Using neural networks and support vector machines for default prediction in South Africa
| dc.contributor.author | Meltzer, Frances | |
| dc.date.accessioned | 2018-12-05T09:11:54Z | |
| dc.date.available | 2018-12-05T09:11:54Z | |
| dc.date.issued | 2017 | |
| dc.description | A thesis submitted to the Faculty of Computer Science and Applied Mathematics, University of Witwatersrand, in fulfillment of the requirements for the Master of Science (MSc) Johannesburg Feb 2017 | en_ZA |
| dc.description.abstract | This is a thesis on credit risk and in particular bankruptcy prediction. It investigates the application of machine learning techniques such as support vector machines and neural networks for this purpose. This is not a thesis on support vector machines and neural networks, it simply looks at using these functions as tools to preform the analysis. Neural networks are a type of machine learning algorithm. They are nonlinear mod- els inspired from biological network of neurons found in the human central nervous system. They involve a cascade of simple nonlinear computations that when aggre- gated can implement robust and complex nonlinear functions. Neural networks can approximate most nonlinear functions, making them a quite powerful class of models. Support vector machines (SVM) are the most recent development from the machine learning community. In machine learning, support vector machines (SVMs) are su- pervised learning algorithms that analyze data and recognize patterns, used for clas- si cation and regression analysis. SVM takes a set of input data and predicts, for each given input, which of two possible classes comprises the input, making the SVM a non-probabilistic binary linear classi er. A support vector machine constructs a hyperplane or set of hyperplanes in a high or in nite dimensional space, which can be used for classi cation into the two di erent data classes. Traditional bankruptcy prediction medelling has been criticised as it makes certain underlying assumptions on the underlying data. For instance, a frequent requirement for multivarate analysis is a joint normal distribution and independence of variables. Support vector machines (and neural networks) are a useful tool for default analysis because they make far fewer assumptions on the underlying data. In this framework support vector machines are used as a classi er to discriminate defaulting and non defaulting companies in a South African context. The input data required is a set of nancial ratios constructed from the company's historic nancial statements. The data is then Divided into the two groups: a company that has defaulted and a company that is healthy (non default). The nal data sample used for this thesis consists of 23 nancial ratios from 67 companies listed on the jse. Furthermore for each company the company's probability of default is predicted. The results are benchmarked against more classical methods that are commonly used for bankruptcy prediction such as linear discriminate analysis and logistic regression. Then the results of the support vector machines, neural networks, linear discriminate analysis and logistic regression are assessed via their receiver operator curves and pro tability ratios to gure out which model is more successful at predicting default. | en_ZA |
| dc.description.librarian | MT 2018 | en_ZA |
| dc.format.extent | Online resource (145 leaves) | |
| dc.identifier.citation | Meltzer, Frances (2017) Using neural networks and support vector machines for default prediction in South Africa, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/26178> | |
| dc.identifier.uri | https://hdl.handle.net/10539/26178 | |
| dc.language.iso | en | en_ZA |
| dc.subject.lcsh | Machine learning | |
| dc.subject.lcsh | Support vector machines | |
| dc.title | Using neural networks and support vector machines for default prediction in South Africa | en_ZA |
| dc.type | Thesis | en_ZA |