AN EXPLORATION INTO BANKRUPTCY DISCRIMINANT MODEL SENSITIVITY

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Abstract
Prediction has been a central theme in much of the accounting research and theory construction and verification over the past decade. Largely ignored in such studies has been consideration of the statistical properties of accounting measures, particularly as related to the effects of those properties on the signals from prediction models that use accounting measures as inputs. This study was designed to provide preliminary insight into the magnitude of the effects of this omission, and a bankruptcy prediction model was selected to facilitate the analysis. Results indicate that the linear discriminant model (as applied to prediction of failure) is sensitive to departures of inputdata distributions from multivariate normal.