IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2406.19403.html
   My bibliography  Save this paper

Temporal distribution of clusters of investors and their application in prediction with expert advice

Author

Listed:
  • Wojciech Wisniewski
  • Yuri Kalnishkan
  • David Lindsay
  • Si^an Lindsay

Abstract

Financial organisations such as brokers face a significant challenge in servicing the investment needs of thousands of their traders worldwide. This task is further compounded since individual traders will have their own risk appetite and investment goals. Traders may look to capture short-term trends in the market which last only seconds to minutes, or they may have longer-term views which last several days to months. To reduce the complexity of this task, client trades can be clustered. By examining such clusters, we would likely observe many traders following common patterns of investment, but how do these patterns vary through time? Knowledge regarding the temporal distributions of such clusters may help financial institutions manage the overall portfolio of risk that accumulates from underlying trader positions. This study contributes to the field by demonstrating that the distribution of clusters derived from the real-world trades of 20k Foreign Exchange (FX) traders (from 2015 to 2017) is described in accordance with Ewens' Sampling Distribution. Further, we show that the Aggregating Algorithm (AA), an on-line prediction with expert advice algorithm, can be applied to the aforementioned real-world data in order to improve the returns of portfolios of trader risk. However we found that the AA 'struggles' when presented with too many trader ``experts'', especially when there are many trades with similar overall patterns. To help overcome this challenge, we have applied and compared the use of Statistically Validated Networks (SVN) with a hierarchical clustering approach on a subset of the data, demonstrating that both approaches can be used to significantly improve results of the AA in terms of profitability and smoothness of returns.

Suggested Citation

  • Wojciech Wisniewski & Yuri Kalnishkan & David Lindsay & Si^an Lindsay, 2024. "Temporal distribution of clusters of investors and their application in prediction with expert advice," Papers 2406.19403, arXiv.org.
    • Handle: RePEc:arx:papers:2406.19403
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2406.19403
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. repec:dau:papers:123456789/4688 is not listed on IDEAS
    3. 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.
    4. Margarita Baltakien.e & Kk{e}stutis Baltakys & Juho Kanniainen & Dino Pedreschi & Fabrizio Lillo, 2019. "Clusters of investors around Initial Public Offering," Papers 1905.13508, arXiv.org, revised Nov 2019.
    5. Musciotto, Federico & Marotta, Luca & Miccichè, Salvatore & Piilo, Jyrki & Mantegna, Rosario N., 2016. "Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 267-278.
    6. 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.
    7. Yong Zhang & Xingyu Yang, 2017. "Online Portfolio Selection Strategy Based on Combining Experts’ Advice," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 141-159, June.
    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. 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.
    2. 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.
    3. Piero Mazzarisi & Adele Ravagnani & Paola Deriu & Fabrizio Lillo & Francesca Medda & Antonio Russo, 2022. "A machine learning approach to support decision in insider trading detection," Papers 2212.05912, arXiv.org.
    4. Baltakienė, Margarita & Kanniainen, Juho & Baltakys, Kęstutis, 2021. "Identification of information networks in stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    5. Paola Deriu & Fabrizio Lillo & Piero Mazzarisi & Francesca Medda & Adele Ravagnani & Antonio Russo, 2022. "How Covid mobility restrictions modified the population of investors in Italian stock markets," Papers 2208.00181, arXiv.org.
    6. Jin’an He & Shicheng Yin & Fangping Peng, 2024. "Weak aggregating specialist algorithm for online portfolio selection," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2405-2434, June.
    7. 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.
    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. Ranieri Dugo & Giacomo Giorgio & Paolo Pigato, 2024. "The Multivariate Fractional Ornstein-Uhlenbeck Process," CEIS Research Paper 581, Tor Vergata University, CEIS, revised 28 Aug 2024.
    10. Frank Emmert-Streib & Aliyu Musa & Kestutis Baltakys & Juho Kanniainen & Shailesh Tripathi & Olli Yli-Harja & Herbert Jodlbauer & Matthias Dehmer, 2017. "Computational Analysis of the structural properties of Economic and Financial Networks," Papers 1710.04455, arXiv.org.
    11. Oldham, Matthew, 2020. "Quantifying the concerns of Dimon and Buffett with data and computation," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    12. Artem Stopochkin & Inessa Sytnik & Janusz Wielki & Nataliia Zemlianska, 2021. "Methodology for Building Trader's Investment Strategy Based on Assessment of the Market Value of the Company," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 913-935.
    13. Semenova, Valentina & Winkler, Julian, 2020. "Reddit's Self-Organised Bull Runs," MPRA Paper 105443, University Library of Munich, Germany.
    14. Xingyu Yang & Jin’an He & Hong Lin & Yong Zhang, 2020. "Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 231-251, January.
    15. 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.
    16. Bongiorno, Christian & Miccichè, Salvatore & Mantegna, Rosario N., 2022. "Statistically validated hierarchical clustering: Nested partitions in hierarchical trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    17. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    18. 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.
    19. Yong Zhang & Jiahao Li & Xingyu Yang & Jianliang Zhang, 2024. "Competitive Online Strategy Based on Improved Exponential Gradient Expert and Aggregating Method," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 789-814, August.
    20. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2406.19403. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.