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Corporate default prediction with adaboost and bagging classifiers

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 d...

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Main Authors: Ramakrishnan, Suresh, Mirzaei, Maryam, Bekri, Mahmoud
Format: Article
Language:English
Published: Penerbit UTM Press 2015
Subjects:
HG Finance
Online Access:http://eprints.utm.my/id/eprint/58171/1/SureshRamakrishnan2015_CorporateDefaultPrediction.pdf
http://eprints.utm.my/id/eprint/58171/
http://dx.doi.org/10.11113/jt.v73.4191
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spelling my.utm.581712021-12-20T02:01:24Z http://eprints.utm.my/id/eprint/58171/ Corporate default prediction with adaboost and bagging classifiers Ramakrishnan, Suresh Mirzaei, Maryam Bekri, Mahmoud HG Finance 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. Penerbit UTM Press 2015 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/58171/1/SureshRamakrishnan2015_CorporateDefaultPrediction.pdf Ramakrishnan, Suresh and Mirzaei, Maryam and Bekri, Mahmoud (2015) Corporate default prediction with adaboost and bagging classifiers. Jurnal Teknologi, 73 (2). pp. 45-50. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v73.4191 DOI: 10.11113/jt.v73.4191
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic HG Finance
spellingShingle HG Finance
Ramakrishnan, Suresh
Mirzaei, Maryam
Bekri, Mahmoud
Corporate default prediction with adaboost and bagging classifiers
description 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.
format Article
author Ramakrishnan, Suresh
Mirzaei, Maryam
Bekri, Mahmoud
author_facet Ramakrishnan, Suresh
Mirzaei, Maryam
Bekri, Mahmoud
author_sort Ramakrishnan, Suresh
title Corporate default prediction with adaboost and bagging classifiers
title_short Corporate default prediction with adaboost and bagging classifiers
title_full Corporate default prediction with adaboost and bagging classifiers
title_fullStr Corporate default prediction with adaboost and bagging classifiers
title_full_unstemmed Corporate default prediction with adaboost and bagging classifiers
title_sort corporate default prediction with adaboost and bagging classifiers
publisher Penerbit UTM Press
publishDate 2015
url http://eprints.utm.my/id/eprint/58171/1/SureshRamakrishnan2015_CorporateDefaultPrediction.pdf
http://eprints.utm.my/id/eprint/58171/
http://dx.doi.org/10.11113/jt.v73.4191
_version_ 1720436865411055616
score 13.211869

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