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dc.contributor.authorAVDOULAS, Christos
dc.contributor.authorBEKIROS, Stelios D.
dc.contributor.authorBOUBAKER, Sabri
dc.date.accessioned2016-03-18T14:32:07Z
dc.date.available2016-03-18T14:32:07Z
dc.date.issued2018
dc.identifier.citationAnnals of operations research, 2018, Vol. 262, No. 2, pp. 307–333en
dc.identifier.issn0254-5330
dc.identifier.issn1572-9338
dc.identifier.urihttps://hdl.handle.net/1814/40377
dc.descriptionPublished online 10 December 2015.en
dc.description.abstractTraditional linear regression and time-series models often fail to produce accurate forecasts due to inherent nonlinearities and structural instabilities, which characterize financial markets and challenge the Efficient Market Hypothesis. Machine learning techniques are becoming widespread tools for return forecasting as they are capable of dealing efficiently with nonlinear modeling. An evolutionary programming approach based on genetic algorithms is introduced in order to estimate and fine-tune the parameters of the STAR-class models, as opposed to conventional techniques. Using a hybrid method we employ trading rules that generate excess returns for the Eurozone southern periphery stock markets, over a long out-of-sample period after the introduction of the Euro common currency. Our results may have important implications for market efficiency and predictability. Investment or trading strategies based on the proposed approach may allow market agents to earn higher returns.en
dc.language.isoenen
dc.publisherSpringer (part of Springer Nature)en
dc.relation.ispartofAnnals of operations researchen
dc.titleEvolutionary-based return forecasting with nonlinear STAR models : evidence from the Eurozone peripheral stock marketsen
dc.typeArticleen
dc.identifier.doi10.1007/s10479-015-2078-z
dc.identifier.volume262
dc.identifier.startpage307
dc.identifier.endpage333
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dc.identifier.issue2


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