This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the rescaled range analysis, a backpropagation neural network is used to capture the relationship between the technical indicators and the levels of the index in the market under study over time. Using different trading strategies, a significant paper profit can be achieved by purchasing the indexed stocks in the respective proportions. The results show that the neural network model can get better returns compared with conventional ARIMA models. The experiment also shows that useful predictions can be made without the use of extensive market data or knowledge. The paper, however, also discusses the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems.
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