IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v51y2018i4d10.1007_s10614-016-9639-3.html
   My bibliography  Save this article

Dynamics Evolution of Trading Strategies of Investors in Financial Market

Author

Listed:
  • Binghui Wu

    (Southeast University
    Lanzhou University of Finance and Economics)

  • Tingting Duan

    (Lanzhou University of Finance and Economics)

  • Jianmin He

    (Southeast University)

Abstract

This paper analyzes investors’ trading strategies based on the theory of evolutionarily stable strategies and replicator dynamics model. Under the assumptions closer to a real market, we reveal the relationships between the investor’s price anticipation and equilibrium sustained state. If the mean value of price deviation anticipated by investors is a negative value, there is one equilibrium sustained solution. And if the mean value of price deviation anticipated by investors is a positive value, one equilibrium sustained solution or two equilibrium sustained solutions are likely to appear in our models. This research is beneficial to evaluate differences in revenues between rational traders and noise traders, to understand dynamic evolutionary process of trading strategies, and to find equilibrium sustained solution. In addition, financial crisis as an exogenous factor is introduced into the evolutionary model. Based on theoretical analysis and simulation experiment, the results show that case 3 in theoretical analysis does not occur in the simulation after the outbreak of financial crisis, and case 1 and case 2 in theoretical analysis correspond to the different regions of the anticipated price deviation curves. Moreover, the changes of two equilibrium sustained solutions show opposite tendency characteristics with an increasing of the mean value of price deviation of risk asset. Relative to the existing research results, this paper distinguishes the different yield between risk assets and riskless assets, and considers the existence of transaction cost, assumes investors having different risk aversion coefficient, and takes financial crisis as an example to research the impacts of exogenous variables on investors’ trading strategies. Through comparative analysis, the conclusions drawn from simulation experiment are consistent with equilibrium sustained solutions in theoretical analysis.

Suggested Citation

  • Binghui Wu & Tingting Duan & Jianmin He, 2018. "Dynamics Evolution of Trading Strategies of Investors in Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 743-760, April.
    • Handle: RePEc:kap:compec:v:51:y:2018:i:4:d:10.1007_s10614-016-9639-3
    DOI: 10.1007/s10614-016-9639-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-016-9639-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-016-9639-3?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. Jun Ma, 2013. "Long-Run Risk and Its Implications for the Equity Premium Puzzle: New Evidence from a Multivariate Framework," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(1), pages 121-145, February.
    2. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    3. Robin K. Chou & George H. K. Wang & Yun‐Yi Wang, 2015. "The Impacts of Individual Day Trading Strategies on Market Liquidity and Volatility: Evidence from the Taiwan Index Futures Market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(5), pages 399-425, May.
    4. Shapiro, Dmitry & Zhuang, Anan, 2015. "Dividends as a signaling device and the disappearing dividend puzzle," Journal of Economics and Business, Elsevier, vol. 79(C), pages 62-81.
    5. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    6. R. M. Harstad & R. Selten, 2014. "Bounded-rationality models:tasks to become intellectually competitive," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 5.
    7. Lorne Switzer & Alan Picard, 2015. "Idiosyncratic Volatility, Momentum, Liquidity, and Expected Stock Returns in Developed and Emerging Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 19(3), pages 169-221, September.
    8. Teppo Felin & Stuart Kauffman & Roger Koppl & Giuseppe Longo, 2014. "Economic Opportunity and Evolution: Beyond Landscapes and Bounded Rationality," Post-Print hal-01415115, HAL.
    9. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    10. Elton, Edwin J. & Gruber, Martin J. & Blake, Christopher R. & Shachar, Or, 2013. "Why Do Closed-End Bond Funds Exist? An Additional Explanation for the Growth in Domestic Closed-End Bond Funds," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(2), pages 405-425, April.
    11. Robert F. Stambaugh & Jianfeng Yu & Yu Yuan, 2015. "Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle," Journal of Finance, American Finance Association, vol. 70(5), pages 1903-1948, October.
    12. Thorsten Hens & Terje Lensberg & Klaus Schenk-Hoppé & Peter Wöhrmann, 2011. "An evolutionary explanation of the value premium puzzle," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 803-815, December.
    13. Sascha Füllbrunn & Ernan Haruvy, 2014. "The dividend puzzle: A laboratory investigation," Research in Experimental Economics, in: Experiments in Financial Economics, volume 16, pages 87-110, Emerald Group Publishing Limited.
    14. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    15. Hou, Kewei & Loh, Roger K., 2016. "Have we solved the idiosyncratic volatility puzzle?," Journal of Financial Economics, Elsevier, vol. 121(1), pages 167-194.
    16. Igor V. Evstigneev & Thorsten Hens & Klaus Reiner Schenk-Hoppé, 2011. "Survival and Evolutionary Stability of the Kelly Rule," World Scientific Book Chapters, in: Leonard C MacLean & Edward O Thorp & William T Ziemba (ed.), THE KELLY CAPITAL GROWTH INVESTMENT CRITERION THEORY and PRACTICE, chapter 20, pages 273-284, World Scientific Publishing Co. Pte. Ltd..
    17. G. Daniel & M. Arce & Todd Sandler, 2005. "The Dilemma of the Prisoners’ Dilemmas," Kyklos, Wiley Blackwell, vol. 58(1), pages 3-24, February.
    18. Evanthia Zervoudi & Spyros Spyrou, 2016. "The equity premium puzzle: new evidence on the optimal holding period and optimal asset allocation," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 8(1), pages 39-57, June.
    19. Robert Bloomfield & Maureen O'Hara & Gideon Saar, 2015. "Hidden Liquidity: Some New Light on Dark Trading," Journal of Finance, American Finance Association, vol. 70(5), pages 2227-2274, October.
    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. Binghui Wu & Tingting Duan, 2019. "Nonlinear Dynamics Characteristic of Risk Contagion in Financial Market Based on Agent Modeling and Complex Network," Complexity, Hindawi, vol. 2019, pages 1-12, June.

    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. Binghui Wu & Tingting Duan, 2019. "Nonlinear Dynamics Characteristic of Risk Contagion in Financial Market Based on Agent Modeling and Complex Network," Complexity, Hindawi, vol. 2019, pages 1-12, June.
    2. Yue Zhao & Difang Wan, 2018. "Institutional high frequency trading and price discovery: Evidence from an emerging commodity futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 243-270, February.
    3. Zhaobo Zhu & Wenjie Ding & Yi Jin & Dehua Shen, 2023. "Dissecting the Idiosyncratic Volatility Puzzle: A Fundamental Analysis Approach," Post-Print hal-04194180, HAL.
    4. Bae, Kee-Hong & Yamada, Takeshi & Ito, Keiichi, 2008. "Interaction of investor trades and market volatility: Evidence from the Tokyo Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 16(4), pages 370-388, September.
    5. 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.
    6. Sun, Licheng & Najand, Mohammad & Shen, Jiancheng, 2016. "Stock return predictability and investor sentiment: A high-frequency perspective," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 147-164.
    7. Li, Jinfang, 2022. "The sentiment pricing dynamics with short-term and long-term learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    8. Owain Ap Gwilym & Iftekhar Hasan & Qingwei Wang & Ru Xie, 2016. "In Search of Concepts: The Effects of Speculative Demand on Stock Returns," European Financial Management, European Financial Management Association, vol. 22(3), pages 427-449, June.
    9. Gruen, D.W.R. & Gizycki, M.C., 1993. "Explaining Forward Discount Bias: Is It Anchoring?," Papers 164, Princeton, Woodrow Wilson School - Public and International Affairs.
    10. Viet Hoang Nguyen & Yongcheol Shin, 2011. "Asymmetric Price Impacts of Order Flow on Exchange Rate Dynamics," Melbourne Institute Working Paper Series wp2011n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Salm, Christian A. & Schuppli, Michael, 2010. "Positive feedback trading in stock index futures: International evidence," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 313-322, December.
    12. Zhi Da & Borja Larrain & Clemens Sialm & José Tessada, 2016. "Coordinated Noise Trading: Evidence from Pension Fund Reallocations," NBER Working Papers 22161, National Bureau of Economic Research, Inc.
    13. Böhl, Gregor & Hommes, Cars H., 2021. "Rational vs. irrational beliefs in a complex world," IMFS Working Paper Series 156, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    14. Ang, Tze Chuan ‘Chewie’ & Lam, F.Y. Eric C. & Wei, K.C. John, 2020. "Mispricing firm-level productivity," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 139-163.
    15. 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.
    16. Jang, Jeewon & Kang, Jangkoo, 2019. "Probability of price crashes, rational speculative bubbles, and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 222-247.
    17. Szymon Lis, 2022. "Investor Sentiment in Asset Pricing Models: A Review," Working Papers 2022-14, Faculty of Economic Sciences, University of Warsaw.
    18. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    19. Michael McAleer & Kim Radalj, 2013. "Herding, Information Cascades and Volatility Spillovers in Futures Markets," Journal of Reviews on Global Economics, Lifescience Global, vol. 2, pages 307-329.
    20. Kumari, Jyoti, 2019. "Investor sentiment and stock market liquidity: Evidence from an emerging economy," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 166-180.

    More about this item

    Keywords

    Limited rationality; Evolutionarily stable strategy; Equilibrium sustained solution; Investor anticipation;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles

    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:kap:compec:v:51:y:2018:i:4:d:10.1007_s10614-016-9639-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.