IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i5p1448-d212313.html
   My bibliography  Save this article

Individual Investors’ Learning Behavior and Its Impact on Their Herd Bias: An Integrated Analysis in the Context of Stock Trading

Author

Listed:
  • Kalugala Vidanalage Aruna Shantha

    (School of Management, Wuhan University of Technology, Wuhan 430070, China
    Faculty of Management Studies, Rajarata University of Sri Lanka, Mihintale 50300, Sri Lanka)

Abstract

The efficient functioning of capital markets ensures that information on companies’ sustainable development endeavors is fully and instantly incorporated into stock prices, which facilitates them in raising capital requirements at a lower cost. It, however, is impaired when market participants are inclined to behavioral biases. The Adaptive Market Hypothesis predicts that such behavioral biases are evolutionary. In that sense, market participants are capable of learning their behavioral mistakes and adapting to market conditions over time. Based on this perspective, this paper aims to explore how learning occurs within individual investors to reduce their herd bias. The data was collected by distributing a web-based self-administrated questionnaire to a sample of 1000 individual investors of the Colombo Stock Exchange, who were randomly selected during a period from March to August 2018. A total of 189 responses were received, which were analyzed using the structural equation modelling technique to test the hypotheses of the theoretical model. The results show that learning takes place when investors cognitively evaluate past trading experiences, which is induced by their desire for learning, and, consequently, reduces their herd bias. However, as the model predicts, strengthening this cognitive reflection from the relationship with the investment advisor and social learning among investors through their peer-relationships appear to be absent due to uncertain market conditions prevailed during the study period and dominance of unsophisticated investors in the market. From these findings, this paper concludes that the cognitive reflection of past experiences and the nature of the trading environment determine the extent of learning within individual investors.

Suggested Citation

  • Kalugala Vidanalage Aruna Shantha, 2019. "Individual Investors’ Learning Behavior and Its Impact on Their Herd Bias: An Integrated Analysis in the Context of Stock Trading," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1448-:d:212313
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/5/1448/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/5/1448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sumit Agarwal & John C. Driscoll & Xavier Gabaix & David Laibson, 2007. "The Age of Reason: Financial Decisions Over the Lifecycle," NBER Working Papers 13191, National Bureau of Economic Research, Inc.
    2. Gervais, Simon & Odean, Terrance, 2001. "Learning to be Overconfident," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 1-27.
    3. Bossan, Benjamin & Jann, Ole & Hammerstein, Peter, 2015. "The evolution of social learning and its economic consequences," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 266-288.
    4. Batmunkh John Munkh-Ulzii & Michael McAleer & Massoud Moslehpour & Wing-Keung Wong, 2018. "Confucius and Herding Behaviour in the Stock Markets in China and Taiwan," Sustainability, MDPI, vol. 10(12), pages 1-16, November.
    5. Nicolosi, Gina & Peng, Liang & Zhu, Ning, 2009. "Do individual investors learn from their trading experience?," Journal of Financial Markets, Elsevier, vol. 12(2), pages 317-336, May.
    6. Lei Feng & Mark Seasholes, 2005. "Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets?," Review of Finance, Springer, vol. 9(3), pages 305-351, September.
    7. Satish Kumar & Nisha Goyal, 2015. "Behavioural biases in investment decision making – a systematic literature review," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 7(1), pages 88-108, February.
    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. Nelson Maina Waweru & Evelyne Munyoki & Enrico Uliana, 2008. "The effects of behavioural factors in investment decision-making: a survey of institutional investors operating at the Nairobi Stock Exchange," International Journal of Business and Emerging Markets, Inderscience Enterprises Ltd, vol. 1(1), pages 24-41.
    10. Lei Feng & Mark S. Seasholes, 2005. "Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets?," Review of Finance, European Finance Association, vol. 9(3), pages 305-351.
    11. Sarstedt, Marko & Ringle, Christian M. & Smith, Donna & Reams, Russell & Hair, Joseph F., 2014. "Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers," Journal of Family Business Strategy, Elsevier, vol. 5(1), pages 105-115.
    12. John A. List, 2011. "Does Market Experience Eliminate Market Anomalies? The Case of Exogenous Market Experience," American Economic Review, American Economic Association, vol. 101(3), pages 313-317, May.
    13. Jennifer Itzkowitz & Jesse Itzkowitz, 2017. "Name-Based Behavioral Biases: Are Expert Investors Immune?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(2), pages 180-188, April.
    14. Steve Waygood, 2011. "How do the capital markets undermine sustainable development? What can be done to correct this?," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 1(1), pages 81-87, February.
    15. Blake LeBaron, 2011. "Active and Passive Learning in Agent-based Financial Markets," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 37(1), pages 35-43.
    16. Yao, Juan & Ma, Chuanchan & He, William Peng, 2014. "Investor herding behaviour of Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 12-29.
    17. Sina Wulfmeyer, 2016. "Irrational Mutual Fund Managers: Explaining Differences in Their Behavior," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 17(2), pages 99-123, April.
    18. LeBaron, Blake, 2012. "Heterogeneous gain learning and the dynamics of asset prices," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 424-445.
    19. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2016. "The evolution of stock market efficiency in the US: a non-Bayesian time-varying model approach," Applied Economics, Taylor & Francis Journals, vol. 48(7), pages 621-635, February.
    20. 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.
    21. World Commission on Environment and Development,, 1987. "Our Common Future," OUP Catalogue, Oxford University Press, number 9780192820808.
    22. Mr. Sunil Sharma & Sushil Bikhchandani, 2000. "Herd Behavior in Financial Markets: A Review," IMF Working Papers 2000/048, International Monetary Fund.
    23. Kalugala Vidanalage Aruna Shantha & Chen Xiaofang & L.P.S. Gamini, 2018. "A conceptual framework on individual investors’ learning behavior in the context of stock trading: An integrated perspective," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1544062-154, January.
    24. Judith Chevalier & Glenn Ellison, 1999. "Are Some Mutual Fund Managers Better Than Others? Cross‐Sectional Patterns in Behavior and Performance," Journal of Finance, American Finance Association, vol. 54(3), pages 875-899, June.
    25. Abreu, Margarida & Mendes, Victor, 2012. "Information, overconfidence and trading: Do the sources of information matter?," Journal of Economic Psychology, Elsevier, vol. 33(4), pages 868-881.
    26. Evermann, Joerg & Tate, Mary, 2016. "Assessing the predictive performance of structural equation model estimators," Journal of Business Research, Elsevier, vol. 69(10), pages 4565-4582.
    27. Balcilar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2014. "What drives herding in oil-rich, developing stock markets? Relative roles of own volatility and global factors," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 418-440.
    28. Spyros Spyrou, 2013. "Herding in financial markets: a review of the literature," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 5(2), pages 175-194, November.
    29. John Hulland, 1999. "Use of partial least squares (PLS) in strategic management research: a review of four recent studies," Strategic Management Journal, Wiley Blackwell, vol. 20(2), pages 195-204, February.
    30. Prashant Kale & Harbir Singh & Howard Perlmutter, 2000. "Learning and protection of proprietary assets in strategic alliances: building relational capital," Strategic Management Journal, Wiley Blackwell, vol. 21(3), pages 217-237, March.
    31. Guney, Yilmaz & Kallinterakis, Vasileios & Komba, Gabriel, 2017. "Herding in frontier markets: Evidence from African stock exchanges," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 152-175.
    32. Xinshu Zhao & John G. Lynch & Qimei Chen, 2010. "Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 197-206, August.
    33. 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.
    34. Bodnaruk, Andriy & Simonov, Andrei, 2015. "Do financial experts make better investment decisions?," Journal of Financial Intermediation, Elsevier, vol. 24(4), pages 514-536.
    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. Puput Tri Komalasari & Marwan Asri & Bernardinus M. Purwanto & Bowo Setiyono, 2022. "Herding behaviour in the capital market: What do we know and what is next?," Management Review Quarterly, Springer, vol. 72(3), pages 745-787, September.
    2. Glaser, Markus & Weber, Martin, 2007. "Why inexperienced investors do not learn: They do not know their past portfolio performance," Finance Research Letters, Elsevier, vol. 4(4), pages 203-216, December.
    3. Philipp Stephan & Rüdiger Nitzsch, 2013. "Do individual investors’ stock recommendations in online communities contain investment value?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(2), pages 149-186, June.
    4. Oscar A. Stolper & Andreas Walter, 2017. "Financial literacy, financial advice, and financial behavior," Journal of Business Economics, Springer, vol. 87(5), pages 581-643, July.
    5. Peiran Jiao, 2015. "The Double-Channeled Effects of Experience on Individual Investment Decisions: Experimental Evidence," Economics Series Working Papers 766, University of Oxford, Department of Economics.
    6. Syed Aliya Zahera & Rohit Bansal, 2018. "Do investors exhibit behavioral biases in investment decision making? A systematic review," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 10(2), pages 210-251, May.
    7. Hayley, Simon & Marsh, Ian W., 2016. "What do retail FX traders learn?," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 16-38.
    8. Duxbury, Darren & Hudson, Robert & Keasey, Kevin & Yang, Zhishu & Yao, Songyao, 2015. "Do the disposition and house money effects coexist? A reconciliation of two behavioral biases using individual investor-level data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 55-68.
    9. Paulo Pereira Silva & Victor Mendes, 2023. "Education and financial mistakes: The case of avoidable trading fees in stock markets," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(2), pages 173-202, May.
    10. Maximilian Koestner & Benjamin Loos & Steffen Meyer & Andreas Hackethal, 2017. "Do individual investors learn from their mistakes?," Journal of Business Economics, Springer, vol. 87(5), pages 669-703, July.
    11. Wanidwaranan, Phasin & Padungsaksawasdi, Chaiyuth, 2020. "The effect of return jumps on herd behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    12. Talpsepp, Tõnn & Liivamägi, Kristjan & Vaarmets, Tarvo, 2020. "Academic abilities, education and performance in the stock market," Journal of Banking & Finance, Elsevier, vol. 117(C).
    13. Nguyen, Huu Manh & Bakry, Walid & Vuong, Thi Huong Giang, 2023. "COVID-19 pandemic and herd behavior: Evidence from a frontier market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    14. Markus Glaser & Thomas Langer & Martin Weber, 2007. "On the Trend Recognition and Forecasting Ability of Professional Traders," Decision Analysis, INFORMS, vol. 4(4), pages 176-193, December.
    15. Liao, Jingchi & Peng, Cameron & Zhu, Ning, 2021. "Extrapolative bubbles and trading volume," LSE Research Online Documents on Economics 118887, London School of Economics and Political Science, LSE Library.
    16. Tanjim Hossain & John A. List, 2012. "The Behavioralist Visits the Factory: Increasing Productivity Using Simple Framing Manipulations," Management Science, INFORMS, vol. 58(12), pages 2151-2167, December.
    17. Oliver Gloede & Lukas Menkhoff, 2014. "Financial Professionals' Overconfidence: Is It Experience, Function, or Attitude?," European Financial Management, European Financial Management Association, vol. 20(2), pages 236-269, March.
    18. Peiran Jiao, 2015. "Losing from Naive Reinforcement Learning: A Survival Analysis of Individual Repurchase Decisions," Economics Series Working Papers 765, University of Oxford, Department of Economics.
    19. Greenwood, Robin & Nagel, Stefan, 2009. "Inexperienced investors and bubbles," Journal of Financial Economics, Elsevier, vol. 93(2), pages 239-258, August.
    20. Ganesh R & Naresh G & Thiyagarajan S, 2020. "Manifesting Overconfidence Bias and Disposition Effect in the Stock Market," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 19(3), pages 257-284, December.

    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:gam:jsusta:v:11:y:2019:i:5:p:1448-:d:212313. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.