Intelligent technologies for investing: a review of engineering literature
Intelligent Decision Technologies
Integration of artificial market simulation and text mining for market analysis
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
The impact of information sharing mechanism to geographic market formation
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
GA-based solutions comparison for warehouse storage optimization
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Analysis of learning types in an artificial market
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
Analysis of emission right prices in greenhouse gas emission trading via agent-based model
Multiagent and Grid Systems
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In this study, we propose an artificial market approach, which is a new agent-based approach to foreign exchange market studies. Using this approach, emergent phenomena of markets such as the peaked and fat-tailed distribution of rate changes were explained. First, we collected the field data through interviews and questionnaires with dealers and found that the features of dealer interaction in learning were similar to the features of genetic operations in biology. Second, we constructed an artificial market model using a genetic algorithm. Our model was a multiagent system with agents having internal representations about market situations. Finally, we carried out computer simulations with our model using the actual data series of economic fundamentals and political news. We then identified three emergent phenomena of the market. As a result, we concluded that these emergent phenomena could be explained by the phase transition of forecast variety, which is due to the interaction of agent forecasts and the demand-supply balance. In addition, the results of simulation were compared with the field data. The field data supported the simulation results. This approach therefore integrates fieldwork and a multiagent model, and provides a quantitative explanation of micro-macro relations in markets