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Dynamic effects of increasing heterogeneity in financial markets

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  • Naimzada, Ahmad K.
  • Ricchiuti, Giorgio

Abstract

Despite canonical behavioural financial market models [Day R, Huang W. Bulls, bears and market sheep. J Econ Behav Org 1990;14:299–329], that use different types of agents (i.e., fundamentalist vs. chartists), we develop a model in which the source of instability is the interaction of groups that are homogeneous in the strategy they use, but have heterogeneous beliefs about the fundamental value of the asset. Specifically, heterogeneity arises among two groups of fundamentalists that follow gurus. We show that an increasing distance between beliefs (the degree of heterogeneity), leads first (i) to a pitchfork bifurcation to arise secondly (ii) it generates, together with a larger reaction to misalignment of both market maker and agents, the appearance of a periodic, or even, chaotic, price fluctuation; (iii) finally a homoclinic bifurcation [Dieci R, Bischi GI, Gardini L. From bi-stability to chaotic oscillations in a macroeconomic model. Chaos, Solitons & Fractals 2001;12:805–22] transforms a two piece chaotic set into a one piece chaotic set that generates bull and bear markets.

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  • Naimzada, Ahmad K. & Ricchiuti, Giorgio, 2009. "Dynamic effects of increasing heterogeneity in financial markets," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1764-1772.
    • Handle: RePEc:eee:chsofr:v:41:y:2009:i:4:p:1764-1772
    DOI: 10.1016/j.chaos.2008.07.022
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    Cited by:

    1. Ahmad K. Naimzada & Giorgio Ricchiuti, 2014. "Complexity with Heterogeneous Fundamentalists and a Multiplicative Price Mechanism," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 43(3), pages 233-247, November.
    2. Naimzada, Ahmad & Pireddu, Marina, 2015. "Real and financial interacting markets: A behavioral macro-model," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 111-131.
    3. Naimzada, Ahmad & Pireddu, Marina, 2020. "Eductive stability may not imply evolutionary stability in the presence of information costs," Economics Letters, Elsevier, vol. 186(C).
    4. Cavalli, Fausto & Naimzada, Ahmad & Pecora, Nicolò & Pireddu, Marina, 2018. "Market sentiment and heterogeneous fundamentalists in an evolutive financial market mode," MPRA Paper 90289, University Library of Munich, Germany.
    5. Giovanni Campisi & Silvia Muzzioli & Fabio Tramontana, 2021. "Uncertainty about fundamental, pessimistic and overconfident traders: a piecewise-linear maps approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 707-726, December.
    6. Michele Gori & Giorgio Ricchiuti, 2018. "A dynamic exchange rate model with heterogeneous agents," Journal of Evolutionary Economics, Springer, vol. 28(2), pages 399-415, April.
    7. F. Cavalli & A. Naimzada & M. Pireddu, 2017. "An evolutive financial market model with animal spirits: imitation and endogenous beliefs," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1007-1040, November.
    8. Carraro, Alessandro & Ricchiuti, Giorgio, 2015. "Heterogeneous fundamentalists and market maker inventories," Chaos, Solitons & Fractals, Elsevier, vol. 79(C), pages 73-82.
    9. Kaltwasser, Pablo Rovira, 2010. "Uncertainty about fundamentals and herding behavior in the FOREX market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1215-1222.
    10. Brianzoni, Serena & Campisi, Giovanni, 2020. "Dynamical analysis of a financial market with fundamentalists, chartists, and imitators," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    11. Giovanni Campisi & Silvia Muzzioli, 2020. "Fundamentalists heterogeneity and the role of the sentiment indicator," Department of Economics 0167, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    12. Ahmad Naimzada & Marina Pireddu, 2014. "Real and financial interacting oscillators: a behavioral macro-model with animal spirits," Working Papers 268, University of Milano-Bicocca, Department of Economics, revised Feb 2014.

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    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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