Year-end Sale % OFF 
Abstract
In real market, the squares of stock price change rates have high autocorrelation, and the change rates show high peak and fat tail distribution. With the aim of analyzing the mechanism of the stock price change, we construct an artificial stock market composed of multiple agents whose investment strategies are represented by tree-shaped programs. The market is optimized by using a Genetic Programming so that the change of its stock price resembles that of "real" stock market statistically. In order to perform an efficient optimization and to analyze agents' behavior easily, we use ADG; Automatically Defined Groups previously proposed by authors. We show experimentally that complex changes such as real market appear in the proposed artificial market. Moreover we analyze the interaction of agents which causes realistic stock price changes.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computational Intelligence and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
