Multiclass Corporate Failure Prediction by Adaboost.M1

Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure or success of a corporation. Despite the complexity of the matter, a two-class problem has usually been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we apply the Adaboost.M1 algorithm to improve the accuracy of a classification tree in a multiclass corporate failure prediction problem using a set of European firms. On the other, we introduce novel discerning measures to rank independent variables in a generic classification task. Copyright International Atlantic Economic Society 2007