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https://hdl.handle.net/2440/62290
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dc.contributor.author | Ghandar, A. | - |
dc.contributor.author | Michalewicz, Z. | - |
dc.contributor.author | Zurbrugg, R. | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | Proceedings of the 2010 IEEE Congress on Evolutionary Computation, held in Barcelona, Spain, July 18-23 2010: pp.1-8 | - |
dc.identifier.isbn | 9781424481262 | - |
dc.identifier.uri | http://hdl.handle.net/2440/62290 | - |
dc.description.abstract | This 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. | - |
dc.description.statementofresponsibility | Adam Ghandar, Zbigniew Michalewicz and Ralf Zurbruegg | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.relation.ispartofseries | IEEE Congress on Evolutionary Computation | - |
dc.rights | ©2010 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/cec.2010.5586198 | - |
dc.title | Interpretable multi-criteria fuzzy rule based decision models for hedge fund management | - |
dc.type | Conference paper | - |
dc.contributor.conference | Congress on Evolutionary Computation (2010 : Barcelona, Spain) | - |
dc.identifier.doi | 10.1109/CEC.2010.5586198 | - |
dc.publisher.place | USA | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP1096053 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP1096053 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Zurbrugg, R. [0000-0002-8652-0028] | - |
Appears in Collections: | Aurora harvest Business School publications |
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