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ROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY

Journal of Systems Science & Complexity 2014年第1期27卷 169-180页

作者： schmidbauer harald ROSCH Angi SEZER Tolga TUNALIOGLU Vehbi Sinan Department of Business Administration Istanbul Bilgi University Santral Campus 34060 Eyiip IstanbulTurkey. FOM University of Applied Scienees Study Centre Munich Arnulfstr. 30 80335 Miinchen Germany. DIME University of Genoa Via Opera Pia 15 16145 Genoa Italy.

Trading 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 predica- ment 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.

关键词： A-priori robustness data-snooping bias efficient market hypothesis evolutionary com-putation intra-day FX markets time-series bootstrap trading rule selection.

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