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We implement a master-slave parallel genetic algorithm (PGA) with a bespokelog-likelihood fitness function to identify emergent clusters within priceevolutions. We use graphics processing units (GPUs) to implement a PGA andvisualise the results using disjoint minimal spanning trees (MSTs). Wedemonstrate that our GPU PGA, implemented on a commercially available generalpurpose GPU, is able to recover stock clusters in sub-second speed, based on asubset of stocks in the South African market. This represents a pragmaticchoice for low-cost, scalable parallel computing and is significantly fasterthan a prototype serial implementation in an optimised C-basedfourth-generation programming language, although the results are not directlycomparable due to compiler differences. Combined with fast online intradaycorrelation matrix estimation from high frequency data for clusteridentification, the proposed implementation offers cost-effective,near-real-time risk assessment for financial practitioners.

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