Schmidbauer, HaraldRoesch, AngiSezer, TolgaTunalioglu, Vehbi Sinan2024-07-182024-07-1820141009-61241559-7067https://doi.org/10.1007/s11424-014-3302-7https://hdl.handle.net/11411/72371st 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 CHINATrading 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.eninfo:eu-repo/semantics/closedAccessA-Priori RobustnessData-Snooping BiasEfficient Market HypothesisEvolutionary Computationİntra-Day Fx MarketsTime-Series BootstrapTrading Rule SelectionConference Object2-s2.0-8489354932710.1007/s11424-014-3302-71801Q216927Q4WOS:000331200200013