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The probability of backtest overfitting.

Bailey, David H. and Borwein, Jonathan M. and de Pardo, Lopez M. and Zhu, Qiji J. The probability of backtest overfitting.

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    Abstract

    Many investment firms and portfolio managers rely on backtests (i.e., simulations of performance based on historical market data) to select investment strategies and allocate capital. Standard statistical techniques designed to prevent regression overfitting, such as hold- out, tend to be unreliable and inaccurate in the context of investment backtests. We propose a general framework to assess the probabil- ity of backtest overfitting (PBO). We illustrate this framework with specific generic, model-free and nonparametric implementations in the context of investment simulations, which implementations we call com- binatorially symmetric cross-validation (CSCV). We show that CSCV produces reasonable estimates of PBO for several useful examples.

    Item Type: Article
    Subjects: UNSPECIFIED
    Faculty: UNSPECIFIED
    Depositing User: Mrs Naghmana Tehseen
    Date Deposited: 28 Mar 2016 13:11
    Last Modified: 28 Mar 2016 13:11
    URI: https://docserver.carma.newcastle.edu.au/id/eprint/1698

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