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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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 |
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QA Mathematics Mirzaei, Maryam Ramakrishnan, Suresh Bekri, Mahmoud Corporate default prediction with industry effects: evidence from emerging markets |
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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. |
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Mirzaei, Maryam Ramakrishnan, Suresh Bekri, Mahmoud |
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Mirzaei, Maryam Ramakrishnan, Suresh Bekri, Mahmoud |
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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 |
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Corporate default prediction with industry effects: evidence from emerging markets |
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Corporate default prediction with industry effects: evidence from emerging markets |
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corporate default prediction with industry effects: evidence from emerging markets |
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Econjournals |
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2016 |
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http://eprints.utm.my/id/eprint/69095/ http://www.scopus.com |
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