IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02276055.html
   My bibliography  Save this paper

Deep Prediction Of Investor Interest: a Supervised Clustering Approach

Author

Listed:
  • Baptiste Barreau

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec, BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab)

  • Laurent Carlier

    (BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab)

  • Damien Challet

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

Abstract

We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given timeframe. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking.

Suggested Citation

  • Baptiste Barreau & Laurent Carlier & Damien Challet, 2021. "Deep Prediction Of Investor Interest: a Supervised Clustering Approach," Post-Print hal-02276055, HAL.
    • Handle: RePEc:hal:journl:hal-02276055
    DOI: 10.3233/AF-200296
    Note: View the original document on HAL open archive server: https://hal.science/hal-02276055v3
    as

    Download full text from publisher

    File URL: https://hal.science/hal-02276055v3/document
    Download Restriction: no
    File URL: https://libkey.io/10.3233/AF-200296?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
    2. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
    3. Kk{e}stutis Baltakys & Juho Kanniainen & Frank Emmert-Streib, 2017. "Multilayer Aggregation with Statistical Validation: Application to Investor Networks," Papers 1708.09850, arXiv.org, revised May 2018.
    4. repec:wsi:acsxxx:v:21:y:2018:i:08:n:s0219525918500194 is not listed on IDEAS
    5. 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.
    6. Damien Challet & R'emy Chicheportiche & Mehdi Lallouache & Serge Kassibrakis, 2016. "Statistically validated lead-lag networks and inventory prediction in the foreign exchange market," Papers 1609.04640, arXiv.org, revised Jul 2018.
    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. Baptiste Barreau & Laurent Carlier & Damien Challet, 2019. "Deep Prediction of Investor Interest: a Supervised Clustering Approach," Papers 1909.05289, arXiv.org, revised Feb 2021.
    2. Ivan Peñaloza & Pablo Padilla, 2022. "A Pricing Method in a Constrained Market with Differential Informational Frameworks," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1055-1100, October.
    3. Ymir Makinen & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Forecasting of Jump Arrivals in Stock Prices: New Attention-based Network Architecture using Limit Order Book Data," Papers 1810.10845, arXiv.org.
    4. 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.
    5. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books," Papers 1808.03668, arXiv.org, revised Jan 2020.
    6. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
    7. Christa Cuchiero & Wahid Khosrawi & Josef Teichmann, 2020. "A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models," Risks, MDPI, vol. 8(4), pages 1-31, September.
    8. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    9. Takuya Shintate & Lukáš Pichl, 2019. "Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning," JRFM, MDPI, vol. 12(1), pages 1-15, January.
    10. Qi Zhao, 2020. "A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data," Papers 2010.07404, arXiv.org.
    11. Bryan Lim & Stefan Zohren & Stephen Roberts, 2019. "Enhancing Time Series Momentum Strategies Using Deep Neural Networks," Papers 1904.04912, arXiv.org, revised Sep 2020.
    12. Paul Bilokon & Yitao Qiu, 2023. "Transformers versus LSTMs for electronic trading," Papers 2309.11400, arXiv.org.
    13. Kieran Wood & Samuel Kessler & Stephen J. Roberts & Stefan Zohren, 2023. "Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies," Papers 2310.10500, arXiv.org, revised Mar 2024.
    14. 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.
    15. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    16. Arthur le Calvez & Dave Cliff, 2018. "Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market," Papers 1811.02880, arXiv.org.
    17. Allison Koenecke & Amita Gajewar, 2019. "Curriculum Learning in Deep Neural Networks for Financial Forecasting," Papers 1904.12887, arXiv.org, revised Jul 2019.
    18. Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665, arXiv.org, revised Nov 2019.
    19. Ymir Mäkinen & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data," Quantitative Finance, Taylor & Francis Journals, vol. 19(12), pages 2033-2050, December.
    20. Marcus Cordi & Serge Kassibrakis & Damien Challet, 2018. "The market nanostructure origin of asset price time reversal asymmetry," Working Papers hal-01966419, HAL.

    More about this item

    Keywords

    investor activity prediction; deep learning; neural networks; mixture of experts; clustering;
    All these keywords.

    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:hal:journl:hal-02276055. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.