AN ICA design of intraday stock prediction models with automatic variable selection

Pik Yin Mok, K. P. Lam, H. S. Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Independent component analysis (ICA) provides a mechanism of decomposing non-Gaussian data signals into statistically independent components. In this paper, ICA is used to extract the underlying news factors from intraday stock data. A prediction algorithm is developed to improve stock index predictions using such extracted "news". Both linear regression model and nonlinear artificial neural network model are proposed to predict stock indexes of Open, Close, High and Low using the ICA extracted "news". These models are compared with models using only raw intraday data as "news". It is demonstrated that ICA helps in extracting market underlying affecting "news", and thus improves the stock prediction accuracy. It shows that the proposed ICA prediction algorithm is a simple to use and versatile algorithm that automatically extracts the most relevant news for different stock index predictions.
Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages2135-2140
Number of pages6
Volume3
DOIs
Publication statusPublished - 1 Dec 2004
Externally publishedYes
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 25 Jul 200429 Jul 2004

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country/TerritoryHungary
CityBudapest
Period25/07/0429/07/04

Keywords

  • Independent component analysis
  • Linear regression
  • Neural Network

ASJC Scopus subject areas

  • Software

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