A Novel Method of Modeling Dynamic Evolutionary Game with Rational Agents for Market Forecasting

Narges Talebimotlagh (), Farzad Hashemzadeh (), Amir RIKHTEHGAR Ghiasi () and Sehraneh Ghaemi ()
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Sehraneh Ghaemi: Control Engineering Department Faculty of Electrical and Computer Engineering: University of Tabriz, Tabriz, Iran

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2017, vol. 51, issue 1, 281-302

Abstract: Gold price modeling and prediction is a difficult problem and drastic changes of the price causes nonlinear dynamic that makes the price prediction one of the most challenging tasks for economists. Since gold market always has been interesting for traders, many of traders with various beliefs were highly active in gold market. The competition among two agents of traders, namely trend followers and rational agents, to gain the highest profit in gold market is formulated as a dynamic evolutionary game, where, the evolutionary equilibrium is considered to be the solution to this game. Furthermore, genetic algorithm is being used to find the unknown parameters of the model, so that we could maximize the fitness of the proposed multi agent model and the gold market daily price data. Besides the evolutionary game dynamic, we proposed a new method for modeling rational expectations using recurrent neural network. The evolutionarily stable strategies is proven despite the prediction error of the expectation. The empirical results show the high efficiency of the proposed method which could forecast future gold price precisely.

Keywords: Evolutionary; Game; Theory; ·; Rational; Agent; ·; Evolutionary; Stable; State; ·; Recurrent; Neural; Network; ·; Two; Step; Ahead; Prediction; ·; Reinforcement; Learning; ·; Gold; Market. (search for similar items in EconPapers)
JEL-codes: C45 C53 C70 C73 (search for similar items in EconPapers)
Date: 2017
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