Early Identification of Firms Requiring Debt Restructuring

Prior research has utilized publicly available data to predict failure/distress of financial institutions and corporations. Models have also been developed to distinguish firms that are likely to re-organize from those that are likely to liquidate after filing for bankruptcy. The literature, however, lacks studies aimed at identifying troubled firms that would require debt restructuring. This paper develops logit models to identify firms that will require debt restructuring up to two years in advance. A troubled firm requiring debt restructuring is defined by Statements of Financial Accounting Standards number 15 (SFAS # 15) as a firm whose creditors, for economic or legal reasons, will grant a "concession" which they would not otherwise consider. There are perhaps two reasons for lack of empirical studies of troubled firms requiring debt restructuring. First, neither an uniform definition nor a disclosure of debt restructuring of troubled firms existed prior to the issuance of SFAS # 15 in 1977. Second and more importantly, it is easier to distinguish bankrupt firms from the healthy ones. Bankruptcy represents an extreme case of financial difficulty. But distinguishing healthy firms from troubled ones requiring debt restructuring is much more difficult. Early identification of troubled firms can help financial institutions, investors, analysts, and other interested parties to reduce or avoid their potential losses. METHODOLOGY This study develops two logistic regression models to predict the odds of a firm requiring debt restructuring one and two years in advance. Logistic regression, like probit, is a maximum likelihood estimation technique that is appropriate for binary choice problems, such as classifying firms into those who will require debt restructuring and those who won't. Each logistic regression model developed is as follows: log( P sub i /l-P sub i ) = a + b sub 1 X sub 1 + b sub 2 X sub 2 + ... + b sub n X sub n where, P sub i = Probability that firm i will require debt restructuring X sub j = jth independent variable b sub j = Regression coefficient of jth independent variable. The independent variables used in this study are listed in Table 1. (table 1 omitted) A firm is classified as requiring debt restructuring if its probability of debt restructuring is greater than an optimum cut-off point. This study chooses the usual cut-off point of .5. This means that a firm with a probability of .5 or over is predicted to require debt restructuring. The lower the cut-off point, the larger will be the number of firs predicted to require debt restructuring. Stepwise (both forward and backward) selection technique was employed to screen the independent variables. With this procedure, variables are added to, or dropped from, a model, one at a time, based on their contribution to the overall fit of a model. An increase in the Chi-Squared goodness of fit value was used as a criterion for measuring the contribution of each independent variable. As such, from a total of 11 independent variables, four were selected for each of the two logit models. SAMPLE AND DATA The debt restructured firms were identified from the 10-K reports in the Company File of LEXIS/NEXIS Services, and the CD-ROM Disclosure data base. Thirty six firms had debt restructuring over a period of four years (1987-1990). Twelve of these firms were not listed o the COMPUSTAT PC Plus CD-ROM and thus were dropped, reducing the number of first with debt restructuring to 24. Several firms had a multiple number of debt restructurings. But we chose only the first year of their restructuring. For each of the 24 debt restructured firms, we selected a matching healthy firm. The healthy firms had no financial difficulty during the last 10 years, i.e., they had no operating loss for any two consecutive years, no working capital deficiency in any year, and no negative operating cash flows in any year. …