During times of stock market turbulence and crises, monitoring the clustering behaviour
of financial instruments allows one to better understand the behaviour of the stock market
and the associated systemic risks. In the study undertaken, I apply an effective and
performant approach to classify data clusters in order to better understand correlations
between stocks. The novel methods aim to address the lack of effective algorithms to
deal with high-performance cluster analysis in the context of large complex real-time
low-latency data-sets. I apply an efficient and novel data clustering approach, namely
the Giada and Marsili log-likelihood function derived from the Noh model and use a Parallel
Genetic Algorithm in order to isolate residual data clusters. Genetic Algorithms
(GAs) are a very versatile methodology for scientific computing, while the application
of Parallel Genetic Algorithms (PGAs) further increases the computational efficiency.
They are an effective vehicle to mine data sets for information and traits. However,
the traditional parallel computing environment can be expensive. I focused on adopting
NVIDIAs Compute Unified Device Architecture (CUDA) programming model in order
to develop a PGA framework for my computation solution, where I aim to efficiently
filter out residual clusters. The results show that the application of the PGA with
the novel clustering function on the CUDA platform is quite effective to improve the
computational efficiency of parallel data cluster analysis.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/14843 |
| Date | 01 July 2014 |
| Creators | Cieslakiewicz, Dariusz |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf, application/pdf |
Page generated in 0.0019 seconds