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Models For Predicting Corporate Financial Distress

INTRODUCTION 2 MEASURING FINANCIAL HEALTH 2is the one proposed by Edward Altman.
FINANCIAL DISTRESS 2 FACTORS AFFECTINGAltman's z-score, or zeta model, combined
FINANCIAL HEALTH 3 Capital Structure andvarious measures of profitability or risk.
Capital Adequacy 3 Operating Cash Flows andThe resulting model was one that demonstrated
Cost Structure 4 Earnings Capacity 4a company's risk of bankruptcy relative to a
Liquidity 4 Asset Conversions - "Growingstandard. Altman's initial study proved his
Broke" 5 Asset Utilisation Efficiencymodel to be very accurate; it correctly
Turnover 5 Strategic Position 5 PREDICTINGpredicted bankruptcy in 94% of the initial
FINANCIAL DISTRESS 6 FAILURE PREDICTIONsample (Altman 1968). Despite the positive
MODELS 7 Altman's Z Score 8 Logit Analysis:results of his study, Altman's model had a
The Model 9 Other Statistical Failurekey weakness; it assumed variables in the
Prediction Models 10 The Gambler's Ruinsample data to be normally distributed. If
Models 10 Alternative Models - Artificialall variables are not normally distributed,
Neural Networks 12 CONCLUSION 12 REFERENCESthe methods employed may result in selection
13 Introduction A company trying to achieveof an inappropriate set of predictors
its business plan faces problems similar to(Sheppard 1994). Chistine Zavgren developed a
those faced by a driver embarking on a longmodel that corrected for this problem. Her
trip. The likelihood that car and driver willmodel used logit analysis to predict
reach their destination is dependent on: 1)bankruptcy. Due to its use of logit analysis,
how much fuel is in the car's tank uponher model is considered more robust (Lo
starting out, 2) the car's fuel efficiency,1986). Further, logit analysis actually
3) how many service stations will beprovides a probability (in terms of a
available to refill the car's fuel tank alongpercentage) of bankruptcy. Also, the
the way and 4) whether the car's fuel tank isprobability calculated might be considered a
large enough to cover unexpected accidents,measure of the effectiveness of management
delays, and detours along the way. Similarly,(ie. effective management will not lead a
whether or not a company survives in a highlycompany to the verge of bankruptcy). During
competitive business environment is dependentthe 1980s and 1990s, the trend has been to
upon: 1) how financially healthy theuse logit analysis in favour of multiple
corporation is at its inception, 2) thediscriminant analysis (Stickney 1996). More
company's ability (and relative flexibilityrecently, logit analysis has been compared to
and efficiency) in creating cash from itsa more advanced analytical tool, neural
continuing operations, 3) the company'snetworks. Research has found that the
access to capital markets, and 4) theapproaches perform similarly and should be
company's financial capacity and stayingused in combination (Altman, Marco, and
power when faced with unplanned cashVaretto 1994). Altman's Z Score Based on
shortfalls. Measuring Financial Health Theremultiple discriminate analysis (MDA), the
is no single measure of financial health.model predicts a company's financial health
Ideally, solvency could be measured along abased on a discriminant function of the form:
continuum in the same way that fuelZ=0.012X1+0.014X2+0.033X3+0.006X4+0.999X5
sufficiency can be measured using a car'sWhere: X1=working capital/total assets
petrol gauge. Full health would equate withX2=retained earnings/total assets X3=earnings
having a full tank of fuel. Poor health wouldbefore interest and taxes/total assets
be equivalent to showing an empty tank. AsX4=market value of equity/book value of total
healthiness progressively decreased, theliabilities X5=sales/total assets The Z-Score
solvency gauge would register movement in themodel (developed in 1968) was based on a
direction of relative insolvency. Ultimately,sample composed of 66 manufacturing companies
as healthiness continues to decline, thewith 33 firms in each of two matched-pair
solvency gauge would hopefully flash agroups. The bankruptcy group consisted of
warning light. Since, in the real world, nocompanies that filed a bankruptcy petition
single measure of financial health exists,under Chapter 11 of the United States
proxies that measure various aspects ofbankruptcy act from 1946 through 1965. Based
solvency are often combined to estimate aon the sample, all firms having a Z-Score
company's healthiness at a point in time.greater than 2.99 clearly fell into the
Financial Distress As a financially healthynon-bankruptcy sector, while those firms
company becomes more and more financiallyhaving a Z-Score below 1.81 were bankrupt.
distressed, it ultimately enters an area ofAltman subsequently developed a revised
great danger. Changes to the company'sZ-Score model (with revised coefficients and
operations and capital structure (ie.Z-Score cut-offs) which dropped variables X4
restructuring) must be made to remainand X5 (above) and replaced them with a new
healthy. Apple Computers' attempts in recentvariable X4 = net worth (book value)/total
years to restructure its operations toliabilities. The X5 variable was dropped to
survive in the highly competitive computerminimise potential industry effects related
hardware business is a good example of ato asset turnover. Around 1977, Altman
company trying to dramatically restructuredeveloped jointly with a private financial
itself in order to maintain solvency.firm (ZETA Services, Inc.) a revised
Continued decreases in financial healthseven-variable ZETA model based on a combined
ultimately lead to insolvency and thensample of 113 manufacturers and retailers.
potentially, bankruptcy. Available evidenceThe ZETA model is allegedly far more accurate
suggests many companies do not adequatelyin bankruptcy classification in years 2
attempt to resolve their financial healththrough 5 with the initial year's accuracy
problems until it is too late to avoidabout equal. However, the coefficients of the
bankruptcy. Factors Affecting Financialmodel are not specified (without retaining
Health Capital Structure and Capital AdequacyZETA Services). The ZETA model is based on
Companies finance their long-term operationsthe following variables:  return on
primarily through two sources of capital -assets  stability of earnings
debt and equity. One of the most important debt service  cumulative
financing decisions a company makes is theprofitability  liquidity/current
proportion of debt to owner's equity in theratio  capitalisation (five year
company's capital structure. Summary measuresaverage of total market value)  size
of a company's capital structure include the(total tangible assets) Logit Analysis: The
company's debt to equity ratio (D/E) and debtModel Application of the logit model requires
to total capital ratio (D/(D+E)). Interestfour steps. 1. a series of seven financial
and principal payments on debt must be paidratios are calculated. 2. each ratio is
from operations before any payments can bemultiplied by a coefficient unique to that
distributed to equity holders (in the form ofratio. This coefficient can be either
dividends or share buy-backs). Therefore, thepositive or negative. 3. the resulting values
interest and principal, which must be paid onare summed together (y). 4. the probability
debt, are considered fixed-costs ofof bankruptcy for a firm is calculated as the
operations. From an operationalinverse of (1 + ey). Explanatory variables
point-of-view, the extent of the burden ofwith a negative coefficient increase the
these fixed obligations can be measuredprobability of bankruptcy because they reduce
relative to the company's continuing abilityey toward zero, with the result that the
to pay the fixed obligations. A frequentlybankruptcy probability function approaches 1
used measure of a company's ability to cover1, or 100 percent. Likewise, independent
its interest payments is its earnings beforevariables with a positive coefficient
interest and taxes and before depreciationdecrease the probability of bankruptcy
and amortisation (EBITDA) to its interest(Stickney 1996). Table 1 shows the financial
expense. A company is financially distressedratios used in the logit model and their
whenever its EBITDA is less than its interestrespective coefficients. TABLE 1 - Financial
expense.  Financial leverage involvesRatios used in Logit Model FINANCIAL RATIO
the substitution of fixed-cost debt forCOEFFICIENT + 0.23883 Average Inventories
owner's equity in the hope of increasingSales - 0.108 Average Receivables/Average
equity returns. As demonstrated by HigginsInventories - 1.583 (Cash + Marketable
and others, financial leverage improvesSecurities)/Total Assets - 10.78 Quick Assets
financial performance when things are goingCurrent Liabilities + 3.074 Income from
well but worsens financial performance whenContinuing Operations/(Total Assets - Current
things are going poorly. Therefore,Liabilities) + 0.486 Long-Term Debt/(Total
increasing the ratio of debt to equity in aAssets - Current Liabilities) - 4.35 Sales
company's capital structure implicitly makes(Net Working Capital + Fixed Assets) + 0.11 y
the company relatively less solvent (on the= Sum of (Coefficient * Ratio) Probability of
downside) and more financially risky than aBankruptcy = 1/(1 + ey) Other Statistical
company without debt.  CapitalFailure Prediction Models Many additional
adequacy relates to whether a company hasbankruptcy prediction models have been
enough capital to finance its planned futuredeveloped since the work of Beaver and
operations. If the company's capital isAltman. Lev (1974), Deakin (1977), Ohlson
inadequate, then it must either be able to:(1980), Taffler (1980), Platt & Platt (1990),
1) successfully issue new equity, or 2)Gilbert, Menon, and Schwartz (1990), and Koh
arrange new debt. The amount of debt aand Killough (1990) amongst others have
company can successfully absorb and repaycontinued to refine the development of
from its continuing operations is normallymultivariate statistical models. Almost all
referred to as the company's debt capacity.of these traditional models have been either
Capital adequacy is normally evaluated bymatched-pair multi-discriminate models or
looking at the company's operational cashlogit models. A 1997 study by Begley, Ming
flow projections and its projections ofand Watts concludes: "Given that Ohlson's
capital needs. When companies undertake majororiginal model is frequently used in academic
new projects or undergo a significantresearch as an indicator of financial
financial restructuring they often performdistress, its strong performance in this
financial feasibility studies to determinestudy supports its use as a preferred model."
whether the company has the financialThe Gambler's Ruin Models Wilcox (1971 and
capacity to undertake the project and whether1976), Santomero (1977), Vinso (1979) and
the company will be able to repay all futureothers have adapted a gambler's ruin approach
debt payments once the project is built.to bankruptcy prediction. Under this
Operating Cash Flows and Cost Structure Allapproach, bankruptcy is probable when a
other factors being equal, companies that cancompany's net liquidation value (NLV) becomes
consistently generate positive cash flowsnegative. Net liquidation value is defined as
from operations will remain relatively moretotal asset liquidation value less total
solvent than those that cannot. This requiresliabilities. From one period to the next, a
that operating cash inflows (collections orcompany's NLV is increased by cash inflows
sales) consistently exceed operating cashand decreased by cash outflows during the
outflows (costs). Companies which experienceperiod. Wilcox combined the cash inflows and
erratic cash outflows and inflows areoutflows and defined them as adjusted cash
relatively more risky because they are lessflow. All other things being equal, the
likely, in one or more time periods, to beprobability of a company's failure increases,
able to cover fixed expenses/outflows.the smaller the company's beginning NLV, the
Companies which have a higher proportion ofsmaller the company's adjusted (net) cash
fixed costs to variable costs are alsoflow, and the larger the variation of the
relatively more risky and relatively lesscompany's adjusted cash flow over time.
solvent than companies with a relativelyWilcox uses the gambler's ruin formula
lower proportion of fixed costs in their(Feller, 1968) to show that a company's risk
operating cost structure. Earnings Capacityof failure is dependent on; 1) the above
All other things being equal, companies withfactors plus, 2) the size of the company's
higher relative earnings and higher relativeadjusted cash flow at risk each period (ie.
returns on investment will remain morethe size of the company's bet). Using a more
solvent than their less fortunaterobust statistical technique, Vinso (1979)
competitors. The most commonly used financialextended Wilcox's gambler's ruin model to
measures of earnings capacity are earningsdevelop a safety index. Based on input
before interest and taxes (EBIT) and netconcerning the variability of expected
income. Liquidity Adequate liquidity is acontribution margin amounts, the index can be
further necessary component of solvency.used to predict the point in time when a
Frequently used liquidity measures include:company's ruin is most likely to occur
a) working capital (current assets minus(called first passage time). The statistics
current liabilities), b) current ratioused in gambler's ruin approaches are
(current assets divided by currentsomewhat formidable (especially to the
liabilities), and c) quick ratio (cash,average reader). However, both Wilcox and
marketable securities and accounts receivableVinso richly describe some of the factors
divided by current liabilities). To evaluatewhich most affect business failure. For
liquidity, each of the assets and liabilitiesexample, Wilcox states: "The (cash) inflow
on a company's balance sheet should berate ... can be increased through higher
evaluated for liquidity. Current assets areaverage return on investment. However, having
those which will likely be converted to casha major impact here usually requires
within one year or less. Current liabilitieslong-term changes in strategic position. This
are those which must be paid within one year.is difficult to control over a short time
However, when a company becomes financiallyperiod except by divestitures of peripheral
distressed, even assets which are normallyunprofitable businesses...The average outflow
considered current assets (accountsrate is controlled by managing the average
receivable and stock, for example) may becomegrowth rate of corporate assets. Effective
relatively "illiquid". Long-term assets, incapital budgeting ... requires resource
general, are far less liquid than currentallocation emphasising those business units,
assets. Some longer-term assets may be verywhich have the highest future payoff. The
"illiquid". Also, as stated above, often asize of the bet is the least understood
company's long-term liabilities can becomefactor in financial risk. Yet management has
immediately due and payable if the companysubstantial control over it. Variability in
violates contractual debt covenants or otherliquidity flows governs the size of the bet.
obligations. Wilcox (1976) argues that netThis variability can be managed through
liquidation value provides a solid conceptualdividend policy, through limiting earning
basis for evaluating a company's liquidity.variability and investment variability, and
Net liquidation value is defined as totalthrough controlling the co-variation between
asset liquidation value less totalprofits and investments...True earnings
liabilities. Wilcox (1976) applies what hesmoothing is attained by control of exposure
calls typical (not definitive) valuationto volatile industries, diversification, and
multipliers to balance sheet assets to arriveimproved strategic position." Vinso supports
at representative asset liquidation values:Wilcox's emphasis on cash flow processes and
 Cash Equivalents 100%  Otherstresses the importance of debt capacity:
Current Assets 70%  Long Term Assets"Before deriving a mathematical model for
50% Wilcox (1976) shows that a companydetermining the risk of ruin, it is necessary
becomes bankrupt when net liquidation valueto describe the process. First, a firm has
is reduced to zero. Asset Conversions -some pool of resources at time = 0 of some
"Growing Broke" Asset and liabilitysize U0, which are available to prevent ruin
conversions are continuously ongoing in any(similar to Wilcox's beginning NAV). Then,
dynamic business. Operationally, the companyearnings come to the firm from
is selling its products thereby creating cashrevenue(s)...less the costs incurred in
inflows. Alternatively, sales may be made onproducing the revenues. There are two types
credit, increasing the company's accountsof costs to be considered: variable, which
receivable. Concurrently, inventories arechange according to the stochastic nature of
produced and sold and production andthe revenue sources, and fixed costs, which
operating expenses are incurred to continuedo not vary with revenue but are a function
operations. If a company's inventories andof the period. So, revenue less variable
accounts receivable grow faster than thecosts...can be defined as variable profit
corresponding growth in the company's sales(which is available to pay fixed costs). If
and accounts payable, liquidity will beUt is less than zero, ruin occurs because no
negatively affected. Strategic assetfunds are available to meet unpaid fixed
conversions are also ongoing, but with lesscosts...These definitions, however, ignore
regularity. Decisions to invest in 'bricksdebt capacity, if available, which must be
and mortar' and other long-term investmentsincluded as the firm can use this source
are made and debt and equity are obtained towithout being forced to confront
supply the capital needed to pay for them.shareholders, creditors or bankruptcy,...debt
Slowly but surely, companies can 'go broke'holders or other creditors will force
when assets are converted to less liquidreorganisation if a firm is unable to meet
forms over a sustained time period. This cancontractual obligations because working
happen when the company's assets grow fastercapital is too low and the firm cannot obtain
than the company's sales (often the case formore debt." Alternative Models - Artificial
many start-up companies). When this happens,Neural Networks Since 1990, another promising
the company becomes more highly leveraged andapproach to bankruptcy prediction, based on
less solvent. Similarly, a company whose longthe use of neural networks, has evolved.
term investment decisions do not pay off inArtificial Neural Networks (ANN) are
terms of planned operating returns (thuscomputers constructed to process information,
increasing fixed cost structures andin parallel, similar to the human brain.
decreasing operating cash flows), will becomeANN's store information in the form of
less solvent. Asset Utilisation Efficiencypatterns and are able to learn from their
Turnover Those companies, which survive, useprocessing experience. Unlike MDA and logit
their human and capital assets relativelyanalyses, ANN's impose less restrictive data
efficiently. That is, they have relativelyrequirements (the requirement for linearity,
higher returns on investment (ROI) and higherfor example) and are especially useful in
returns per employee than less successfulrecognising and learning complex data
competitors. They achieve relatively higherrelationships. Recent ANN bankruptcy
returns through superior asset managementprediction studies include those of Bell, et
(capital and human assets) and throughal. (1990), Hansen & Messier (1991), Chung &
superior strategic positioning. In theTam (1992), Liang, et al. (1992), Tam & Kiang
absence of aggressive asset management,(1992), Salchenberger (1993), Coats & Fant
companies must usually resort to wholesale(1993), Fanning & Cogger (1994), Brockett, et
asset divestitures and/or are forced toal. (1994), Boritz, et al. (1995), and
restructure to fund their continuingEtheridge & Siriam (1995 and 1997). Research
operations. Strategic Position Schofflerhas shown that ANN's offer a viable
(Buzzell and Gale, 1987) and others havealternative to other more traditional methods
documented the high correlation betweenof bankruptcy prediction. The ability of the
positive returns on investment and suchmodel to learn allows for the constant
factors as: 1) higher relative market shares,re-calibration and validation of the model,
2) relative product quality and 3) lowerwhich helps increase classification rates.
relative capital intensity. Companies thatFrom a theoretical perspective, ANN's are
have strong strategic market positions aremore desirable because they make fewer
more likely to experience higher relativeassumptions about the data normality and
returns on investment than their competitors.linear separability. One of the main
These positive returns, in turn, increase thedisadvantages of ANN's is the inability to
solvency of the market leaders. Thoseassign intuition the network weights. Another
competitors that have lower market shares ordisadvantage is that the model might simply
lower product quality are less likely tomemorise the data as opposed to forming a
achieve industry average returns and are thusgeneral set of classification rules, which
more likely to become less solvent in thecan cause estimates on future samples to be
future. Predicting Financial Distress Inless reliable. Conclusion Future research in
America, each year approximately one percentbankruptcy prediction should analyse the
of all firms required to file with theeconomic and institutional factors that can
Securities and Exchange Commission file forimpact the reasons for bankruptcy. Jones
bankruptcy. The American Bankruptcy Institute(1987) indicated that the lack of homogeneity
reports that around 50,000 businesses filedin the motivation for a bankruptcy filing
for bankruptcy in 1997. Attempts to developmight complicate the modelling effort.
bankruptcy prediction models began seriouslyAlthough normally motivated by an effort to
sometime in the late 1960's and continueresolve severe financial problems, a firm may
through today. At least three distinct typesfile for bankruptcy primarily to void a union
of models have been used to predictcontract or for other legal reasons (Jones
bankruptcy: a) statistical models (univariate1987). Another area where models can be
analysis, multiple discriminate analysesimproved is in catering for predictor
[MDA]), and conditional logit regressionvariables other than financial ratios may
analyses, b) gambler's ruin-mathematicalprove beneficial. For example, measures of
statistical models, and c) artificial neuralmanagement experience, management expertise,
network models. Each of these models isor other behavioural aspects that impact the
discussed below. Most of the publiclyoperations of the firm could be significant
available information regarding predictionin a bankruptcy prediction model.
models is based on research published byAdditionally, including variables that
academics. Commercial banks, publiccontrol for a changing economic environment
accounting firms and other institutionalmay provide valuable insights for predicting
entities (ratings agencies, for example)bankruptcy. Bibliography References Altman,
appear to be the primary beneficiaries ofEdward I. Corporate Financial Distress. New
this research, since they can use theYork, NY: John Wiley and Sons, 1983. Altman,
information to minimise their exposure toEdward I. (1968) Financial Ratios,
potential client failures. While continuingDiscriminate Analysis and the Prediction of
research has been ongoing for almost thirtyCorporate Bankruptcy, The Journal of Finance.
years, it is interesting to note that noAltman, Edward I. Homepage of Professor
unified well-specified theory of how and whyEdward I. Altman, New York, NY: Stern School
corporations fail has yet been developed. Theof Business. Available at Altman, Edward I,
available statistical models derive merelyGiancarlo Marco, and Franco Varetto (1994)
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ratios. As stated by Wilcox (1973) the lackUsing Linear Discriminant Analysis and Neural
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Failure Prediction Models The early historyPrediction: An Empirical Analysis,, 1977, in
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