Equity Forecasting: a Case Study on the Klse Index

This paper presents the research of neural networks as applied in equity forecast ing in an emerging market such as the Kuala Lumpur Stock Exchange KLSE Backpropagation neural networks are used to capture the relationship between the technical indicators and the levels of the KLSE index over time The exper iment shows that useful predictions can be made without the use of extensive market data or knowledge In fact a signi cant paper pro t can be achieved by purchasing indexed stocks in the respective proportions 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