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Long-term ecology of investors in a financial market

Author

Listed:
  • Federico Musciotto

    (Università di Palermo)

  • Luca Marotta

    (Università di Palermo)

  • Jyrki Piilo

    (University of Turku)

  • Rosario N. Mantegna

    (Università di Palermo
    Complexity Science Hub Vienna
    University College London)

Abstract

The cornerstone of modern finance is the efficient market hypothesis. Under this hypothesis all information available about a financial asset is immediately incorporated into its price dynamics by fully rational investors. In contrast to this hypothesis many studies have pointed out behavioral biases in investors. Recently it has become possible to access databases that track the trading decisions of investors. Studies of such databases have shown that investors acting in a financial market are highly heterogeneous among them, and that heterogeneity is a common characteristic of many financial markets. The article describes an empirical study of the daily trading decisions of all Finnish investors investing Nokia stock over a time period of 15 years. The investigation is performed by adapting and using methods and tools in network science. By investigating daily trading decisions, and by constructing the time-evolution of statistically validated networks of investors, clusters of investors—and their time evolution— which are characterized by similar trading profiles are detected. These clusters are performing distinct trading decisions on time scales ranging from several months to twelve years. These empirical observations show the presence of an ecology of groups of investors characterized by different attributes and by various investment styles over many years. Some of the detected clusters present a persistent over-expression of specific investor categories. The study shows that the logarithm of the ratio of pairs of statistically validated trading decisions is different for different values of the market volatility. These findings suggest that an ecology of investors is present in financial markets and that groups of traders are always competing, adopting, using and eventually discarding new investment strategies. This adaptation process is observed over a multiplicity of time scales, and is compatible with several conclusions of behavioral finance and with the assumptions of the so-called adaptive market hypothesis.

Suggested Citation

  • Federico Musciotto & Luca Marotta & Jyrki Piilo & Rosario N. Mantegna, 2018. "Long-term ecology of investors in a financial market," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:4:y:2018:i:1:d:10.1057_s41599-018-0145-1
    DOI: 10.1057/s41599-018-0145-1
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    2. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
    3. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    4. Geetika Madaan & Sanjeet Singh, 2019. "An Analysis of Behavioral Biases in Investment Decision-Making," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 10(4), pages 55-67, July.
    5. Takumi Sueshige & Didier Sornette & Hideki Takayasu & Misako Takayasu, 2019. "Classification of position management strategies at the order-book level and their influences on future market-price formation," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    6. Carlo Campajola & Fabrizio Lillo & Daniele Tantari, 2019. "Unveiling the relation between herding and liquidity with trader lead-lag networks," Papers 1909.10807, arXiv.org, revised Mar 2020.
    7. 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.
    8. Maarten P. Scholl & Anisoara Calinescu & J. Doyne Farmer, 2021. "How market ecology explains market malfunction," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(26), pages 2015574118-, June.
    9. Marcus Cordi & Serge Kassibrakis & Damien Challet, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Working Papers hal-01966419, HAL.
    10. Marcus Cordi & Damien Challet & Serge Kassibrakis, 2021. "The market nanostructure origin of asset price time reversal asymmetry," Quantitative Finance, Taylor & Francis Journals, vol. 21(2), pages 295-304, February.
    11. Zain UI Abideen & Zeeshan Ahmed & Huan Qiu & Yiwei Zhao, 2023. "Do Behavioral Biases Affect Investors’ Investment Decision Making? Evidence from the Pakistani Equity Market," Risks, MDPI, vol. 11(6), pages 1-32, June.
    12. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    13. Semenova, Valentina & Winkler, Julian, 2020. "Reddit's Self-Organised Bull Runs," MPRA Paper 105443, University Library of Munich, Germany.
    14. Margarita Baltakienė & Kęstutis Baltakys & Juho Kanniainen & Dino Pedreschi & Fabrizio Lillo, 2019. "Clusters of investors around initial public offering," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-14, December.
    15. Takumi Sueshige & Kiyoshi Kanazawa & Hideki Takayasu & Misako Takayasu, 2018. "Ecology of trading strategies in a forex market for limit and market orders," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-14, December.
    16. Oldham, Matthew, 2020. "Quantifying the concerns of Dimon and Buffett with data and computation," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).

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