EXHIBIT 2 AN EXAMPLE OF THE HIERARCHY OF RELATIVE CASH FLOW COMPONENTS UNDER VARIOUS RISK CONDITIONS Relative Cash Flow Components ( CFC * ) Net Operating

/ The primary contribution of this study is the use of cash flow components in an inductive learning system to predict financial failure. The underlying conceptual framework associated with inductive learning is presented, and an example of entropy is developed in an appendix. The sample included 14 cash flow components and two qualitative variables for 198 companies, 99 failed and 99 nonfailed companies. These inputs were used in a C4.5 inductive learning program to predict the failed/nonfailed status of the sample companies. The program induces a decision tree that reflects the structure of the inputs used to classify the companies as being failed or nonfailed. A global tree interpretation combined with a jackknife procedure was used to repeat the experiment 198 times, which resulted in a predictive accuracy of 86 percent. A global tree represents a composite of the 198 induced trees. The global tree approach indicates that knowing the level of dividends * * * (DIV ), net capital investment (NIF ) and net operating cash flow (NOF ) results in the correct identification of 89 percent of the failed and nonfailed companies. The inductive learning test results were superior to the 67.5 percent predictive accuracy of a series of probit tests. The results of these tests are encouraging and indicate the need for further study in the use of inductive learning systems in predicting and interpreting financial performance. USING INDUCTIVE LEARNING TO PREDICT BANKRUPTCY'' / Since the mid 1960s numerous empirical models have been developed that use annual financial information to discriminate between firms that declare bankruptcy and the ones that remain solvent. In general these models lack an underlying theory (Scott [1976]) and their results are dependent on the time period studied, the firms included in the sample and the statistical methodology used. During the past decade a separate stream of studies used market determined returns and risk measures to explain the bankruptcy process, reorganization, and the costs associated with bankruptcy. Finally, there was a third stream of theoretical research that used security pricing formulas to explain corporate bankruptcy. ^ Although the bankruptcy literature is extensive, there is continued interest in the development of a theoretical foundation that would capture the many dimensions of financial distress and failure. Likewise there are numerous lenders and investors who are deeply interested in improving their ability to explain, interpret and predict bankruptcy. Most of the studies use financial ratios in a statistical model such as multiple discriminant analysis, probit or logit. However, cash flow information provides unique and subtle insights into the prediction of bankruptcy, bond ratings and loan risk ratings. A fundamental contribution of this study is to use cash flow components in an inductive learning system to predict if a firm is either bankrupt or nonbankrupt. Inductive learning is a relatively new analytical approach that is based on an information theory concept called entropy.