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     Research Journal of Mathematics and Statistics


Predicting the Probability of Loan-Default: An Application of Binary Logistic Regression

1Charles Kwofie, 1Caleb Owusu-Ansah and 2Caleb Boadi
1Department of Statistics, University of Ghana, P.O. Box LG 25
2School of Business, University of Ghana, P.O. Box LG 25, Legon-Accra, Ghana
Research Journal of Mathematics and Statistics   2015  4:46-52
http://dx.doi.org/10.19026/rjms.7.2206  |  © The Author(s) 2015
Received: January ‎14, ‎2015  |  Accepted: February ‎14, ‎2015  |  Published: November 25, 2015

Abstract

This study examines the performance of logistic regression in predicting probability of default using data from a microfinance company. A logistic regression analysis was conducted to predict default status of loan beneficiaries using 90 sampled beneficiaries for model building and 30 out of sample beneficiaries for prediction. Age, marital status, gender number of years of education, number of years in business and base capital were used as predictors. The predictors that were significant in the model were marital status, number of years in business and base capital. The explained variability in the response variable in the logistic regression was very weak.

Keywords:

Default, loan, logistic regression, response variable,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7505
ISSN (Print):   2042-2024
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