Journal of Systems Science and Complexity

, Volume 27, Issue 1, pp 169–180 | Cite as

Robust trading rule selection and forecasting accuracy

  • Harald Schmidbauer
  • Angi Rösch
  • Tolga Sezer
  • Vehbi Sinan Tunalioğlu


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.


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


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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Harald Schmidbauer
    • 1
  • Angi Rösch
    • 2
  • Tolga Sezer
    • 3
  • Vehbi Sinan Tunalioğlu
    • 3
  1. 1.Department of Business AdministrationIstanbul Bilgi University, Santral CampusEyüp İstanbulTurkey
  2. 2.FOM University of Applied SciencesStudy Centre MunichMünchenGermany
  3. 3.DIMEUniversity of GenoaGenoaItaly

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