Forecasting the KLSE index using neural networks
JingTao Yao, Hean-Lee Poh · 2002
In this paper, based on the rescaled range analysis, the indices of Kuala Lumpur Stock Exchange (KLSE) are predicted by the popularly used backpropagation neural network. The choice of KLSE is an interesting one, as KLSE is one of the largest stock markets in the emerging economies in terms of capitalization. Using different trading strategies, a significant paper profit can be achieved by purchasing indexed stocks in the respective proportions. The experiment shows that useful predictions can be made without the use of extensive market data or knowledge.