Determinants of Corporate Failure: The Case of the Johannesburg Stock Exchange

Queen Magadi Mabe and Wei Lin

MPRA Paper from University Library of Munich, Germany

Abstract: The aim of this paper is to estimate the probability of default for JSE listed companies. Our distinctive contribution is to use the multi-sector approach in estimating corporate failure instead of estimating failure in one sector, as failing companies are faced with the same challenge regardless of the sectors they operate in. The study creates a platform to identify the effect of Book-value to Market-value ratio on the probability to default, as this variable is often used as a proxy for corporate default in asset pricing models. Moreover, the use of Classification and Regression Trees uncovers other variables as reliable predictors to estimate corporate failure as the model is designed to choose the covariates with respect to classification ability. Our study also serves to add to the literature on how Logistic model performance compares to Machine Learning methods such as Classification and Regression Trees and Support Vector Machines. The study is the first to apply Support Vector Machines to predict failure on South African listed companies.

Keywords: Corporate default; Logistic Regression; Support Vector Machines; Classification and Regression Trees. (search for similar items in EconPapers)
JEL-codes: C61 G33 (search for similar items in EconPapers)
Date: 2018-08-08
New Economics Papers: this item is included in nep-big and nep-cfn
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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