the choice of procedures should be made on the basis of the underlying distributional characteristics of the data, as discussed in Topic 4. The choice of statistical procedures especially affects the results when the distributional assumptions about the data are severely violated. It is worthwhile to note here that there is evidence to suggest that the ratio distributions are not normally distributed. Recently, nonparametric statistical techniques have been employed for estimating financial distress models. Empirical illustrations may be found in Marais, Patell, and Wolfson (1984) and Frydman, Altman, and Kao (1985). Such techniques are less dependent on the distributional characteristics of the 10

11 Corporate Distress Analysis data and focus more on recognising basic patterns in the data. Consequently, they may offer some improvement in predictive performance. Readings Students should attempt to source from their Libraries or the following Readings: Early evidence Reading 9.1: Beaver, W.H. (1966) Financial Ratios as the Predictors of Failure, Supplement to Journal of Accounting Research, pp Reading 9.2: Altman, E.1. (1968) Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance, Vol.23, No.4, pp Reading 9.3: Beaver, W.H. (1968) Market Prices, Financial Ratios, and the Prediction of Failure, Journal of Accounting Research, Autumn, pp Reading 9.4: Altman, E.I. (1984) The Success of Business Failure Prediction Models: An International Survey, Journal of Banking and Finance, Vol.8, pp Recent evidence Reading 9.5: Kane, G.D., Richardson, F.M. & Meade, N.L. (1998) Rank Transformations and the Prediction of Corporate Failure, Contemporary Accounting Research, Vol.15, No.2, pp Reading 9.6: Kane, G.D., Richardson, F.M. & Graybeal, P. (1996) Recession- Induced Stress and the Prediction of Corporate Failure, Contemporary Accounting Research, Vol.13, No.2, pp Reading 9.7: Lindsay, D.H. & Campbell, A. (1996) A Chaos Approach to Bankruptcy Prediction, Journal of Applied Business Research, Vol.12, No.4, pp.1-9. Additional evidence The following research articles will assist you in understanding further relevant issues in this topic. Reading 9.8: Denis, D.J. & Denis, D.K. (1996) Leveraged Recaps and the Causes of Financial Distress, Journal of Financial Economics, Vol.8, No.4, pp Reading 9.9: Laitinen, E.K. & Laitinen, T. (1998) Cash Management Behaviour and Failure Prediction, Journal of Business Finance and Accounting, Vol.25, No.7&8, pp

12 Reading 9.10: Chen, K.C. & Church B.K. (1996) Going Concern Opinions and the Market s Reaction to Bankruptcy Filings, The Accounting Review, Vol.71, No.1, pp Activity Problems 1. What indications do the single-variable analyses give between bankrupt and liquid firms? 2. Other than auditors, nominate three other stakeholders who must be professionally aware of Financial Distress indicators. 3. What would be typical sources of data in relation to Financial Distress? Solutions to Activity Problems 1. The former have lower rates of return, lower liquid-asset composition, lower liquidity position, and lower fixed payment coverage than do non-bankrupt firms. 2. Legislators, Regulators, Labour Negotiators, Portfolio Managers, Lenders/Creditors. 3. From Moody's Industrial Manual for both the failed and non-failed samples. Other studies use annual report data, Compustat tapes, and ASIC Form 10-K reports. 12