Country risk analysis: an application of logistic regression and neural networks

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dc.contributor.author Ncube, Gugulethu
dc.date.accessioned 2017-12-21T10:08:23Z
dc.date.available 2017-12-21T10:08:23Z
dc.date.issued 2017
dc.identifier.citation Ncube, Gugulethu (2017) Country risk analysis: an application of logistic regression and neural networks, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/23554>
dc.identifier.uri http://hdl.handle.net/10539/23554
dc.description A research report submitted to the Faculty of Science, School of Statistics and Actuarial Science in partial fulfilment of the requirements for the degree of Master of Science, University of the Witwatersrand. Johannesburg, 08 June 2017. Mathematical Statistics degree, 2017 en_ZA
dc.description.abstract Country risk evaluation is a crucial exercise when determining the ability of countries to repay their debts. The global environment is volatile and is filled with macro-economic, financial and political factors that may affect a country’s commercial environment, resulting in its inability to service its debt. This re search report compares the ability of conventional neural network models and traditional panel logistic regression models in assessing country risk. The mod els are developed using a set of economic, financial and political risk factors obtained from the World Bank for the years 1996 to 2013 for 214 economies. These variables are used to assess the debt servicing capacity of the economies as this has a direct impact on the return on investments for financial institu tions, investors, policy makers as well as researchers. The models developed may act as early warning systems to reduce exposure to country risk. Keywords: Country risk, Debt rescheduling, Panel logit model, Neural net work models en_ZA
dc.format.extent Online resource (xii, 150 leaves)
dc.language.iso en en_ZA
dc.subject.lcsh Country risk
dc.subject.lcsh Investments
dc.subject.lcsh Neural networks
dc.subject.lcsh Mathematical statistics
dc.subject.lcsh World Bank
dc.title Country risk analysis: an application of logistic regression and neural networks en_ZA
dc.type Thesis en_ZA
dc.description.librarian XL2017 en_ZA


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