Socially embedded multi agent based simulation of financial market

This paper proposed a new approach that integrated an artificial market simulation and text-mining with real information. In this approach, economic trends were extracted from text data circulating in the real world. Then, the trends were inputted into the market simulation. The simulation could support users' action to the actual market. This approach was used for the decision of exchange rate policy and suggested that the operation by intervention was effective for the stabilization of the yen-dollar rate in 1995. Our simulation revealed that the action rule proposed by our system could reduce over 70% of rate fluctuation. This approach can offer a useful social simulation as a tool to users.