ROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY

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dc.contributor.authorSchmidbauer, Harald
dc.contributor.authorRoesch, Angi
dc.contributor.authorSezer, Tolga
dc.contributor.authorTunalioglu, Vehbi Sinan
dc.date.accessioned2024-07-18T20:42:18Z
dc.date.available2024-07-18T20:42:18Z
dc.date.issued2014
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description1st International Conference on Forecasting Economic and Financial Systems (FEFS) / 5th International Workshop on Singular Spectrum Analysis and its Applications (SSA) -- MAY 17-20, 2012 -- Beijing, PEOPLES R CHINAen_US
dc.description.abstractTrading rules performing well on a given data set seldom lead to promising out-of-sample results, a problem which is a consequence of the in-sample data snooping bias. Efforts to justify the selection of trading rules by assessing the out-of-sample performance will not really remedy this predicament either, because they are prone to be trapped in what is known as the out-of-sample data-snooping bias. Our approach to curb the data-snooping bias consists of constructing a framework for trading rule selection using a-priori robustness strategies, where robustness is gauged on the basis of time-series bootstrap and multi-objective criteria. This approach focuses thus on building robustness into the process of trading rule selection at an early stage, rather than on an ex-post assessment of trading rule fitness. Intra-day FX market data constitute the empirical basis of the proposed investigations. Trading rules are selected from a wide universe created by evolutionary computation tools. The authors show evidence of the benefit of this approach in terms of indirect forecasting accuracy when investing in FX markets.en_US
dc.identifier.doi10.1007/s11424-014-3302-7
dc.identifier.endpage180en_US
dc.identifier.issn1009-6124
dc.identifier.issn1559-7067
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-84893549327en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage169en_US
dc.identifier.urihttps://doi.org/10.1007/s11424-014-3302-7
dc.identifier.urihttps://hdl.handle.net/11411/7237
dc.identifier.volume27en_US
dc.identifier.wosWOS:000331200200013en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal of Systems Science & Complexityen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectA-Priori Robustnessen_US
dc.subjectData-Snooping Biasen_US
dc.subjectEfficient Market Hypothesisen_US
dc.subjectEvolutionary Computationen_US
dc.subjectİntra-Day Fx Marketsen_US
dc.subjectTime-Series Bootstrapen_US
dc.subjectTrading Rule Selectionen_US
dc.titleROBUST TRADING RULE SELECTION AND FORECASTING ACCURACYen_US
dc.typeConference Objecten_US

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