In this study the prediction capabilities of Artificial Neural Networks and typical econometric methods are compared. This is done in the domains of Finance and Economics. Initially, the Neural Networks are shown to outperform traditional econometric models in forecasting nonlinear behaviour. The comparison is extended to indicate that the accuracy of share price forecasting is not necessarily improved when applying Neural Networks rather than traditional time series analysis. Finally, Neural Networks are used to forecast the South African inflation rates, and its performance is compared to that of vector error correcting models, which apparently outperform Artificial Neural Networks. / Prof. D.J. Marais
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:2910 |
| Date | 17 June 2008 |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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