Principles of Corporate Renewal, Second Edition

3. Failure Prediction

CHAPTER 3 Failure Prediction In this chapter, we examine a number of techniques to measure corporate health and ways to predict company failure. Ratio techniques are the first failure-prediction methods discussed in this chapter. From ratio techniques we move into a discussion of financial and operating leverage , two alternative measures of corporate risk. Following that, early warning system (EWS) models are presented. Then, all of these methods are applied in an example of the performance of a corporate turnaround . At the end of the chapter, two other measures of performance and risk, sustainable growth rate and EVA, are examined. Lenders, customers, suppliers, and stockholders are keenly interested in being warned before a company becomes financially distressed or, even worse, fails. These parties risk substantial financial losses and would have to endure innumerable hours of paperwork and meetings after receiving one of the following nasty surprises: • nonpayment of a contractual obligation • unfulfilled warranty services • distressed debt restructurings • formal bankruptcy filings Their probable losses increase as their seniority position declines and their collateral value dwindles. Even when a formal bankruptcy filing is not an issue, expenses paid by institutions attempting to work out nonpaying accounts may amount to 5 percent or even 10 percent of loan values (Platt 1993-94). Losses are even greater after a bankruptcy filing; Altman (1984) documents total bankruptcy costs ranging between 11 percent and 17 percent of firm value. These expenditures are deadweight losses depleting assets that remain for creditors. Companies should evaluate the creditworthiness of potential clients .1 The least creditworthy should be rejected, while those on the 75 76 Principles of Corporate Renewal border of high risk should be asked for deposits or other forms of security. Few firms allot sufficient resources to implementing failureprediction techniques despite the tremendous financial losses they risk from distressed clients. Undercommitment to bankruptcy prediction results from • excessive optimism or an unwillingness to acknowledge potential failure2 • unfamiliarity with advances in failure prediction and • corporate organizations that delink lenders from the consequences of unsound loans Instead of avoiding the costs of failure by predicting it, some financial firms treat failure as a nonzero probability risk and increase their fees to compensate for expected losses. Subprime lenders report nonpayments of 15 percent or more during recessions. This inferior strategy results in a higher cost structure and lower profits. Not every failure is predictable. No single set of factors unfailingly predicts either corporate distress or bankruptcy. Yet there are compelling similarities between healthy enterprises, on the one hand, and unhealthy firms, on the other. The key to developing a feasible failureprediction model is to understand these similarities and the reasons why a single model is insufficient. A major reason why a single model cannot predict failure across all firms is the extent of differences among industries. These include special practices and regulations, idiosyncrasies in bankruptcy and common laws, and lending guidelines established by financial institutions. Although no single framework works for all, failure within particular populations of firms can be predicted with a reasonable degree of accuracy. 3 Examples of industry-specific bankruptcy-prediction models include those for savings and loan institutions, commercial banks, industrial corporations, oil and gas companies, and initial public offerings. Each group of companies has a unique set of predictors. Failure prediction is not a modern idea. Lenders historically have tried to determine the quality of loan applicants, distressed bond buyers have pursued "money good" credits, and trade creditors have attempted to avoid shipping products to poor-risk clients, all with varying degrees of success. What is new is the application of scientific skills to the ancient art of failure prediction. Since 1965 academic research has extensively studied bankruptcy prediction, using EWS models. EWS models provide a quantitative assessment of company risk. These risk estimates play two roles in corporate renewal: Failure Prediction 77 1. as analytical screens aiding the choice of investments, loans, or clients 2. to guide a turnaround by assessing the health of the distressed firm (Altman and LaFleur 1981; Platt and Platt 1991a). In the first role, the EWS is employed to evaluate instantly hundreds or even thousands of companies, and poor performers are identified. In the second role, the corporate renewal agent measures the success or failure of corrective actions for a particular client firm by the quarter-to-quarter changes in the EWS model estimates. Despite these innovations, some corporate renewal specialists eschew computers and statistical modeling techniques. Instead, they rely on more elemental methods such as financial-ratio analysis. Simple financial...