Simultaneous optimization of artificial neural networks for financial forecasting

Kyoung Jae Kim, Hyunchul Ahn

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.

Original languageEnglish
Pages (from-to)887-898
Number of pages12
JournalApplied Intelligence
Volume36
Issue number4
DOIs
StatePublished - Jun 2012

Keywords

  • Artificial neural networks
  • Genetic algorithms
  • Simultaneous optimization
  • Stock market prediction

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