Open AccessHow Should You Discount Your Backtest PnL?
Author(s) -
Rej Adam,
Seager Philip,
Bouchaud JeanPhilippe
Publication year - 2019
Publication title -
wilmott
Language(s) - English
Resource type - Journals
eISSN - 1541-8286
pISSN - 1540-6962
DOI - 10.1002/wilm.10793
Subject(s) - overfitting , discounting , sample (material) , investment strategy , investment (military) , computer science , simple (philosophy) , econometrics , economics , artificial intelligence , finance , artificial neural network , philosophy , chemistry , chromatography , epistemology , politics , market liquidity , political science , law
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 article 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.