Document Type

Conference Proceeding

Publisher

IEEE Computer Society Press

Faculty

Computing, Health and Science

School

Computer and Information Science

RAS ID

6098

Comments

This article was originally published as: Chaigusin, S. , Chirathamjaree, C. , & Clayden, J. M. (2008). The Use of Neural Networks in the Prediction of the Stock Exchange of Thailand (SET) Index. Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation. (pp. 670-673). Vienna, Austria. IEEE Computer Society Press. Original article available here

© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract

Prediction of stock prices is an issue of interest to financial markets. Many prediction techniques have been reported in stock forecasting. Neural networks are viewed as one of the more suitable techniques. In this study, an experiment on the forecasting of the Stock Exchange of Thailand (SET) was conducted by using feedforward backpropagation neural networks. In the experiment, many combinations of parameters were investigated to identify the right set of parameters for the neural network models in the forecasting of SET. Several global and local factors influencing the Thai stock market were used in developing the models, including the Dow Jones index, Nikkei index, Hang Seng index, gold prices, Minimum Loan Rate (MLR), and the exchange rates of the Thai Baht and the US dollar. Two years’ historical data were used to train and test the models. Three suitable neural network models identified by this research are a three layer, a four layer and a five layer neural network. The Mean Absolute Percentage Error (MAPE) of the predictions of each models were 1.26594, 1.14719 and 1.14578 respectively.

DOI

10.1109/CIMCA.2008.83

Access Rights

free_to_read

Article Location

 
COinS
 

Link to publisher version (DOI)

10.1109/CIMCA.2008.83