IDEAS home Printed from https://ideas.repec.org/a/hin/complx/1678086.html
   My bibliography  Save this article

Exploring Contrarian Degree in the Trading Behavior of China's Stock Market

Author

Listed:
  • Yue Chen
  • Xiaojian Niu
  • Yan Zhang

Abstract

We study the contrarian and trend-following trading behavior of market timers in China's stock market. Using a network model to describe interpersonal relationships, we deploy the Ising model to capture trading strategies for both contrarians and followers. With empirical data of China's stock market, we find that contrarians account for 12-14% of trading volume. We further compare the performance of contrarians and followers and demonstrate the inefficiency of China's stock market where timing arbitrage exists. We highlight the fact that while the actual return sequence is driven by followers, the contrarians seize a lot of profitable arbitrage opportunities.

Suggested Citation

  • Yue Chen & Xiaojian Niu & Yan Zhang, 2019. "Exploring Contrarian Degree in the Trading Behavior of China's Stock Market," Complexity, Hindawi, vol. 2019, pages 1-12, April.
  • Handle: RePEc:hin:complx:1678086
    DOI: 10.1155/2019/1678086
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/1678086.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2019/1678086.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2019/1678086?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
    ---><---

    References listed on IDEAS

    as
    1. Ponta, Linda & Pastore, Stefano & Cincotti, Silvano, 2018. "Static and dynamic factors in an information-based multi-asset artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 814-823.
    2. Vincenzo Crescimanna & Luca Di Persio, 2016. "Herd Behavior and Financial Crashes: An Interacting Particle System Approach," Journal of Mathematics, Hindawi, vol. 2016, pages 1-7, February.
    3. Shi, Huai-Long & Zhou, Wei-Xing, 2017. "Time series momentum and contrarian effects in the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 309-318.
    4. Juan Eberhard & Jaime F. Lavin & Alejandro Montecinos-Pearce, 2017. "A Network-Based Dynamic Analysis in an Equity Stock Market," Complexity, Hindawi, vol. 2017, pages 1-16, November.
    5. A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 13(3), pages 547-560, February.
    6. Sornette, Didier & Zhou, Wei-Xing, 2006. "Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 704-726.
    7. D. Sornette, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based models," Papers 1404.0243, arXiv.org.
    8. Immonen, Eero, 2017. "Simple agent-based dynamical system models for efficient financial markets: Theory and examples," Journal of Mathematical Economics, Elsevier, vol. 69(C), pages 38-53.
    9. Bonggyun Ko & Jae Wook Song & Woojin Chang, 2016. "Simulation of financial market via nonlinear Ising model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(04), pages 1-15, April.
    10. Kaizoji, Taisei & Bornholdt, Stefan & Fujiwara, Yoshi, 2002. "Dynamics of price and trading volume in a spin model of stock markets with heterogeneous agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 441-452.
    11. Linda Ponta & Silvano Cincotti, 2018. "Traders’ Networks of Interactions and Structural Properties of Financial Markets: An Agent-Based Approach," Complexity, Hindawi, vol. 2018, pages 1-9, January.
    12. Didier SORNETTE, 2014. "Physics and Financial Economics (1776-2014): Puzzles, Ising and Agent-Based Models," Swiss Finance Institute Research Paper Series 14-25, Swiss Finance Institute.
    13. Wen Fang & Jun Wang, 2012. "Statistical Properties And Multifractal Behaviors Of Market Returns By Ising Dynamic Systems," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 23(03), pages 1-14.
    14. Kacperski, Krzysztof & Hołyst, Janusz A., 2000. "Phase transitions as a persistent feature of groups with leaders in models of opinion formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 631-643.
    15. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    16. Hung-Wen Lin & Mao-Wei Hung & Jing-Bo Huang, 2018. "Artificial Momentum, Native Contrarian, and Transparency in China," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 263-294, February.
    17. A. Krawiecki, 2005. "Microscopic Spin Model For The Stock Market With Attractor Bubbling And Heterogeneous Agents," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 549-559.
    18. da Silva, L.R & Stauffer, D, 2001. "Ising-correlated clusters in the Cont-Bouchaud stock market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 294(1), pages 235-238.
    19. Chang, Sanders S. & Albert Wang, F., 2019. "Informed contrarian trades and stock returns," Journal of Financial Markets, Elsevier, vol. 42(C), pages 75-93.
    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. Quanbo Zha & Gang Kou & Hengjie Zhang & Haiming Liang & Xia Chen & Cong-Cong Li & Yucheng Dong, 2020. "Opinion dynamics in finance and business: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-22, December.
    2. Yuichi Ikeda, 2020. "An Interacting Agent Model of Economic Crisis," Papers 2001.11843, arXiv.org.
    3. 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.
    4. Simon Cramer & Torsten Trimborn, 2019. "Stylized Facts and Agent-Based Modeling," Papers 1912.02684, arXiv.org.
    5. Torsten Trimborn & Philipp Otte & Simon Cramer & Max Beikirch & Emma Pabich & Martin Frank, 2018. "SABCEMM-A Simulator for Agent-Based Computational Economic Market Models," Papers 1801.01811, arXiv.org, revised Oct 2018.
    6. Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    7. Xing, Yani & Wang, Jun, 2019. "Statistical volatility duration and complexity of financial dynamics on Sierpinski gasket lattice percolation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 234-247.
    8. Matthew Oldham, 2019. "Understanding How Short-Termism and a Dynamic Investor Network Affects Investor Returns: An Agent-Based Perspective," Complexity, Hindawi, vol. 2019, pages 1-21, July.
    9. Katahira, Kei & Chen, Yu & Hashimoto, Gaku & Okuda, Hiroshi, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 503-518.
    10. Stein, Julian Alexander Cornelius & Braun, Dieter, 2019. "Stability of a time-homogeneous system of money and antimoney in an agent-based random economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 232-249.
    11. T. T. Chen & B. Zheng & Y. Li & X. F. Jiang, 2017. "New approaches in agent-based modeling of complex financial systems," Papers 1703.06840, arXiv.org.
    12. Kei Katahira & Yu Chen, 2019. "Heterogeneous wealth distribution, round-trip trading and the emergence of volatility clustering in Speculation Game," Papers 1909.03185, arXiv.org.
    13. Kristoufek, Ladislav & Vošvrda, Miloslav S., 2016. "Herding, minority game, market clearing and efficient markets in a simple spin model framework," FinMaP-Working Papers 68, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    14. Alessio Emanuele Biondo, 2019. "Order book modeling and financial stability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 469-489, September.
    15. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034, Decembrie.
    16. Tilfani, Oussama & Kristoufek, Ladislav & Ferreira, Paulo & El Boukfaoui, My Youssef, 2022. "Heterogeneity in economic relationships: Scale dependence through the multivariate fractal regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    17. 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.
    18. Maximilian Beikirch & Simon Cramer & Martin Frank & Philipp Otte & Emma Pabich & Torsten Trimborn, 2020. "Robust Mathematical Formulation And Probabilistic Description Of Agent-Based Computational Economic Market Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 23(06), pages 1-41, September.
    19. Zhong, Li-Xin & Xu, Wen-Juan & Chen, Rong-Da & Zhong, Chen-Yang & Qiu, Tian & Ren, Fei & He, Yun-Xing, 2018. "Self-reinforcing feedback loop in financial markets with coupling of market impact and momentum traders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 301-310.
    20. Fang, Wen & Ke, Jinchuan & Wang, Jun & Feng, Ling, 2016. "Linking market interaction intensity of 3D Ising type financial model with market volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 531-542.

    More about this item

    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:hin:complx:1678086. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.