Equity Forecasting: A CASE Study on the Klse Index

摘要

This paper presents the research of neural networks as applied in equity forecast-ing in an emerging market such s the Kuala Lumpur Stock Exchange(KLSE). Backproapagation neural networks are used to capture the relationship between the technical inidicators and the levels of the KLSE index over time. The experiment shows that useful predictions can be made without the use of extensive market data or knowledge. On fact, a significant paper profit can be acheived by purchesing indexed stocks in the repertive propprtions. The paper, however, also discussed the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems.