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High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

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  • Dieter Hendricks
  • Diane Wilcox
  • Tim Gebbie

Abstract

We implement a master-slave parallel genetic algorithm (PGA) with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a PGA and visualise the results using disjoint minimal spanning trees (MSTs). We demonstrate that our GPU PGA, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable due to compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

Suggested Citation

  • Dieter Hendricks & Diane Wilcox & Tim Gebbie, 2014. "High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm," Papers 1403.4099, arXiv.org, revised Aug 2015.
    • Handle: RePEc:arx:papers:1403.4099
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    References listed on IDEAS

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    1. Giada, Lorenzo & Marsili, Matteo, 2002. "Algorithms of maximum likelihood data clustering with applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 315(3), pages 650-664.
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    Cited by:

    1. Dieter Hendricks & Tim Gebbie & Diane Wilcox, 2015. "Detecting intraday financial market states using temporal clustering," Papers 1508.04900, arXiv.org, revised Feb 2017.

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