Detecting intraday financial market states using temporal clustering

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.

30 pages, 16 figures, 8 tables, published in Quantitative Finance

References in corpus (2)

• Power-law distributions in empirical data
• Parameter estimation for power-law distributions by maximum likelihood methods

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