IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0215685.html
   My bibliography  Save this article

Propensity to sell stocks in an artificial stock market

Author

Listed:
  • Wlademir Prates
  • Newton Da Costa Jr
  • Manuel Rocha Armada
  • Sergio Da Silva

Abstract

This experimental study of an artificial stock market investigates what explains the propensity to sell stocks and thus the disposition effect. It is a framed field experiment that follows the steps of a previous observational study of investor behavior in the Finnish stock market. Our experimental approach has an edge over the observational study in that it can control extraneous variables and two or more groups can be compared. We consider in particular the groups of amateur students and professional investors because it is well established in the literature that the disposition effect is less pronounced in professionals. The disposition effect was measured by both the traditional metric and a broader one that properly considers return intervals. A full logit model with control variables was employed in the latter case. As a result, we replicate for the broader definition what already has been found for the traditional measure: that investor experience dampens the disposition effect. Trades with positive returns exhibited higher propensity to sell than trades with negative returns. For the overall sample of participants, we find the disposition effect cannot be explained by prospect theory, but we cast doubt on this stance from partitions of data from amateurs and professionals.

Suggested Citation

  • Wlademir Prates & Newton Da Costa Jr & Manuel Rocha Armada & Sergio Da Silva, 2019. "Propensity to sell stocks in an artificial stock market," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-12, April.
    • Handle: RePEc:plo:pone00:0215685
    DOI: 10.1371/journal.pone.0215685
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215685
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0215685&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0215685?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. Da Costa, Newton & Goulart, Marco & Cupertino, Cesar & Macedo, Jurandir & Da Silva, Sergio, 2013. "The disposition effect and investor experience," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1669-1675.
    2. Shefrin, Hersh, 2010. "Behavioralizing Finance," Foundations and Trends(R) in Finance, now publishers, vol. 4(1–2), pages 1-184, March.
    3. Kaustia, Markku, 2010. "Prospect Theory and the Disposition Effect," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(3), pages 791-812, June.
    4. Dorow, Anderson & da Costa, Newton & Takase, Emilio & Prates, Wlademir & Da Silva, Sergio, 2018. "On the neural substrates of the disposition effect and return performance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 17(C), pages 16-21.
    5. Mark Grinblatt & Matti Keloharju, 2001. "What Makes Investors Trade?," Journal of Finance, American Finance Association, vol. 56(2), pages 589-616, April.
    6. Haijun Yang & Shuheng Chen, 2018. "A heterogeneous artificial stock market model can benefit people against another financial crisis," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-25, June.
    7. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    8. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    9. repec:bla:jfinan:v:53:y:1998:i:5:p:1775-1798 is not listed on IDEAS
    10. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    11. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    12. Ravi Dhar & Ning Zhu, 2006. "Up Close and Personal: Investor Sophistication and the Disposition Effect," Management Science, INFORMS, vol. 52(5), pages 726-740, May.
    13. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.
    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. Kahya, Evrim Hilal & Ekinci, Cumhur, 2022. "Disposition bias among Borsa Istanbul investors: What do we know about type, size and trading frequency?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 35(C).
    2. De Winne, Rudy, 2021. "Measuring the disposition effect," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    3. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers Archive 37358, Iowa State University, Department of Economics.
    4. Elena G. Irwin, 2010. "New Directions For Urban Economic Models Of Land Use Change: Incorporating Spatial Dynamics And Heterogeneity," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 65-91, February.
    5. Min Zheng & Duo Wang & Xue-Zhong He, 2009. "Asymmetry of technical analysis and market price volatility," Published Paper Series 2009-6, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    6. Frank H. Westerhoff, 2009. "Exchange Rate Dynamics: A Nonlinear Survey," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 11, Edward Elgar Publishing.
    7. 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.
    8. Pyo, Dong-Jin, 2015. "Animal spirits and stock market dynamics," ISU General Staff Papers 201501010800005596, Iowa State University, Department of Economics.
    9. Florian Hauser & Jürgen Huber & Bob Kaempff, 2015. "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 205-229, August.
    10. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.
    11. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    12. Barberis, Nicholas & Xiong, Wei, 2012. "Realization utility," Journal of Financial Economics, Elsevier, vol. 104(2), pages 251-271.
    13. Cees Diks & Cars Hommes & Valentyn Panchenko & Roy Weide, 2008. "E&F Chaos: A User Friendly Software Package for Nonlinear Economic Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 221-244, September.
    14. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    15. Yamamoto, Ryuichi & Hirata, Hideaki, 2013. "Strategy switching in the Japanese stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 37(10), pages 2010-2022.
    16. Shu-Heng Chen & Chung-Chih Liao & Pei-Jung Chou, 2008. "On the plausibility of sunspot equilibria," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 3(1), pages 25-41, June.
    17. Chueh-Yung Tsao & Ya-Chi Huang, 2018. "Revisiting the issue of survivability and market efficiency with the Santa Fe Artificial Stock Market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 537-560, October.
    18. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    19. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    20. 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.

    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:plo:pone00:0215685. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.