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By Izumi K.

During this examine, we proposed a brand new method of foreign currency echange industry reviews, a man-made industry process. the unreal industry technique built-in fieldwork reviews and multiagent versions on the way to clarify the micro and macro relation in markets.

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If exchange rate models which are built up from the agent level have the above problems, why not build up a model from a lower level such as the belief system level? Let us consider beliefs about factors as building blocks of an exchange rate model. Beliefs about factors have several important features. First, they are replicators: they are imitated or transmitted by other agents with some degree of reproductive accuracy. Second, they are instructors: they organize each agent’s belief system about the foreign exchange market, and according to his own belief system each agent makes prediction and decides behavior.

They had more than two years of experience on the trading desk. 1 Interview Methods The interviewees were asked to explain the rate dynamics of the two years from January 1994 to November 1995, when the interview took place. Concretely, we asked each dealer to do the following things: 1. e. without referring to any material) the rate dynamics of these two years and also to talk both about how he forecasted the weekly yen-dollar rates and about how he recognized the market situations such as the rate trend.

Hence, the more precisely a chromosome predicts the rate, the higher its fitness value. 15) j wi,t xi,t − ∆St |. i=1 We use the usual single-point crossover and the mutation operator with uniform probability. , mutation rate, pmut). Genetic algorithm can be interpreted economically as follows: Each chromosome can be regarded as an agent’s belief system about the exchange rate. That is, it represents which data are regarded as the important causes of the rate change. It must be noted that the belief systems can differ among agents.

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An Artificial Market Model of a Foreign Exchange Market by Izumi K.


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