Neural network ensemble for financial trend prediction

Author

Abdullah, Mohd Hark Lye b ; Ganapathy, V.

Author_Institution

Fac. of Eng., Multimedia Univ., Cyberjaya, Malaysia

Volume

3

fYear

2000

fDate

2000

Firstpage

157

Abstract

Presents a study of an artificial neural network that works collectively as an ensemble for the task of predicting the future trends of a financial time series from a classification approach. The Kuala Lumpur Stock Exchange composite index is chosen as a case study. A multilayer perceptron neural network is trained by backpropagation algorithms to capture the relationship between the future trends and the past values of the index. The performance of the neural network ensemble is compared to the single neural network. A confidence measure using agreement (variance) within the ensemble and with the ensemble output is studied

Keywords

backpropagation; financial data processing; forecasting theory; multilayer perceptrons; pattern classification; stock markets; time series; Kuala Lumpur Stock Exchange composite index; backpropagation algorithm; case study; classification; confidence measure; ensemble agreement; financial time series; financial trend prediction; multilayer perceptron training; neural network ensemble; past values; performance; variance; Artificial neural networks; Backpropagation algorithms; Economic forecasting; Finance; Information technology; Multi-layer neural network; Multilayer perceptrons; Neural networks; Stock markets; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2000. Proceedings

Conference_Location

Kuala Lumpur

Print_ISBN

0-7803-6355-8

Type

conf

DOI

10.1109/TENCON.2000.892242

Filename

892242