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An unsupervised parallel genetic cluster algorithm for graphics processing units

D. Hendricks, D. Cieslakiewicz, D. Wilcox, T. Gebbie
School of Computational and Applied Mathematics, University of the Witwatersrand, South Africa

@article{hendricks2014unsupervised,

   title={An unsupervised parallel genetic cluster algorithm for graphics processing units},

   author={Hendricks, Dieter and Cieslakiewicz, Dariusz and Wilcox, Diane and Gebbie, Tim},

   journal={arXiv preprint arXiv:1403.4099},

   year={2014}

}

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During times of stock market turbulence, monitoring the intraday clustering behaviour of financial instruments allows one to better understand market characteristics and systemic risks. While genetic algorithms provide a versatile methodology for identifying such clusters, serial implementations are computationally intensive and can take a long time to converge to the global optimum. We implement a Master-Slave parallel genetic algorithm (PGA) with a Marsili and Giada log-likelihood fitness function to identify clusters within stock correlation matrices. We utilise the Nvidia Compute Unified Device Architecture (CUDA) programming model to implement a PGA and visualise the results using minimal spanning trees (MSTs). We demonstrate that the CUDA PGA implementation runs significantly faster than the test case implementation of a comparable serial genetic algorithm. This, combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, may enhance near-real-time risk assessment for financial practitioners.
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