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Nonlinearity Linkage Detection for Financial Time Series Analysis

Chiotis, T; Clack, CD; (2007) Nonlinearity Linkage Detection for Financial Time Series Analysis. In: GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2. (pp. 1179 - 1186). ASSOC COMPUTING MACHINERY

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Standard detection algorithms for nonlinearity linkage fail when applied to typical problems in the analysis of financial time-series data. We explain how this failure arises when standard algorithms are applied naively, how linkage detection needs to be applied directly to the observed data samples, and how this raises problems that; are not addressed by current techniques.We extend the, existing DSMDGA linkage detection algorithm and present a new system that can determine the required nonlinearity linkage in observed data samples for financial time series. The new system has been evaluated on synthetic datasets and experimental results are provided. The sensitivity of the system to changes in both the problem and the algorithm parameters has also been explored and we discuss the results, We present, evidence of the success of the new system and identify areas for further work.

Type: Proceedings paper
Title: Nonlinearity Linkage Detection for Financial Time Series Analysis
Event: Annual Conference of Genetic and Evolutionary Computation Conference
Location: London, ENGLAND
Dates: 2007-07-07 - 2007-07-11
ISBN-13: 978-1-59593-697-4
Keywords: Genetic Algorithms, Linkage Learning, Epistasis, Composition, Perturbation, Hierarchical, Time Series, Finance
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/124589
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