TY - GEN
T1 - Using learning classifier system for making investment strategies based on institutional analysis
AU - Lin, Ju Yin
AU - Cheng, Chi Pin
AU - Tsai, Wen Chih
AU - Chen, An-Pin
PY - 2004/2
Y1 - 2004/2
N2 - The artificial intelligence can dynamically learn and adapt to the change of environments for maximizing the desired goals. This paper conducts simulation experiment to evolve learning classifier system (LCS) for short-term stock forecast decision. Since stock price trend is influenced by unknown and unpredictable surroundings, using LCS to model the fluctuations on financial market allows for the capability to discover the patterns of future trends. Furthermore, the institutional investment is the main consideration of this research by implementing LCS for making strategies. More specifically, LCS is capable of evolving from generation to generation, and in this way can provide the highest profit for future decision-making. In simulation work using real financial data, it is found that LCS produces great profits, and is quite practical for investors.
AB - The artificial intelligence can dynamically learn and adapt to the change of environments for maximizing the desired goals. This paper conducts simulation experiment to evolve learning classifier system (LCS) for short-term stock forecast decision. Since stock price trend is influenced by unknown and unpredictable surroundings, using LCS to model the fluctuations on financial market allows for the capability to discover the patterns of future trends. Furthermore, the institutional investment is the main consideration of this research by implementing LCS for making strategies. More specifically, LCS is capable of evolving from generation to generation, and in this way can provide the highest profit for future decision-making. In simulation work using real financial data, it is found that LCS produces great profits, and is quite practical for investors.
KW - Institutional investment
KW - Learning classifier system
KW - Stock market
UR - http://www.scopus.com/inward/record.url?scp=11144344213&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:11144344213
SN - 088986375X
SN - 9780889863750
T3 - Proceedings of the IASTED International Conference. Applied Informatics
SP - 765
EP - 769
BT - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics)
A2 - Hamza, M.H.
T2 - Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (as part of the 22nd IASTED International Multi-Conference on Applied Informatics
Y2 - 16 February 2004 through 18 February 2004
ER -