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Bailey, David H. [Verfasser:in] ; Ger, Stephanie [Sonstige Person, Familie und Körperschaft]; Lopez de Prado, Marcos [Sonstige Person, Familie und Körperschaft]; Sim, Alexander [Sonstige Person, Familie und Körperschaft]; Wu, Kesheng [Sonstige Person, Familie und Körperschaft]

Statistical Overfitting and Backtest Performance

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  • Medientyp: E-Book
  • Titel: Statistical Overfitting and Backtest Performance
  • Beteiligte: Bailey, David H. [Verfasser:in]; Ger, Stephanie [Sonstige Person, Familie und Körperschaft]; Lopez de Prado, Marcos [Sonstige Person, Familie und Körperschaft]; Sim, Alexander [Sonstige Person, Familie und Körperschaft]; Wu, Kesheng [Sonstige Person, Familie und Körperschaft]
  • Erschienen: [S.l.]: SSRN, [2015]
  • Umfang: 1 Online-Ressource (10 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.2507040
  • Identifikator:
  • Entstehung:
  • Anmerkungen: In: "Risk-Based and Factor Investing", Quantitative Finance Elsevier, 2015 (Forthcoming)
    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 7, 2014 erstellt
  • Beschreibung: In the field of mathematical finance, a “backtest” is the usage of historical market data to assess the performance of a proposed trading strategy. It is a relatively simple matter for a present-day computer system to explore thousands, millions or even billions of variations of a proposed strategy, and pick the best performing variant as the “optimal” strategy “in sample” (i.e., on the input dataset). Unfortunately, such an “optimal” strategy often performs very poorly “out of sample” (i.e., on another dataset), because the parameters of the invest strategy have been overfit to the in-sample data, a situation known as “backtest overfitting”.While the mathematics of backtest overfitting has been examined in several recent theoretical studies, here we pursue a more tangible analysis of this problem, in the form of an online simulator tool. Given a input random walk time series, the tool develops an “optimal” variant of a simple strategy by exhaustively exploring all integer parameter values among a handful of parameters. That “optimal” strategy is overfit, since by definition a random walk is unpredictable. Then the tool tests the resulting “optimal” strategy on a second random walk time series. In most runs using our online tool, the “optimal” strategy derived from the first time series performs poorly on the second time series, demonstrating how hard it is not to overfit a backtest. We offer this online tool to facilitate further research in this area
  • Zugangsstatus: Freier Zugang