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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.

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Bibliographic Info

Article provided by University of Economics, Prague in its journal Prague Economic Papers.

Volume (Year): 2005 (2005)
Issue (Month): 2 ()
Pages: 163-170

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Handle: RePEc:prg:jnlpep:v:2005:y:2005:i:2:id:260:p:163-170

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Related research

Keywords: fractal market hypothesis; efficient market hypothesis; agents' trading strategies; agents' investment horizons;

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References

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  1. D. Sornette & W. -X. Zhou, 2003. "Predictability of large future changes in major financial indices," Papers cond-mat/0304601, arXiv.org, revised Aug 2004.
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  5. William A. Brock & Cars H. Hommes, 1997. "A Rational Route to Randomness," Econometrica, Econometric Society, vol. 65(5), pages 1059-1096, September.
  6. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
  7. Carl Chiarella & Xue-Zhong He, 2000. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model with a Market Maker," Research Paper Series 35, Quantitative Finance Research Centre, University of Technology, Sydney.
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Cited by:
  1. 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.
  2. Lukáš Vácha & Miloslav Vošvrda, 2007. "Wavelet Decomposition of the Financial Market," Prague Economic Papers, University of Economics, Prague, vol. 2007(1), pages 38-54.
  3. 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.
  4. Jozef Barunik & Lukas Vacha & Miloslav Vosvrda, 2009. "Smart predictors in the heterogeneous agent model," Journal of Economic Interaction and Coordination, Springer, vol. 4(2), pages 163-172, November.
  5. Lukáš Vácha & Miloslav Vošvrda, 2005. "Heterogeneous Agents Model with the Worst Out Algorithm," Working Papers IES 91, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised 2005.

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