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

Authors: T.J. Brailsford, T.J. O'Neill, J. Penm

Addresses: UQ Business School, University of Queensland, Australia. ' School of Finance and Applied Statistics, The Australian National University, Canberra, Australia. ' School of Finance and Applied Statistics, The Australian National University, Canberra, Australia

Abstract: In this paper we develop an evolutionary kernel-based time update algorithm to recursively estimate subset discrete lag models (including fullorder models) with a forgetting factor and a constant term, using the exactwindowed 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.

Keywords: causality detection; evolutionary algorithms; time series modelling; financial modelling; mutual funds.

DOI: 10.1504/IJSS.2006.010470

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

Published online: 24 Jul 2006 *

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