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Detecting intraday financial market states using temporal clustering

Author

Listed:
  • Dieter Hendricks
  • Tim Gebbie
  • Diane 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 which enable online state detection. The state signature vectors 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 time-scale-specific state space reduction for participating agents.

Suggested Citation

  • Dieter Hendricks & Tim Gebbie & Diane Wilcox, 2015. "Detecting intraday financial market states using temporal clustering," Papers 1508.04900, arXiv.org, revised Feb 2017.
    • Handle: RePEc:arx:papers:1508.04900
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    References listed on IDEAS

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    Cited by:

    1. Tim Gebbie & Fayyaaz Loonat, 2016. "Learning zero-cost portfolio selection with pattern matching," Papers 1605.04600, arXiv.org.
    2. Dieter Hendricks, 2016. "Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets," Papers 1603.06805, arXiv.org, revised May 2017.

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