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dc.contributor.authorGhandar, A.en
dc.contributor.authorMichalewicz, Z.en
dc.contributor.authorZurbrugg, R.en
dc.identifier.citationProceedings of the 2010 IEEE Congress on Evolutionary Computation, held in Barcelona, Spain, July 18-23 2010: pp.1-8en
dc.description.abstractThis paper describes an approach to constructing fuzzy rules for predictive modeling that involves a local search heuristic and an evolutionary algorithm. This approach is applied for learning strategies to manage a portfolio that comprises positions in the share market. We provide experimental results comparing the approach to random strategies and the market index. A non-linear prediction model that relates asset performance to a large set of explanatory variables is represented with fuzzy rules. Rulebases are combined to build multi-criteria recommendations for trading decisions that consider different forecast horizons and both risk and return criteria.en
dc.description.statementofresponsibilityAdam Ghandar, Zbigniew Michalewicz and Ralf Zurbrueggen
dc.relation.ispartofseriesIEEE Congress on Evolutionary Computationen
dc.rights©2010 IEEEen
dc.titleInterpretable multi-criteria fuzzy rule based decision models for hedge fund managementen
dc.typeConference paperen
dc.contributor.conferenceCongress on Evolutionary Computation (2010 : Barcelona, Spain)en
dc.identifier.orcidZurbrugg, R. [0000-0002-8652-0028]en
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