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Tbe Coben Commission and previous researcb bave suggested tbat auditors' opinions are inferior indicators of bankruptcy relative to tbe predictions of statistical models. Tbis researcb reexamines tbis question in Iigbt of two important considerations tbat make tbe comparison between audit opinions and model predictions considerably more reOective of tbe auditors' real-world dedsion environment. First, tbe sample is partitioned into stressed and nonstrnsed observations and tbe importance of doing so is demonstrated; second, tbe statistical models and tbe forecast errors are adjusted so tbat tbey refiect tbe proportion of bankrupt firms actually fiaced by auditors. Tbe empirical results provide convincing evidence suggesting tbat tbe notion establisbed in previous researcb tbat auditors' opinions are inferior to models in predicting bankruptcy is unfounded. It sbould be noted, bowever, tbat neitber tbe auditors' opinions nor tbe bankruptcy prediction model are very good predictors of bankruptcy wben population proportions, differences in misdasification costs, and finandal stress levels are considered. Rtsumi. Les travaux de recbercbe de la Commission Coben et d'autres travaux qui les ont pr6cedes semblent indiquer que les opinions des verificateurs sont des indicateuTS de faillite moins efficaces que les predictions des mod&les statistiques. Les auteurs se pencbent

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Abstract. This research empirically investigated the effect of nonnormality on financial stress prediction. The analysis included the application of prohit, logit and multiple discriminant analysis to prediction models found in previous literature, and also involved separate samples for both bankrupt and prohlem-status companies. Finally, the statistical techniques were evaluated under extreme conditions of nonnormality.Two basic procedures were used to modify the ratio distdhutions to achieve normality. These included a square-root transformation procedure and an outlier deletion procedure. Results were compared using hoth a univariate and a multivariate technique to identify and remove outliers. The results indicate the general sensitivity of the multiple discriminant analysis technique to departures from normality and the sensitivity of the logit and probit tectuiiques to extreme nonnormality. The data indicate that researchers interested in assessing classification accuracy might benefit by testing for distrihutional sensitivity using procedures outlined in this researeh.Risutni. Les auteurs ont proc^d6 k une analyse empirique de l'incidence des 6carts par rapport h. la nonnalit6 sur la provision des contraintes financi&res. L'analyse comporte Tapplication du probit, du logit et de l'analyse ^ discriminants multiples aux modMes pr^visionnnels que Ton trouve dans des publications, et l'on a eu recours ^ des ^chantillons distincts tant pour les socidt^s en situation de faillite que pour les soci6t6s en difficultd. Enfin, Ies techniques statistiques ont ii& 6valu6es dans des conditions extremes d'6cart par rapport ^ la normality.Deux m6thodes fondamentales ont €vt utilisdes pour modifier les distributions de ratios de fagon k parvenir & la normality. Ces methodes compiennent un proc^d^ de transformation de la racine can^e et un proc6d6 d'61imination des 6Mments isolds. Les rdsultats obtenus ont 6t6 compares ^ I'aide d'une technique univari6e ainsi que d'une technique multivari6e pour rep6reret supprimer les 616ments isol^s. Les rfisultats indiquent la sensibiht6 g6n&ale de la technique d'analyse'^ discriminants multiples aux ddviations par rapport & la normality et la sensihilitd des techniques logit et probit aux hearts extremes par rapport k la normality. Ces donn6es rdvfelent que les chercheurs qui s'int^ressent a l'^v^uation de l'axactitude de la classification pourraient tirer profit d'une verification de la sensibility de la distribution au moyen des mdthodes ddcrites par les auteurs. Financial Distress Prediction Models 285Introduction A major stream of research in the accounting literature has focused on the prediction of financial distress using linear statistical models and financial ratios (eg. Altman, Haldeman and Narayanan (1977), Ohlson (1980), and summaries in Zavgren (1983) and Foster (1986 ch. 15)). Frecka and Hopwood (1983) observed that financial ratios are typically skewed and nonnonnally distributed and that, in some cases, the nonnormality can be very extreme. They suggested that the prob...