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Market Mood, Adaptive Beliefs and Asset Price Dynamics

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Abstract

Empirical evidence has suggested that, facing different trading strategies and complicated decision, the proportions of agents relying on particular strategies may stay at constant level or vary over time. This paper presents a simple "dynamic market fraction" model of two groups of traders, fundamentalists and trend followers, under a market maker scenario. Market mood and evolutionary adaption are characterized by fixed and adaptive switching fraction among two groups, respectively. Using local stability and bifurcation analysis, as well as numerical simulation, the role played by the key parameters in the market behaviour is examined. Particular attention is payed to the impact of the market fraction, determined by the fixed proportions of confident fundamentalists and trend followers, and by the proportion of adaptively rational agents, who adopt different strategies over time depending on realized profits.

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File URL: http://www.qfrc.uts.edu.au/research/research_papers/rp162.pdf
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Paper provided by Quantitative Finance Research Centre, University of Technology, Sydney in its series Research Paper Series with number 162.

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Length: 24 pages
Date of creation: 01 Aug 2005
Publication status: Published as: Dieci, R., Foroni, I., Gardini, L. and He, X., 2006, "Market Mood, Adaptive Beliefs and Asset Price Dynamics", Chaos, Solitons and Fractals, 29(3), 520-534.
Handle: RePEc:uts:rpaper:162
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Cited by:

  1. Palczewski, Jan & Schenk-Hoppé, Klaus Reiner & Wang, Tongya, 2016. "Itchy feet vs cool heads: Flow of funds in an agent-based financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 63(C), pages 53-68.
  2. Xue-Zhong He & Youwei Li, 2015. "The Adaptiveness in Stock Markets: Testing the Stylized Facts in the Dax 30," Research Paper Series 364, Quantitative Finance Research Centre, University of Technology, Sydney.
  3. Michael Wegener & Frank Westerhoff, 2012. "Evolutionary competition between prediction rules and the emergence of business cycles within Metzler’s inventory model," Journal of Evolutionary Economics, Springer, vol. 22(2), pages 251-273, April.
  4. Christian R. Proano, 2009. "Heterogenous Behavioral Expectations, FX Fluctuations and Dynamic Stability in a Stylized Two-Country Macroeconomic Model," IMK Working Paper 03-2009, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
  5. He, Xue-Zhong & Li, Kai & Wei, Junjie & Zheng, Min, 2009. "Market stability switches in a continuous-time financial market with heterogeneous beliefs," Economic Modelling, Elsevier, vol. 26(6), pages 1432-1442, November.
  6. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
  7. He, Xue-Zhong & Zheng, Min, 2010. "Dynamics of moving average rules in a continuous-time financial market model," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 615-634, December.
  8. Fabio Dercole & Davide Radi, 2014. "Does the "uptick rule" stabilize the stock market? Insights from Adaptive Rational Equilibrium Dynamics," Papers 1405.7747, arXiv.org.
  9. Loretti I. Dobrescu & Mihaela Neamtu & Dumitru Opris, 2011. "A Discrete--Delay Dynamic Model for the Stock Market," Discussion Papers 2012-11, School of Economics, The University of New South Wales.
  10. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2011. "The dynamic behaviour of asset prices in disequilibrium: a survey," International Journal of Behavioural Accounting and Finance, Inderscience Enterprises Ltd, vol. 2(2), pages 101-139.
  11. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
  12. He, Xue-Zhong & Li, Kai & Wang, Chuncheng, 2016. "Volatility clustering: A nonlinear theoretical approach," Journal of Economic Behavior & Organization, Elsevier, vol. 130(C), pages 274-297.
  13. Xue-Zhong He & Youwei Li, 2008. "Heterogeneity, convergence, and autocorrelations," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 59-79.
  14. Huang, Weihong & Zheng, Huanhuan & Chia, Wai-Mun, 2010. "Financial crises and interacting heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1105-1122, June.
  15. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
  16. Christian R. Proaño, 2013. "Monetary Policy Rules And Macroeconomic Stabilization In Small Open Economies Under Behavioral Fx Trading: Insights From Numerical Simulations," Manchester School, University of Manchester, vol. 81(6), pages 992-1011, December.
  17. Christian Proaño, 2011. "Monetary Policy Rules and Macroeconomic Stabilization in Small Open Economies under Behavioral FX Trading: Insights from Numerical Simulations," Working Papers 1102, New School for Social Research, Department of Economics.
  18. Orlando Gomes, 2008. "Adaptive Learning and Complex Dynamics," Working Papers Series 1 ercwp2108, ISCTE-IUL, Business Research Unit (BRU-IUL).
  19. Proaño, Christian R., 2011. "Exchange rate determination, macroeconomic dynamics and stability under heterogeneous behavioral FX expectations," Journal of Economic Behavior & Organization, Elsevier, vol. 77(2), pages 177-188, February.
  20. Mauro Sodini, 2011. "Local and Global Dynamics in an Overlapping Generations Model with Endogenous Time Discounting," Computational Economics, Springer;Society for Computational Economics, vol. 38(3), pages 277-293, October.

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