Benchmarking a neural network forecaster against statistical measures

M.Ing. (Mechanical Engineering)The combination of non-linear signal processing and financial market forecasting is a relatively new field of research. This dissertation concerns the forecasting of shares quoted on the Johannesburg Stock Exchange by using Artificial Neural Networks, and does so by comparing neural network results with established statistical results. The share price rise or fall are predicted as well as buy, sell and hold signals and compared to Time Series model and Moving Average Convergence Divergence results. The dissertation will show that artificial neural networks predict the share price rise or fall with less error than statistical models and yielded the highest profit when forecasting buy, sell and hold signals for a particular share