Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-speed detection of emergent market clustering via an unsupervised
  parallel genetic algorithm   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.

10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat