IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1711.08282.html
   My bibliography  Save this paper

Asymmetric return rates and wealth distribution influenced by the introduction of technical analysis into a behavioral agent based model

Author

Listed:
  • F. M. Stefan
  • A. P. F. Atman

Abstract

Behavioral Finance has become a challenge to the scientific community. Based on the assumption that behavioral aspects of investors may explain some features of the Stock Market, we propose an agent based model to study quantitatively this relationship. In order to approximate the simulated market to the complexity of real markets, we consider that the investors are connected among them through a small world network; each one has its own psychological profile (Imitation, Anti-Imitation, Random); two different strategies for decision making: one of them is based on the trust neighborhood of the investor and the other one considers a technical analysis, the momentum of the market index technique. We analyze the market index fluctuations, the wealth distribution of the investors according to their psychological profiles and the rate of return distribution. Moreover, we analyze the influence of changing the psychological profile of the hub of the network and report interesting results which show how and when anti-imitation becomes the most profitable strategy for investment. Besides this, an intriguing asymmetry of the return rate distribution is explained considering the behavioral aspect of the investors. This asymmetry is quite robust being observed even when a completely different algorithm to calculate the decision making of the investors was applied to it, a remarkable result which, up to our knowledge, has never been reported before.

Suggested Citation

  • F. M. Stefan & A. P. F. Atman, 2017. "Asymmetric return rates and wealth distribution influenced by the introduction of technical analysis into a behavioral agent based model," Papers 1711.08282, arXiv.org.
    • Handle: RePEc:arx:papers:1711.08282
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1711.08282
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    3. Drew Fudenberg & Jean Tirole, 1991. "Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061414, December.
    4. Stefan, F.M. & Atman, A.P.F., 2015. "Is there any connection between the network morphology and the fluctuations of the stock market index?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 630-641.
    5. Hou, Bonan & Yao, Yiping & Liao, Dongsheng, 2012. "Identifying all-around nodes for spreading dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4012-4017.
    6. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda & Dirk Helbing, 2013. "Are Random Trading Strategies More Successful than Technical Ones?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    7. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    8. A. E. Biondo & A. Pluchino & A. Rapisarda & D. Helbing, 2013. "Are random trading strategies more successful than technical ones?," Papers 1303.4351, arXiv.org, revised Jul 2013.
    9. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169.
    10. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    11. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    12. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    13. Hart, M & Jefferies, P & Johnson, N.F & Hui, P.M, 2001. "Crowd–anticrowd theory of the minority game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 298(3), pages 537-544.
    14. Lux, Thomas, 2012. "Estimation of an agent-based model of investor sentiment formation in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1284-1302.
    15. Thomas Lux, 2009. "Applications of Statistical Physics in Finance and Economics," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 9, Edward Elgar Publishing.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefan, F.M. & Atman, A.P.F., 2023. "Asymmetric rate of returns and wealth distribution influenced by the introduction of technical analysis into a behavioral agent-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. Stefan, F.M. & Atman, A.P.F., 2015. "Is there any connection between the network morphology and the fluctuations of the stock market index?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 630-641.
    3. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    4. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
    5. Po-Keng Cheng & Young Shin Kim, 2017. "Speculative bubbles and crashes： Fundamentalists and positive‐feedback trading," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1381370-138, January.
    6. Schinckus, C., 2013. "Between complexity of modelling and modelling of complexity: An essay on econophysics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3654-3665.
    7. Hommes, C.H., 2005. "Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006," CeNDEF Working Papers 05-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    8. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    9. Farmer, J. Doyne & Joshi, Shareen, 2002. "The price dynamics of common trading strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 149-171, October.
    10. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    11. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    12. Yeh, Chia-Hsuan & Yang, Chun-Yi, 2010. "Examining the effectiveness of price limits in an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 2089-2108, October.
    13. J. Doyne Farmer, 2002. "Market force, ecology and evolution," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(5), pages 895-953, November.
    14. Frijns, Bart & Lehnert, Thorsten & Zwinkels, Remco C.J., 2010. "Behavioral heterogeneity in the option market," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2273-2287, November.
    15. Marco Raberto & Silvano Cincotti & Sergio Focardi & Michele Marchesi, 2003. "Traders' Long-Run Wealth in an Artificial Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 255-272, October.
    16. J.-H. Steffi Yang & Satchell, S.E., 2002. "The Impact of Technical Analysis on Asset Price Dynamics," Cambridge Working Papers in Economics 0219, Faculty of Economics, University of Cambridge.
    17. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    18. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    19. Cars Hommes, 2006. "Interacting Agents in Finance," Tinbergen Institute Discussion Papers 06-029/1, Tinbergen Institute.
    20. Xue-Zhong He & Youwei Li, 2017. "The adaptiveness in stock markets: testing the stylized facts in the DAX 30," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1071-1094, November.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1711.08282. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.