Corporate default prediction with data mining techniques

Abstract

Default has recently upraised as an excessive concern due to the recent world crisis. Early forecasting of firms default provides decision-support information for financial and regulatory institutions. In spite of several progressive methods that have widely been proposed, this area of research is not out dated and still needs further examination. In this paper, the performance of different multiple classifier systems are assessed in terms of their capability to appropriately classify default and non-default Iranian firms listed in Tehran Stock Exchange (TSE). On the other hand, TSE have had very high return which provided more than 140 percent return in last year. For this reason, TSE could be more attractive for investors. Most multi- stage combination classifiers provided significant improvements over the single classifiers. In addition, Adaboost afford enhancement in performance over the single classifiers.