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Agent-Based Approach to Investors? Behavior and Asset Price Fluctuation in Financial Markets

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Abstract

In this paper, we use Agent-Based Approach to analyze how asset prices are affected by investors and investment systems that are based on Behavioral Finance. We build a virtual financial market that contains two types of investors: fundamentalists and non-fundamentalists. As a result of intensive experiments in the market, we find that (1) the traded price agrees with the fundamental value and the fundamentalists survive according to the principle of natural selection in the case that the market contains the same number of fundamentalists and trend predictors (investors who make trend prediction), (2) the traded price largely deviates from the fundamental value and the non-fundamentalists frequently obtain excess returns and therefore the fundamentalists are eliminated according to the principle of natural selection in the case that the proportion of trend predictors is extremely high or in the case that the investment ratio of the risk asset is restricted, and (3) the traded price largely deviates from the fundamental value in the case that the non-fundamentalists estimate the losses excessively, as pointed in Prospect Theory. These results indicate that the non-fundamentalists affect the traded prices and obtain excess returns also in real markets.

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  • Hiroshi Takahashi & Takao Terano, 2003. "Agent-Based Approach to Investors? Behavior and Asset Price Fluctuation in Financial Markets," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(3), pages 1-3.
  • Handle: RePEc:jas:jasssj:2002-35-3
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    2. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Gurjeet Dhesi & Muhammad Bilal Shakeel & Ling Xiao, 2015. "Modified Brownian Motion Approach to Modelling Returns Distribution," Papers 1507.02203, arXiv.org.
    4. Mario A Bertella & Felipe R Pires & Ling Feng & Harry Eugene Stanley, 2014. "Confidence and the Stock Market: An Agent-Based Approach," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
    5. Navarro-Barrientos, Jesús Emeterio & Cantero-Álvarez, Rubén & Matias Rodrigues, João F. & Schweitzer, Frank, 2008. "Investments in random environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2035-2046.
    6. Doris Neuberger & Roger Rissi, 2014. "Macroprudential Banking Regulation: Does One Size Fit All?," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 1(1), pages 5-28, May.
    7. Mario A Bertella & Felipe R Pires & Henio H A Rego & Jonathas N Silva & Irena Vodenska & H Eugene Stanley, 2017. "Confidence and self-attribution bias in an artificial stock market," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-20, February.
    8. Dimitrios Kourtidis & Željko Šević & Prodromos Chatzoglou, 2016. "Mood and stock returns: evidence from Greece," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 43(2), pages 242-258, May.
    9. Sylvain Mignot & Gabriele Tedeschi & Annick Vignes, 2012. "An Agent Based Model of Switching: The Case of Boulogne S/mer Fish Market," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-3.
    10. Gilbert Peffer & Barbara Llacay, 2007. "Higher-Order Simulations: Strategic Investment Under Model-Induced Price Patterns," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-6.
    11. Arvid Oskar Ivar Hoffmann & Wander Jager & J. H. Von Eije, 2007. "Social Simulation of Stock Markets: Taking It to the Next Level," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-7.
    12. Heba M. Ezzat, 2019. "Disposition effect and multi-asset market dynamics," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 11(2), pages 144-164, June.
    13. Jiahua Wang & Hongliang Zhu & Dongxin Li, 2018. "Price Dynamics in an Order-Driven Market with Bayesian Learning," Complexity, Hindawi, vol. 2018, pages 1-15, November.
    14. Shi-Woei Lin & Hui-Lung Huang, 2007. "Agent-Based Modeling To Investigate The Disposition Effect In Financial Markets," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(2), pages 145-163.
    15. Khrennikova, Polina, 2016. "Application of quantum master equation for long-term prognosis of asset-prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 253-263.
    16. Hokky Situngkir & Yohanes Surya, 2004. "Agent-based Model Construction In Financial Economic System," Finance 0405006, University Library of Munich, Germany.
    17. Bertella, Mario A. & Silva, Jonathas N. & Stanley, H. Eugene, 2020. "Loss aversion, overconfidence and their effects on a virtual stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).

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