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

By Izumi K.

During this examine, we proposed a brand new method of foreign currency marketplace reports, a synthetic industry method. the substitute marketplace strategy built-in fieldwork experiences and multiagent versions on the way to clarify the micro and macro relation in markets.

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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. In order to improve his prediction, each agent changes his own belief system with three operators: selection, crossover, and mutation (Fig.

10) JxS ≡ {j : ∆qtj∗ < 0 and Ejt [∆St ] ≤ x} . 11) (DDt (x∗ ) = SSt (x∗ )) . Buyers(Sellers) with higher(lower) order rates can execute their exchanges and coincide their holding quantities qtj with the optimal quantities qtj∗. 12) Step 5: Adaptation In the proposed model, different agents have different prediction methods j (combinations of the weights wi,t ). After the rate determination, each agent improves his prediction method using other agents’ prediction. The model uses genetic algorithms to describe the interaction between agents in learning.

Thus the results of interpretation, the data xi,t ’s, are the same for all agents. 1). Those values range discretely from −3 to +3. Plus values indicate that the data change causes dollar depreciation according to the traditional economic theories. Minus values indicate dollar appreciation. For an instance, a comment “Unemployment Rate of United States decreased largely” is coded as “Employment : −3”. And data “Last week, the yen/dollar rate decreased beyond expectation” is coded as “Change in the last week : +2”.

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