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In-sample overfitting is a drawback of any backtest-based investment strategy. It is thus of paramount importance to have an understanding of why and how the in-sample overfitting occurs. In this talk we propose a simple framework that allows one to model and quantify in-sample PnL overfitting. This allows us to compute the factor appropriate for discounting PnLs of in-sample investment strategies.

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