Causality Detection on US Mutual Fund Movements using Evolutionary Subset Time-Series

dc.contributor.authorBrailsford, Timothy John
dc.contributor.authorO'Neill, Terence
dc.contributor.authorPenm, Jack HW
dc.date.accessioned2015-12-07T22:19:25Z
dc.date.issued2006
dc.date.updated2015-12-07T08:35:25Z
dc.description.abstractIn this paper we develop an evolutionary kernel-based time update algorithm to recursively estimate subset discrete lag models (including full-order models) with a forgetting factor and a constant term, using the exact-windowed case. The algorithm applies to causality detection when the true relationship occurs with a continuous or a random delay. We then demonstrate the use of the proposed evolutionary algorithm to study the monthly mutual fund data, which come from the 'CRSP Survivor-bias free US Mutual Fund Database'. The results show that the NAV is an influential player on the international stage of global bond and stock markets.
dc.identifier.issn1740-8849
dc.identifier.urihttp://hdl.handle.net/1885/19330
dc.publisherInderscience Publishers
dc.sourceInternational Journal of Services and Standards
dc.subjectKeywords: Causality detection; Evolutionary algorithms
dc.titleCausality Detection on US Mutual Fund Movements using Evolutionary Subset Time-Series
dc.typeJournal article
local.bibliographicCitation.issue4
local.bibliographicCitation.lastpage384
local.bibliographicCitation.startpage368
local.contributor.affiliationBrailsford, Timothy John, University of Queensland
local.contributor.affiliationO'Neill, Terence, College of Business and Economics, ANU
local.contributor.affiliationPenm, Jack HW, College of Business and Economics, ANU
local.contributor.authoremailu7601382@anu.edu.au
local.contributor.authoruidO'Neill, Terence, u7601382
local.contributor.authoruidPenm, Jack HW, u7800853
local.description.notesImported from ARIES
local.identifier.absfor010401 - Applied Statistics
local.identifier.ariespublicationu8902633xPUB7
local.identifier.citationvolume2
local.identifier.doi10.1504/IJSS.2006.010470
local.identifier.scopusID2-s2.0-33751109237
local.identifier.uidSubmittedByu8902633
local.type.statusPublished Version

Downloads