TY - JOUR
T1 - Using the XCS classifier system for portfolio allocation of MSCI index component stocks
AU - Tsai, Wen Chih
AU - Chen, An-Pin
PY - 2011/1/1
Y1 - 2011/1/1
N2 - 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.
AB - 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.
KW - Financial forecasting
KW - MSCI Taiwan index component stock
KW - Reinforcement learning
KW - XCS
UR - http://www.scopus.com/inward/record.url?scp=77956612541&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2010.06.031
DO - 10.1016/j.eswa.2010.06.031
M3 - Article
AN - SCOPUS:77956612541
SN - 0957-4174
VL - 38
SP - 151
EP - 154
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 1
ER -