Robust trading rule selection and forecasting accuracy


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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Correspondence to Harald Schmidbauer.

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Schmidbauer, H., Rösch, A., Sezer, T. et al. Robust trading rule selection and forecasting accuracy. J Syst Sci Complex 27, 169–180 (2014).

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  • A-priori robustness
  • data-snooping bias
  • efficient market hypothesis
  • evolutionary computation
  • intra-day FX markets
  • time-series bootstrap
  • trading rule selection