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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Schmidbauer, H., Rösch, A., Sezer, T. et al. Robust trading rule selection and forecasting accuracy. J Syst Sci Complex 27, 169–180 (2014). https://doi.org/10.1007/s11424-014-3302-7
- A-priori robustness
- data-snooping bias
- efficient market hypothesis
- evolutionary computation
- intra-day FX markets
- time-series bootstrap
- trading rule selection