IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v419y2015icp630-641.html
   My bibliography  Save this article

Is there any connection between the network morphology and the fluctuations of the stock market index?

Author

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

Abstract

Models which consider behavioral aspects of the investors have attracted increasing interest in the Finance and Econophysics literature in the last years. Different behavioral profiles (imitation, anti-imitation, indifference) were proposed for the investors, which take their decision based on their trust network (neighborhood). Results from agent-based models have shown that most of the features observed in actual stock market indices can be replicated in simulations. Here, we present a deeper investigation of an agent based model considering different network morphologies (regular, random, small-world) for the investors’ trust network, in an attempt to answer the question raised in the title. We study the model by considering four scenarios for the investors and different initial conditions to analyze their influence in the stock market fluctuations. We have characterized the stationary limit for each scenario tested, focusing on the changes introduced when complex networks were used, and calculated the Hurst exponent in some cases. Simulations showed interesting results suggesting that the fluctuations of the stock market index are strongly affected by the network morphology, a remarkable result which we believe was never reported or predicted before.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:419:y:2015:i:c:p:630-641
    DOI: 10.1016/j.physa.2014.10.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114008644
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2014.10.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    2. Burgos, E. & Ceva, Horacio & Perazzo, R.P.J., 2004. "The evolutionary minority game with local coordination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(3), pages 635-644.
    3. Copic Jernej & Jackson Matthew O. & Kirman Alan, 2009. "Identifying Community Structures from Network Data via Maximum Likelihood Methods," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 9(1), pages 1-40, September.
    4. H. Lavička & F. Slanina, 2007. "Evolution of imitation networks in Minority Game model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 56(1), pages 53-63, March.
    5. Cajueiro, D.O., 2011. "Enforcing social behavior in an Ising model with complex neighborhoods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(9), pages 1695-1703.
    6. Drew Fudenberg & Jean Tirole, 1991. "Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061414, December.
    7. Wei, Yi-ming & Ying, Shang-jun & Fan, Ying & Wang, Bing-Hong, 2003. "The cellular automaton model of investment behavior in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 325(3), pages 507-516.
    8. Challet, Damien & Marsili, Matteo & Zhang, Yi-Cheng, 2013. "Minority Games: Interacting agents in financial markets," OUP Catalogue, Oxford University Press, number 9780199686698, Decembrie.
    9. 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.
    10. Johnson, Neil F. & Lamper, David & Jefferies, Paul & Hart, Michael L. & Howison, Sam, 2001. "Application of multi-agent games to the prediction of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 222-227.
    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. 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.
    13. G. Tedeschi & G. Iori & M. Gallegati, 2009. "The role of communication and imitation in limit order markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 489-497, October.
    14. 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.
    15. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169.
    16. B. A. Mello & V. M.C.S. Souza & D. O. Cajueiro & R. F.S. Andrade, 2010. "Network evolution based on minority game with herding behavior," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 76(1), pages 147-156, July.
    17. Lee, Kuen & Hui, P.M. & Johnson, N.F., 2003. "The minority game with different payoff functions: crowd–anticrowd theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 321(1), pages 309-317.
    18. Neil F. Johnson & David Lamper & Paul Jefferies & Michael L. Hart & Sam Howison, 2001. "Application of multi-agent games to the prediction of financial time-series," OFRC Working Papers Series 2001mf04, Oxford Financial Research Centre.
    19. Blake LeBaron & Ryuichi Yamamoto, 2008. "The Impact of Imitation on Long Memory in an Order-Driven Market," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 504-517.
    20. Johnson, Neil F. & Jefferies, Paul & Hui, Pak Ming, 2003. "Financial Market Complexity," OUP Catalogue, Oxford University Press, number 9780198526650, Decembrie.
    21. N. F. Johnson & D. Lamper & P. Jefferies & M. L. Hart & S. Howison, 2001. "Application of multi-agent games to the prediction of financial time-series," Papers cond-mat/0105303, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ducha, F.A. & Atman, A.P.F. & Bosco de Magalhães, A.R., 2021. "Information flux in complex networks: Path to stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    2. 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.
    3. 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).
    4. Fonseca, Carla L.G. & de Resende, Charlene C. & Fernandes, Danilo H.C. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2021. "Is the choice of the candlestick dimension relevant in econophysics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).

    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. 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.
    2. 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.
    3. 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).
    4. Xin-Jie Zhang & Yong Tang & Jason Xiong & Wei-Jia Wang & Yi-Cheng Zhang, 2018. "Dynamics of Cooperation in Minority Games in Alliance Networks," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    5. Li, Da-Ye & Nishimura, Yusaku & Men, Ming, 2014. "Fractal markets: Liquidity and investors on different time horizons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 144-151.
    6. Lustosa, Bernardo C. & Cajueiro, Daniel O., 2010. "Constrained information minority game: How was the night at El Farol?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1230-1238.
    7. Guglielmo Maria Caporale & Antoaneta Serguieva & Hao Wu, 2009. "Financial contagion: evolutionary optimization of a multinational agent‐based model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 111-125, January.
    8. Wei, J.R. & Huang, J.P. & Hui, P.M., 2013. "An agent-based model of stock markets incorporating momentum investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(12), pages 2728-2735.
    9. J. Wiesinger & D. Sornette & J. Satinover, 2013. "Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 41(4), pages 475-492, April.
    10. Chen, Fang & Gou, Chengling & Guo, Xiaoqian & Gao, Jieping, 2008. "Prediction of stock markets by the evolutionary mix-game model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3594-3604.
    11. Pištěk, Miroslav & Slanina, František, 2011. "Diversity of scales makes an advantage: The case of the Minority Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2549-2561.
    12. 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.
    13. 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.
    14. Zhang, H.S. & Shen, X.Y. & Huang, J.P., 2016. "Pattern of trends in stock markets as revealed by the renormalization method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 340-346.
    15. Gou, Chengling, 2006. "Deduction of initial strategy distributions of agents in mix-game models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 633-640.
    16. Groot, Robert D. & Musters, Pieter A.D., 2005. "Minority Game of price promotions in fast moving consumer goods markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(2), pages 533-547.
    17. Zhang, Mengqi & Jiang, Xin & Fang, Zehua & Zeng, Yue & Xu, Ke, 2019. "High-order Hidden Markov Model for trend prediction in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 1-12.
    18. Kei Katahira & Yu Chen & Gaku Hashimoto & Hiroshi Okuda, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Papers 1902.02040, arXiv.org.
    19. Miroslav Piv{s}tv{e}k & Frantisek Slanina, 2014. "Diversity of scales makes an advantage: The case of the Minority Game," Papers 1401.4331, arXiv.org.
    20. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.

    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:eee:phsmap:v:419:y:2015:i:c:p:630-641. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.