Phase transition in a foreign exchange market-analysis based on an artificial market approach

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.