Using the XCS classifier system for portfolio allocation of MSCI index component stocks

Wen Chih Tsai*, An-Pin Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In a recent study, Schulenburg and Ross (2001) proposed the LCS for short-term stock forecast. Studley and Bull (2007) proposed the extended classifier system (XCS) agent to model different traders by supplying different input information. Announcement made by Morgan Stanley Capital Investment (MSCI) regarding the additions, removals, and even the weights of the component stocks in its country indices every quarter generally would cause changes to the prices and/or trade volumes of the associated component stocks. This paper takes an XCS in artificial intelligence to dynamically learn and adapt to the changes to the component stocks in order to optimize portfolio allocation of the component stocks. Since these price trends of MSCI component stocks are influenced by unknown and unpredictable surroundings, using XCS to model the fluctuations on financial market allows for the capability to discover the patterns of future trends. This simulation works on the basis of the changes to 121 component stocks in the MSCI Taiwan index between 1998 and 2009 suggests the XCS can produce the great profit and optimize portfolio allocation.

Original languageEnglish
Pages (from-to)151-154
Number of pages4
JournalExpert Systems with Applications
Volume38
Issue number1
DOIs
StatePublished - 1 Jan 2011

Keywords

  • Financial forecasting
  • MSCI Taiwan index component stock
  • Reinforcement learning
  • XCS

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