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Dynamical Agents' Strategies and the Fractal Market Hypothesis

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  • Lukáš Vácha
  • Miloslav S. Vošvrda

Abstract

The efficient market hypothesis (EMH) fails as a valid model of financial markets. The fractal market hypothesis (FMH) is a more general alternative way to the EMH. The FMH is formed on the following parameter space: agents' investment horizons. A financial market is more stable when a fractal character in the structures of agent's investment horizons is adopted. For computer simulations, the classical model is modified. This adjusted model shows that various frequency distributions on agents' investment horizons lead to different returns behaviour. The FMH focuses on matching of demand and supply of agents' investment horizons in the financial market. The FMH asserts that investors have different information based on temporal attributes. Since all investors in the market have different time investment horizons, the market remains stable. Our simulations of probability distributions of agents' investment horizons demonstrate that many investment horizons guarantee stability on the financial market.

Suggested Citation

  • Lukáš Vácha & Miloslav S. Vošvrda, 2005. "Dynamical Agents' Strategies and the Fractal Market Hypothesis," Prague Economic Papers, Prague University of Economics and Business, vol. 2005(2), pages 163-170.
    • Handle: RePEc:prg:jnlpep:v:2005:y:2005:i:2:id:260:p:163-170
    DOI: 10.18267/j.pep.260
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    Cited by:

    1. Lukáš Vácha & Miloslav Vošvrda, 2006. "Wavelet Applications to Heterogeneous Agents Model," Working Papers IES 2006/21, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2006.
    2. Sobolev, Daphne, 2017. "The effect of price volatility on judgmental forecasts: The correlated response model," International Journal of Forecasting, Elsevier, vol. 33(3), pages 605-617.
    3. Kostanjcar, Zvonko & Jeren, Branko & Juretic, Zeljan, 2012. "Impact of uncertainty in expected return estimation on stock price volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5563-5571.
    4. Alexander V Laktyunkin & Alexander A Potapov, 2020. "Impact of COVID-19 on the Financial Crisis - Calculation of Fractal Parameters," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 30(5), pages 23768-23772, October.
    5. Miloslav Vošvrda & Lukáš Vácha, 2007. "Heterogeneous Agents Model with the Worst Out Algorithm," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 1(1), pages 54-66, March.
    6. Peter Albrecht & Svatopluk Kapounek & Zuzana Kučerová, 2023. "Economic policy uncertainty and stock markets’ co‐movements," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3471-3487, October.
    7. Lukáš Vácha & Miloslav Vošvrda, 2007. "Wavelet Decomposition of the Financial Market," Prague Economic Papers, Prague University of Economics and Business, vol. 2007(1), pages 38-54.
    8. Jozef Barunik & Lukas Vacha & Miloslav Vosvrda, 2009. "Smart predictors in the heterogeneous agent model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 4(2), pages 163-172, November.

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    More about this item

    Keywords

    efficient market hypothesis; fractal market hypothesis; agents' investment horizons; agents' trading strategies;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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