Robust trading rule selection and forecasting accuracy |
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Authors: | Harald Schmidbauer Angi Rösch Tolga Sezer Vehbi Sinan Tunalio?lu |
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Institution: | [1]Department of Business Administration, Istanbul Bilgi University, Santral Campus, 34060 Eyiip Istanbul,Turkey. [2]FOM University of Applied Scienees, Study Centre Munich, Arnulfstr. 30, 80335 Miinchen, Germany. [3]DIME, University of Genoa, Via Opera Pia 15, 16145 Genoa, Italy. |
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Abstract: | 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 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. |
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Keywords: | 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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