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Detecting intraday financial market states using temporal clustering

Listed author(s):
  • Dieter Hendricks
  • Tim Gebbie
  • Diane Wilcox
Registered author(s):

    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.

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    File URL: http://arxiv.org/pdf/1508.04900
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    Paper provided by arXiv.org in its series Papers with number 1508.04900.

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    Date of creation: Aug 2015
    Date of revision: Feb 2017
    Handle: RePEc:arx:papers:1508.04900
    Contact details of provider: Web page: http://arxiv.org/

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