The effects of neural networks training factors on stock price prediction errors

Ahmed F. Aleroud, Izzat M. Alsmadi, Ahmad I. Alaiad, Qasem A. Al-Radaideh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning approaches have been widely used for many financial applications. Neural network is an evolutionary computational approach which has proved its effectiveness in stock price prediction. This study evaluates the effects of changing two training factors on the performance of neural networks in short and long term stock price prediction error rates. The training time and the length of training period are boosted in a regular manner. Their effect on prediction error rate is evaluated. The results have shown that the length of training time has a significant effect on minimizing error rate in short term prediction, compared with its effect on long term prediction error rate. The results of training period factor indicated that increasing the training period has more effects on minimizing error rate of long term stock price prediction than its effect on the short term price prediction.

Original languageEnglish (US)
Title of host publicationProceedings of the IASTED International Conference on Communication, Internet, and Information Technology, CIIT 2012
Pages362-368
Number of pages7
DOIs
StatePublished - 2012
Externally publishedYes
EventInternational Conference on Communication, Internet, and Information Technology, CIIT 2012 - Baltimore, MD, United States
Duration: May 14 2012May 16 2012

Publication series

NameProceedings of the IASTED International Conference on Communication, Internet, and Information Technology, CIIT 2012

Conference

ConferenceInternational Conference on Communication, Internet, and Information Technology, CIIT 2012
CountryUnited States
CityBaltimore, MD
Period5/14/125/16/12

Keywords

  • Feed forward network
  • Neural networks
  • Stock price prediction
  • Training time

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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