Causality detection on US mutual fund movements using evolutionary subset time-series

Tim J. Brailsford, Terry J. O'Neill, Jack Penm

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

In 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.

Original languageEnglish
Pages (from-to)368-384
Number of pages17
JournalInternational Journal of Services and Standards
Volume2
Issue number4
DOIs
Publication statusPublished - 2006
Externally publishedYes

Fingerprint

Time series
Set theory
Evolutionary algorithms
Financial markets
Mutual funds
Evolutionary
Causality
Bond market
Kernel
Survivors
Data base
Lag
Stock market
Factors
Forgetting

Cite this

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Causality detection on US mutual fund movements using evolutionary subset time-series. / Brailsford, Tim J.; O'Neill, Terry J.; Penm, Jack.

In: International Journal of Services and Standards, Vol. 2, No. 4, 2006, p. 368-384.

Research output: Contribution to journalArticleResearchpeer-review

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