Abstract
This paper focuses on the development of a stock market forecasting model based on artificial neural network architecture. A baseline neural network model was developed using GFF architecture. The performance of the baseline model was evaluated by using representative large-cap stocks in six critical industrial sectors. Key performance measures, which included correlation coefficient and mean square error, were identified and used to compare the different models. A self-organising map network was developed to reduce the set of 56 stock market indicators into a final set of 11 indicators that covered market momentum, market volatility, market trend, broad market indictors and general momentum indicators. The model still required additional developments to better forecast turning points in the market. Based on Elliot's Wave Theory, two additional indicators were introduced to improve the forecast accuracy for turning points.
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