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FACTORS AFFECTING FINANCIAL HEALTH 3 Capital Structure and Capital Adequacy 3 Operating Cash Flows and Cost Structure 4 Asset Conversions Growing Broke 5 Asset Utilisation Efficiency/Turnover 5 Other Statistical Failure Prediction Models 10 Alternative Models - Artificial Neural Networks 12 A company trying to achieve its business plan faces problems similar to those faced by a driver embarking on a long trip. The likelihood that car and driver will reach their destination is dependent on: 1) how much fuel is in the car's tank upon starting out, 3) how many service stations will be available to refill the car's fuel tank along the way and 4) whether the car's fuel tank is large enough to cover unexpected accidents, delays, and detours along the way. Similarly, whether or not a company survives in a highly competitive business environment is dependent upon: 1) how financially healthy the corporation is at its inception, 2) the company's ability (and relative flexibility and efficiency) in creating cash from its continuing operations, 3) the company's access to capital markets, and 4) the company's financial capacity and staying power when faced with unplanned cash shortfalls. There is no single measure of financial health. Ideally, solvency could be measured along a continuum in the same way that fuel sufficiency can be measured using a car's petrol gauge. Full health would equate with having a full tank of fuel.
Poor health would be equivalent to showing an empty tank. As healthiness progressively decreased, the solvency gauge would register movement in the direction of relative insolvency. Ultimately, as healthiness continues to decline, the solvency gauge would hopefully flash a warning light. Since, in the real world, no single measure of financial health exists, proxies that measure various aspects of solvency are often combined to estimate a company's healthiness at a point in time. As a financially healthy company becomes more and more financially distressed, it ultimately enters an area of great danger. Changes to the company's operations and capital structure (ie.
restructuring) must be made to remain healthy. Apple Computers' attempts in recent years to restructure its operations to survive in the highly competitive computer hardware business is a good example of a company trying to dramatically restructure itself in order to maintain solvency. Continued decreases in financial health ultimately lead to insolvency and then potentially, bankruptcy. Available evidence suggests many companies do not adequately attempt to resolve their financial health problems until it is too late to avoid bankruptcy. Capital Structure and Capital Adequacy Companies finance their long-term operations primarily through two sources of capital - debt and equity. One of the most important financing decisions a company makes is the proportion of debt to owner's equity in the company's capital structure.
Summary measures of a company's capital structure include the company's debt to equity ratio (D/E) and debt to total capital ratio (D/ (D+E) ). Interest and principal payments on debt must be paid from operations before any payments can be distributed to equity holders (in the form of dividends or share buy-backs). Therefore, the interest and principal, which must be paid on debt, are considered fixed-costs of operations. From an operational point-of-view, the extent of the burden of these fixed obligations can be measured relative to the company's continuing ability to pay the fixed obligations.
A frequently used measure of a company's ability to cover its interest payments is its earnings before interest and taxes and before depreciation and amortisation (EBITDA) to its interest expense. A company is financially distressed whenever its EBITDA is less than its interest expense. &# 61623; Financial leverage involves the substitution of fixed-cost debt for owner's equity in the hope of increasing equity returns. As demonstrated by Higgins and others, financial leverage improves financial performance when things are going well but worsens financial performance when things are going poorly. Therefore, increasing the ratio of debt to equity in a company's capital structure implicitly makes the company relatively less solvent (on the downside) and more financially risky than a company without debt. &# 61623; Capital adequacy relates to whether a company has enough capital to finance its planned future operations. If the company's capital is inadequate, then it must either be able to: 1) successfully issue new equity, or The amount of debt a company can successfully absorb and repay from its continuing operations is normally referred to as the company's debt capacity. Capital adequacy is normally evaluated by looking at the company's operational cash flow projections and its projections of capital needs.
When companies undertake major new projects or undergo a significant financial restructuring they often perform financial feasibility studies to determine whether the company has the financial capacity to undertake the project and whether the company will be able to repay all future debt payments once the project is built. Operating Cash Flows and Cost Structure All other factors being equal, companies that can consistently generate positive cash flows from operations will remain relatively more solvent than those that cannot. This requires that operating cash inflows (collections or sales) consistently exceed operating cash outflows (costs). Companies which experience erratic cash outflows and inflows are relatively more risky because they are less likely, in one or more time periods, to be able to cover fixed expenses / outflows . Companies which have a higher proportion of fixed costs to variable costs are also relatively more risky and relatively less solvent than companies with a relatively lower proportion of fixed costs in their operating cost structure. All other things being equal, companies with higher relative earnings and higher relative returns on investment will remain more solvent than their less fortunate competitors.
The most commonly used financial measures of earnings capacity are earnings before interest and taxes (EBIT) and net income. Adequate liquidity is a further necessary component of solvency. Frequently used liquidity measures include: a) working capital (current assets minus current liabilities), b) current ratio (current assets divided by current liabilities), and c) quick ratio (cash, marketable securities and accounts receivable divided by current liabilities). To evaluate liquidity, each of the assets and liabilities on a company's balance sheet should be evaluated for liquidity. Current assets are those which will likely be converted to cash within one year or less.
Current liabilities are those which must be paid within one year. However, when a company becomes financially distressed, even assets which are normally considered current assets (accounts receivable and stock, for example) may become relatively illiquid. Long-term assets, in general, are far less liquid than current assets. Some longer-term assets may be very illiquid. Also, as stated above, often a company's long-term liabilities can become immediately due and payable if the company violates contractual debt covenants or other obligations. Wilcox (1976) argues that "net liquidation value" provides a solid conceptual basis for evaluating a company's liquidity.
Net liquidation value is defined as total asset liquidation value less total liabilities. Wilcox (1976) applies what he calls typical (not definitive) valuation multipliers to balance sheet assets to arrive at representative asset liquidation values: Wilcox (1976) shows that a company becomes bankrupt when net liquidation value is reduced to zero. Asset and liability conversions are continuously ongoing in any dynamic business. Operationally, the company is selling its products thereby creating cash inflows. Alternatively, sales may be made on credit, increasing the company's accounts receivable. Concurrently, inventories are produced and sold and production and operating expenses are incurred to continue operations.
If a company's inventories and accounts receivable grow faster than the corresponding growth in the company's sales and accounts payable, liquidity will be negatively affected. Strategic asset conversions are also ongoing, but with less regularity. Decisions to invest in bricks and mortar and other long-term investments are made and debt and equity are obtained to supply the capital needed to pay for them. Slowly but surely, companies can go broke when assets are converted to less liquid forms over a sustained time period. This can happen when the company's assets grow faster than the company's sales (often the case for many start-up companies).
When this happens, the company becomes more highly leveraged and less solvent. Similarly, a company whose long term investment decisions do not pay off in terms of planned operating returns (thus increasing fixed cost structures and decreasing operating cash flows), will become less solvent. Asset Utilisation Efficiency/Turnover Those companies, which survive, use their human and capital assets relatively efficiently. That is, they have relatively higher returns on investment (ROI) and higher returns per employee than less successful competitors. They achieve relatively higher returns through superior asset management (capital and human assets) and through superior strategic positioning.
In the absence of aggressive asset management, companies must usually resort to wholesale asset divestitures and / or are forced to restructure to fund their continuing operations. Schoffler (Buzzell and Gale, 1987) and others have documented the high correlation between positive returns on investment and such factors as: 3) lower relative capital intensity. Companies that have strong strategic market positions are more likely to experience higher relative returns on investment than their competitors. These positive returns, in turn, increase the solvency of the market leaders. Those competitors that have lower market shares or lower product quality are less likely to achieve industry average returns and are thus more likely to become less solvent in the future. In America, each year approximately one percent of all firms required to file with the Securities and Exchange Commission file for bankruptcy.
The American Bankruptcy Institute reports that around 50, 000 businesses filed for bankruptcy in 1997. Attempts to develop bankruptcy prediction models began seriously sometime in the late 1960 's and continue through today. At least three distinct types of models have been used to predict bankruptcy: a) statistical models (univariate analysis, multiple discriminate analyses [MDA]), and conditional logit regression analyses, b) gambler's ruin-mathematical / statistical models, and c) artificial neural network models. Each of these models is discussed below. Most of the publicly available information regarding prediction models is based on research published by academics. Commercial banks, public accounting firms and other institutional entities (ratings agencies, for example) appear to be the primary beneficiaries of this research, since they can use the information to minimise their exposure to potential client failures.
While continuing research has been ongoing for almost thirty years, it is interesting to note that no unified well-specified theory of how and why corporations fail has yet been developed. The available statistical models derive merely from the statistical optimisation of a set of ratios. As stated by Wilcox (1973) the "lack of conceptual framework results in the limited amount of available data on bankrupt firms being statistically 'used up' by the search before a useful generalisation emerges. " Almost universally, the decision criterion used to evaluate the usefulness of the models has been how well they classify a company as solvent or non-solvent compared to the company's actual status known after-the-fact. Most of the studies consider a type I error as the classification of a failed company as healthy, and consider a type II error as the classification of a healthy company as failed.
In general, type I errors are considered more costly to most users than type II errors. The usefulness of fail / non -fail prediction models is suggested by Nelson (1980)... real world problems concern themselves with choices which have a richer set of possible outcomes. No decision problem I can think of has a payoff space which is partitioned naturally into the binary status bankruptcy versus non-bankruptcy... I have also refrained from making inferences regarding the relative usefulness of alternative models, ratios and predictive systems... Most of the analysis should simply be viewed as descriptive statistics - which may, to some extent, include estimated prediction error-rates - and no "theories" of bankruptcy or usefulness of financial ratios are tested.
Subject to the qualifications expressed above, bankruptcy prediction models continue to be used to predict failure. The early history of researchers' attempts to classify and predict business failure (and bankruptcy) is well documented in Edward Altman's 1983 book, Corporate Financial Distress. Statistical prediction models are more generally better known as measures of financial distress. Three stages in the development of statistical financial distress models exist: 2.
multivariate (or multi-discriminate [MDA]) analysis, and Univariate analysis assumes "that a single variable can be used for predictive purposes" (Cook and Nelson 1998). The univariate model as proposed by William Beaver achieved a "moderate level of predictive accuracy" (Sheppard 1994). Univariate analysis identified factors related to financial distress, however, it did not provide a measure of the relevant risk (Stickney 1996). In the next stage of financial distress measurement, multivariate analysis (also known as multiple discriminant analysis or MDA) attempted to "overcome the potentially conflicting indications that may result from using single variables" (Cook and Nelson 1998).
The best-known, and most-widely used, multiple discriminant analysis method is the one proposed by Edward Altman. Altman's z-score, or zeta model, combined various measures of profitability or risk. The resulting model was one that demonstrated a companys risk of bankruptcy relative to a standard. Altman's initial study proved his model to be very accurate; it correctly predicted bankruptcy in 94 % of the initial sample (Altman 1968). Despite the positive results of his study, Altman's model had a key weakness; it assumed variables in the sample data to be normally distributed. "If all variables are not normally distributed, the methods employed may result in selection of an inappropriate set of predictors" (Sheppard 1994). Christine Zavgren developed a model that corrected for this problem.
Her model used logit analysis to predict bankruptcy. Due to its use of logit analysis, her model is considered "more robust" (Lo 1986). Further, logit analysis actually provides a probability (in terms of a percentage) of bankruptcy. Also, the probability calculated might be considered a measure of the effectiveness of management (ie. effective management will not lead a company to the verge of bankruptcy). During the 1980 s and 1990 s, the trend has been to use logit analysis in favour of multiple discriminant analysis (Stickney 1996).
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Research essay sample on Models For Predicting Corporate Financial Distress
