The Dynamics of Quote Prices in an Artificial Financial Market with Learning Effects

In this paper we study the evolution of bid and ask prices in an electronic financial market populated by portfolio traders who optimally choose their allocation strategy on the basis of their views about market conditions. We design an order book market system where agents enter the market sequentially and trade to adjust their portfolio according to their optimal target allocations. They apply a copula function to generate the joint distribution of returns to be used to determine the optimal portfolio allocations. We create asynchronous updating assuming that different groups of agents entered the market at different moments in time. We simplify the optimization problem assuming that investors are myopic: at the beginning of the investment horizon they choose their portfolios as if there will be no further trading.