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

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By Izumi K.

During this examine, we proposed a brand new method of foreign currency echange industry experiences, a synthetic marketplace procedure. the bogus industry strategy built-in fieldwork reviews and multiagent types which will clarify the micro and macro relation in markets.

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Extra info for An Artificial Market Model of a Foreign Exchange Market

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Thus, the variance is inversely proportional to the degree of confidence of each agent’s forecast. It must be noted that Ejt [∆St] and Varjt [∆St] are different for both each agent j and each week t because the weights are different. 3 Step 3: Strategy Making Each agent has dollar assets and yen assets. Each agent decides, on the bases of his own prediction, his trading strategy (order to buy or sell dollar) . He maximizes his utility function of his expected return of the next week. The strategy making process of the proposed model is common to the conventional portfolio balance model in econometrics.

1), because they are data of the events in the real world. 1), because they are calculated using the rate which the model made in the simulation. 2 Step 2: Prediction After perception, using above data, each agent predicts future change of the rate. j Each agent has his own weights of the 17 data. wi,t is defined as a weight of each datum i in each agent j’s prediction of the future rate at week t. 1, 0}. value of wi,t With his own weights, each agent j predicts change of logarithms of the rates ∆St = St − St−1 , where St denotes a logarithm of the exchange rate at 51 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Data (xi,t ) Raw Data Economic activities [U][J] GDP,NAPM index etc.

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.

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