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A Dynamical Model for Financial Market: Among Common Market Strategies Who and How Moves the Price to Fluctuate, Inflate, and Burst?

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  • Annalisa Fabretti

    (Department of Economics and Finance, University of Rome Tor Vergata, 00133 Rome, Italy)

Abstract

A piecewise linear dynamical model is proposed for a stock price. The model considers the price is driven by three rather standard demand components: chartist, fundamental and market makers. The chartist demand component is related to the study of differences between moving averages. This generates a high order system characterized by a piecewise linear map not trivial to study. The model has been studied analytically in its fixed points and dynamics and then numerically. Results are in line with the related literature: the fundamental demand component helps the stability of the system and keeps prices bounded; market makers satisfy their role of restoring stability, while the chartist demand component produces irregularity and chaos. However, in some cases, the chartist demand component assumes the role to compensate the fundamental demand component, felt in an autogenerated loop, and pushes the dynamics to equilibrium. This fact suggests that the instability must not be searched into the nature of the different investment styles rather in the relative proportion of the contribution of market actors.

Suggested Citation

  • Annalisa Fabretti, 2022. "A Dynamical Model for Financial Market: Among Common Market Strategies Who and How Moves the Price to Fluctuate, Inflate, and Burst?," Mathematics, MDPI, vol. 10(5), pages 1-17, February.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:679-:d:755921
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    References listed on IDEAS

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    1. Tramontana, Fabio & Westerhoff, Frank & Gardini, Laura, 2010. "On the complicated price dynamics of a simple one-dimensional discontinuous financial market model with heterogeneous interacting traders," Journal of Economic Behavior & Organization, Elsevier, vol. 74(3), pages 187-205, June.
    2. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    3. Beja, Avraham & Goldman, M Barry, 1980. "On the Dynamic Behavior of Prices in Disequilibrium," Journal of Finance, American Finance Association, vol. 35(2), pages 235-248, May.
    4. Cars Hommes & Helena Nusse, 1991. "“Period three to period two” bifurcation for piecewise linear models," Journal of Economics, Springer, vol. 54(2), pages 157-169, June.
    5. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    6. Franke, Reiner & Westerhoff, Frank, 2012. "Structural stochastic volatility in asset pricing dynamics: Estimation and model contest," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1193-1211.
    7. J.A. Hołyst & M. Żebrowska & K. Urbanowicz, 2001. "Observations of deterministic chaos in financial time series by recurrence plots, can one control chaotic economy?," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 20(4), pages 531-535, April.
    8. Claire G. Gilmore, 1996. "Detecting Linear and Nonlinear Dependence in Stock Returns: New Methods Derived from Chaos Theory," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 23(9-10), pages 1357-1377, December.
    9. Carl Chiarella, 1992. "The Dynamics of Speculative Behaviour," Working Paper Series 13, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
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    Cited by:

    1. Arsen Palestini, 2022. "Preface to the Special Issue “Mathematical Modeling with Differential Equations in Physics, Chemistry, Biology, and Economics”," Mathematics, MDPI, vol. 10(10), pages 1-2, May.

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