IDEAS home Printed from https://ideas.repec.org/a/cys/ecocyb/v50y2017i1p281-302.html
   My bibliography  Save this article

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

Author

Listed:
  • Narges TALEBIMOTLAGH
  • Farzad HASHEMZADEH
  • Amir RIKHTEHGAR GHIASI
  • Sehraneh GHAEMI

    (Control Engineering Department Faculty of Electrical and Computer Engineering: University of Tabriz, Tabriz, Iran)

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.

Suggested Citation

  • Narges TALEBIMOTLAGH & Farzad HASHEMZADEH & Amir RIKHTEHGAR GHIASI & Sehraneh GHAEMI, 2017. "A Novel Method of Modeling Dynamic Evolutionary Game with Rational Agents for Market Forecasting," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 281-302.
    • Handle: RePEc:cys:ecocyb:v:50:y:2017:i:1:p:281-302
    as

    Download full text from publisher

    File URL: ftp://www.eadr.ro/RePEc/cys/ecocyb_pdf/ecocyb1_2017p281-302.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agnés d’Artigues & Thierry Vignolo, 2004. "An evolutionary theory of the convergence towards low inflation rates," Journal of Evolutionary Economics, Springer, vol. 15(1), pages 51-64, January.
    2. Schipper, Burkhard C., 2008. "On An Evolutionary Foundation Of Neuroeconomics," Economics and Philosophy, Cambridge University Press, vol. 24(03), pages 495-513, November.
    3. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    4. Matthias Hanauske & Jennifer Kunz & Steffen Bernius & Wolfgang Konig, 2009. "Doves and hawks in economics revisited. An evolutionary quantum game theory-based analysis of financial crises," Papers 0904.2113, arXiv.org.
    5. Ricardo Azevedo Araujo & Helmar Nunes Moreira, 2014. "Lyapunov stability in an evolutionary game theory model of the labour market," Economia, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics], vol. 15(1), pages 41-53.
    6. Araujo, Ricardo Azevedo & de Souza, Nathalia Almeida, 2010. "An evolutionary game theory approach to the dynamics of the labour market: A formal and informal perspective," Structural Change and Economic Dynamics, Elsevier, vol. 21(2), pages 101-110, May.
    7. Schipper, Burkhard C., 2008. "On An Evolutionary Foundation Of Neuroeconomics," Economics and Philosophy, Cambridge University Press, vol. 24(3), pages 495-513, November.
    8. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    9. Mauricio G. Villena & Marcelo J. Villena, 2004. "Evolutionary Game Theory and Thorstein Veblen’s Evolutionary Economics: Is EGT Veblenian?," Journal of Economic Issues, Taylor & Francis Journals, vol. 38(3), pages 585-610, September.
    10. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    11. Elvio Accinelli Gamba & Edgar J. Sánchez Carrera, 2010. "The Evolutionary Processes for the Populations of Firms and Workers," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 39-68, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    2. Zhu, Mei & Wang, Duo & Guo, Maozheng, 2011. "Stochastic equilibria of an asset pricing model with heterogeneous beliefs and random dividends," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 131-147, January.
    3. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    4. Wigniolle, B., 2014. "Optimism, pessimism and financial bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 188-208.
    5. Anufriev, M. & Dindo, P.D.E., 2007. "Wealth Selection in a Financial Market with Heterogeneous Agents," CeNDEF Working Papers 07-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    6. Franco Ruzzenenti & Andreas A. Papandreou, 2015. "Effects of fossil fuel prices on the transition to a low-carbon economy," Working papers wpaper89, Financialisation, Economy, Society & Sustainable Development (FESSUD) Project.
    7. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    8. Anufriev, Mikhail & Dindo, Pietro, 2010. "Wealth-driven selection in a financial market with heterogeneous agents," Journal of Economic Behavior & Organization, Elsevier, vol. 73(3), pages 327-358, March.
    9. Barbara Dluhosch, 2011. "European Economics at a Crossroads, by J. Barkley Rosser, Jr., Richard P. F. Holt, and David Colander," Journal of Regional Science, Wiley Blackwell, vol. 51(3), pages 629-631, August.
    10. Hommes, Cars & Vroegop, Joris, 2019. "Contagion between asset markets: A two market heterogeneous agents model with destabilising spillover effects," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 314-333.
    11. Qi Nan Zhai, 2015. "Asset Pricing Under Ambiguity and Heterogeneity," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2015, January-A.
    12. Bekiros, Stelios & Jlassi, Mouna & Lucey, Brian & Naoui, Kamel & Uddin, Gazi Salah, 2017. "Herding behavior, market sentiment and volatility: Will the bubble resume?," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 107-131.
    13. Cars Hommes & Florian Wagener, 2008. "Complex Evolutionary Systems in Behavioral Finance," Tinbergen Institute Discussion Papers 08-054/1, Tinbergen Institute.
    14. Mikhail Anufriev & Pietro Dindo, 2006. "Equilibrium Return and Agents’ Survival in a Multiperiod Asset Market: Analytic Support of a Simulation Model," Lecture Notes in Economics and Mathematical Systems, in: Charlotte Bruun (ed.), Advances in Artificial Economics, chapter 19, pages 269-282, Springer.
    15. Hommes, Cars, 2018. "Behavioral & experimental macroeconomics and policy analysis: a complex systems approach," Working Paper Series 2201, European Central Bank.
    16. Panchenko, Valentyn & Gerasymchuk, Sergiy & Pavlov, Oleg V., 2013. "Asset price dynamics with heterogeneous beliefs and local network interactions," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2623-2642.
    17. Rosser Jr., J. Barkley, 2010. "Is a transdisciplinary perspective on economic complexity possible?," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 3-11, July.
    18. Dieci, Roberto & Westerhoff, Frank, 2010. "Heterogeneous speculators, endogenous fluctuations and interacting markets: A model of stock prices and exchange rates," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 743-764, April.
    19. Kai Li, 2014. "Asset Price Dynamics with Heterogeneous Beliefs and Time Delays," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2014, January-A.
    20. Feldman, Todd, 2010. "Portfolio manager behavior and global financial crises," Journal of Economic Behavior & Organization, Elsevier, vol. 75(2), pages 192-202, August.

    More about this item

    Keywords

    Evolutionary Game Theory · Rational Agent · Evolutionary Stable State · Recurrent Neural Network · Two Step Ahead Prediction · Reinforcement Learning · Gold Market.;

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cys:ecocyb:v:50:y:2017:i:1:p:281-302. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/feasero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.