A boosting approach for corporate failure prediction
| Autor: | Matías Gámez Martínez, Noelia Rubio, Esteban Alfaro Cortés |
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| Rok vydání: | 2006 |
| Předmět: |
Variables
Boosting (machine learning) Computer science business.industry media_common.quotation_subject Decision tree learning Financial ratio Machine learning computer.software_genre Generalization error Corporate failure Artificial Intelligence Profitability index Artificial intelligence business computer media_common |
| Zdroj: | Applied Intelligence. 27:29-37 |
| ISSN: | 1573-7497 0924-669X |
| DOI: | 10.1007/s10489-006-0028-9 |
| Popis: | 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/success of a corporation. Despite the importance of this problem, until now only classical machine learning tools have been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we introduce novel discerning measures to rank independent variables in a generic classification task. On the other hand, we apply boosting techniques to improve the accuracy of a classification tree. We apply this methodology to a set of European firms, considering the usual predicting variables such as financial ratios, as well as including novel variables rarely used before in corporate failure prediction, such as firm size, activity and legal structure. We show that our approach decreases the generalization error about thirty percent with respect to the error produced with a classification tree. In addition, the most important ratios deal with profitability and indebtedness, as is usual in failure prediction studies. |
| Databáze: | OpenAIRE |
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