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Detecting intraday financial market states using temporal clustering

Author

Listed:
  • D. Hendricks
  • T. Gebbie
  • D. Wilcox

Abstract

We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then study the identified temporal state features to extract state signature vectors (SSVs) which enable online state detection. The SSVs serve as low-dimensional state descriptors which can be used in learning algorithms for optimal planning in the high-frequency trading domain. We present a feasible scheme for real-time intraday state detection from streaming market data feeds. This study identifies an interesting hierarchy of system behaviour which motivates the need for timescale-specific state space reduction for participating agents.

Suggested Citation

  • D. Hendricks & T. Gebbie & D. Wilcox, 2016. "Detecting intraday financial market states using temporal clustering," Quantitative Finance, Taylor & Francis Journals, vol. 16(11), pages 1657-1678, November.
    • Handle: RePEc:taf:quantf:v:16:y:2016:i:11:p:1657-1678
    DOI: 10.1080/14697688.2016.1171378
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    References listed on IDEAS

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    1. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496, December.
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    Cited by:

    1. Fayyaaz Loonat & Tim Gebbie, 2018. "Learning zero-cost portfolio selection with pattern matching," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-38, September.
    2. Dieter Hendricks & Adam Cobb & Richard Everett & Jonathan Downing & Stephen J. Roberts, 2017. "Inferring agent objectives at different scales of a complex adaptive system," Papers 1712.01137, arXiv.org.
    3. George Barnes & Sanjaye Ramgoolam & Michael Stephanou, 2023. "Permutation invariant Gaussian matrix models for financial correlation matrices," Papers 2306.04569, arXiv.org.
    4. Yelibi, Lionel & Gebbie, Tim, 2020. "Fast Super-Paramagnetic Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    5. Pier Francesco Procacci & Tomaso Aste, 2018. "Forecasting market states," Papers 1807.05836, arXiv.org, revised May 2019.
    6. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Detecting discrete processes with the Epps effect," Papers 2005.10568, arXiv.org, revised Dec 2024.
    7. Patrick Chang & Roger Bukuru & Tim Gebbie, 2019. "Revisiting the Epps effect using volume time averaging: An exercise in R," Papers 1912.02416, arXiv.org, revised Feb 2020.
    8. Patrick Chang & Etienne Pienaar & Tim Gebbie, 2020. "Malliavin-Mancino estimators implemented with non-uniform fast Fourier transforms," Papers 2003.02842, arXiv.org, revised Nov 2020.
    9. Barnes, George & Ramgoolam, Sanjaye & Stephanou, Michael, 2024. "Permutation invariant Gaussian matrix models for financial correlation matrices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 651(C).
    10. Tim Gebbie & Fayyaaz Loonat, 2016. "Learning zero-cost portfolio selection with pattern matching," Papers 1605.04600, arXiv.org.

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