Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling

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dc.contributor.author He, Xuezhong en_US
dc.contributor.author Hamill, Philip en_US
dc.contributor.author Li, You Wei en_US
dc.contributor.editor Brabazon, A; O'Neill, M en_US
dc.date.accessioned 2010-05-28T09:39:48Z
dc.date.available 2010-05-28T09:39:48Z
dc.date.issued 2008 en_US
dc.identifier 2007004855 en_US
dc.identifier.citation He Xuezhong, Hamill Philip, and Li You Wei 2008, 'Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling', in NA (ed.), Springer-Verlag, Berlin, Germany, pp. 253-269. en_US
dc.identifier.issn 9783540774761 en_US
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/8015
dc.description.abstract This chapter uses a simple stochastic market fraction (MF) asset pricing model to investigate market dominance, profitability, and how traders adopting fundamental analysis or trend following strategies can survive under various market conditions in the long/shoft-run. This contrasts with the modern theory of finance which relies on the paradigm of utility maximizing representative agents and rational expectations assumptions which some contemporary theorists regard as extreme. This school of thought would predict that trend followers will be driven out of the markets in the long-run. Our analysis shows that in a MF framework this is not necessarily the case and that trend followers can survive in the long-run. en_US
dc.language en_US
dc.publisher Springer en_US
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon NA en_US
dc.rights The original publication is available at www.springerlink.com en_US
dc.title Can Trend Followers Survive in the Long-Run? Insights from Agent-Based Modeling en_US
dc.parent Natural Computing in Computational Finance en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 253 en_US
dc.identifier.endpage 269 en_US
dc.cauo.name BUS.School of Finance and Economics en_US
dc.conference Verified OK en_US
dc.for 150201 en_US
dc.personcode 010238 en_US
dc.personcode 0000046372 en_US
dc.personcode 0000026056 en_US
dc.percentage 80 en_US
dc.classification.name Finance en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US


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