Bailey, David H.
[Verfasser:in]
;
Borwein, Jonathan
[Sonstige Person, Familie und Körperschaft];
Lopez de Prado, Marcos
[Sonstige Person, Familie und Körperschaft];
Zhu, Qiji Jim
[Sonstige Person, Familie und Körperschaft]
Anmerkungen:
In: Journal of Computational Finance (Risk Journals), 2015, Forthcoming
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 27, 2015 erstellt
Beschreibung:
Most firms and portfolio managers rely on backtests (or historical simulations of performance) to select investment strategies and allocate them capital. Standard statistical techniques designed to prevent regression over-fitting, such as hold-out, tend to be unreliable and inaccurate in the context of investment backtests. We propose a framework that estimates the probability of backtest over-fitting (PBO) specifically in the context of investment simulations, through a numerical method that we call combinatorially symmetric cross-validation (CSCV). We show that CSCV produces accurate estimates of the probability that a particular backtest is over-fit.The appendices for this paper are available at the following URL: "http://ssrn.com/abstract=2568435" http://ssrn.com/abstract=2568435