Prospect theory-based portfolio optimisation: An empirical study and analysis using intelligent algorithms

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/12131

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DC Field	Value	Language
dc.contributor.author	Grishina, N	-
dc.contributor.author	Lucas, CA	-
dc.contributor.author	Date, P	-
dc.date.accessioned	2016-02-18T13:07:42Z	-
dc.date.available	2016-02-18T13:07:42Z	-
dc.date.issued	2016	-
dc.identifier.citation	N. Grishina, C. A. Lucas & P. Date (2017) Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms, Quantitative Finance, 17:3, 353-367, DOI: 10.1080/14697688.2016.1149611	en_US
dc.identifier.issn	1469-7696	-
dc.identifier.uri	https://www.tandfonline.com/doi/full/10.1080/14697688.2016.1149611	-
dc.identifier.uri	https://bura.brunel.ac.uk/handle/2438/12131	-
dc.description.sponsorship	The first author was part funded by RFBR (grant 14-01-00140).	en_US
dc.language.iso	en	en_US
dc.publisher	Taylor & Francis (Routledge)	en_US
dc.subject	portfolio optimisation	en_US
dc.subject	behavioural nuance	en_US
dc.subject	prospect theory	en_US
dc.subject	index tracking	en_US
dc.subject	risk modelling	en_US
dc.title	Prospect theory-based portfolio optimisation: An empirical study and analysis using intelligent algorithms	en_US
dc.type	Article	en_US
dc.identifier.doi	https://doi.org/10.1080/14697688.2016.1149611	-
dc.relation.isPartOf	Quantitative Finance	-
pubs.publication-status	Published	-
Appears in Collections:	Dept of Mathematics Research Papers

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