ABSTRACT
No analytic procedures currently exist for determining optimal artificial neural network structures and
parameters for any given application. Traditionally, when artificial neural networks have been applied
to financial modelling problems, structure and parameter choices are often made a priori without
sufficient consideration of the effect of such choices. A key aim of this study is to develop a general
method that could be used to construct artificial neural networks by exploring the model structure and
parameter space so that informed decisions could be made relating to the model design. In this study,
a formal approach is followed to determine suitable structures and parameters for a Feed Forward
Multi-layered Perceptron artificial neural network with a Resilient Propagation learning algorithm with
a single hidden layer. This approach is demonstrated through the modelling of four South African
economic variables, namely the average monthly returns on the money, bond and equity markets as
well as monthly inflation. Artificial neural networks can be constructed on the aforementioned variables
in isolation or, jointly, in an integrated model. The performance of a range of more traditional time
series models is compared with that of the artificial neural network models. The results suggest that,
on a statistical level, artificial neural networks perform as well as time series models at forecasting the
returns for financial markets. Hybrid models, combining artificial neural networks with the time series
models, are constructed, trained and tested for the money market and for the rate of inflation. They
appear to add value to the time series models when forecasting inflation, but not for the money market.