Corporate Default Prediction with AdaBoost and Bagging Classifiers

Authors

  • Suresh Ramakrishnan Finance and Accounting Department, Faculty of Management, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Maryam Mirzaei Finance and Accounting Department, Faculty of Management, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Mahmoud Bekri Economic and Statistic Institute, Karlsruhe Institute of Technology, Germany

DOI:

https://doi.org/10.11113/jt.v73.4191

Keywords:

Default prediction, Adaboost, bagging, data mining

Abstract

This study aims to show a substitute technique to corporate default prediction. Data mining techniques have been extensively applied for this task, due to its ability to notice non-linear relationships and show a good performance in presence of noisy information, as it usually happens in corporate default prediction problems. 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 ensemble classifier systems is assessed in terms of their capability to appropriately classify default and non-default Malaysian firms listed in Bursa Malaysia. AdaBoost and Bagging are novel ensemble learning algorithms that construct the base classifiers in sequence using different versions of the training data set. In this paper, we compare the prediction accuracy of both techniques and single classifiers on a set of Malaysian firms, considering the usual predicting variables such as financial ratios. We show that our approach decreases the generalization error by about thirty percent with respect to the error produced with a single classifier. 

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Published

2015-03-09

Issue

Vol. 73 No. 2: Special Issue on International Conference of Recent Trends in Information and Communication Technology (IRICT 2014)

Section

Science and Engineering

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Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

Corporate Default Prediction with AdaBoost and Bagging Classifiers. (2015). Jurnal Teknologi (Sciences & Engineering), 73(2). https://doi.org/10.11113/jt.v73.4191