Stock Exchange of Thailand Index Prediction Using Back Propagation Neural Networks

Abstract

In this paper, we investigate predicting the Stock Exchange of Thailand Index movement. Currently, there are two stock markets in Thailand; the Stock Exchange of Thailand (SET) and the Market for Alternative Investment (MAI). This paper focuses on the movement of the Stock Exchange of Thailand Index (SET Index). The back propagation neural network (BPNN) technology was employed in forecasting the SET index. An experiment was conducted by using data of 124 trading days from 2 July 2004 to 30 December 2004. The data were divided into two groups: 53 days for BPNN training and 71 days for testing. The experimental results show that the BPNN successfully predicts the SET Index with less than 2% error. The BPNN also achieves a lower prediction error when compared with the Adaptive Evolution Strategy, but a higher prediction error when compared with the (1+1) Evolution Strategy.