Corporate default prediction with industry effects: evidence from emerging markets

The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors. Firm-specific data accompany with industry and macroeconomic factors offer a potentially large number of candidate predictors of corporate default. We employ a pred...

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Main Authors: Mirzaei, Maryam, Ramakrishnan, Suresh, Bekri, Mahmoud
Format: Article
Published: Econjournals 2016
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Online Access:http://eprints.utm.my/id/eprint/69095/
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spelling my.utm.690952017-11-20T08:52:14Z http://eprints.utm.my/id/eprint/69095/ Corporate default prediction with industry effects: evidence from emerging markets Mirzaei, Maryam Ramakrishnan, Suresh Bekri, Mahmoud QA Mathematics The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors. Firm-specific data accompany with industry and macroeconomic factors offer a potentially large number of candidate predictors of corporate default. We employ a predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logistic regression model. Overall entire analyses indicate that the orientation between firm-level determinants and the probability of default is affected by each industry’s characteristics. As well, our selection method represents an efficient way of introducing non-linear effects of predictor variables on the default probability. Econjournals 2016 Article PeerReviewed Mirzaei, Maryam and Ramakrishnan, Suresh and Bekri, Mahmoud (2016) Corporate default prediction with industry effects: evidence from emerging markets. International Journal of Economics and Financial Issues, 6 (3). pp. 161-169. ISSN 2146-4138 http://www.scopus.com
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/
topic QA Mathematics
spellingShingle QA Mathematics
Mirzaei, Maryam
Ramakrishnan, Suresh
Bekri, Mahmoud
Corporate default prediction with industry effects: evidence from emerging markets
description The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors. Firm-specific data accompany with industry and macroeconomic factors offer a potentially large number of candidate predictors of corporate default. We employ a predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logistic regression model. Overall entire analyses indicate that the orientation between firm-level determinants and the probability of default is affected by each industry’s characteristics. As well, our selection method represents an efficient way of introducing non-linear effects of predictor variables on the default probability.
format Article
author Mirzaei, Maryam
Ramakrishnan, Suresh
Bekri, Mahmoud
author_facet Mirzaei, Maryam
Ramakrishnan, Suresh
Bekri, Mahmoud
author_sort Mirzaei, Maryam
title Corporate default prediction with industry effects: evidence from emerging markets
title_short Corporate default prediction with industry effects: evidence from emerging markets
title_full Corporate default prediction with industry effects: evidence from emerging markets
title_fullStr Corporate default prediction with industry effects: evidence from emerging markets
title_full_unstemmed Corporate default prediction with industry effects: evidence from emerging markets
title_sort corporate default prediction with industry effects: evidence from emerging markets
publisher Econjournals
publishDate 2016
url http://eprints.utm.my/id/eprint/69095/
http://www.scopus.com
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score 13.187197