Estimating the Level of, and Correcting for, Snooping Bias

Is there a tractable way of estimating the level of data snooping bias in investment strategy studies and thereby correcting for it? In their April 2018 paper entitled “Detection of False Investment Strategies Using Unsupervised Learning Methods”, Marcos Lopez de Prado and Michael Lewis summarize and validate an approach for estimating snooping bias derived from backtesting multiple strategies on the same data and using that estimate to correct for the bias. The approach involves estimating the overall scope and dispersion of multiple backtests based on correlation clusters within known backtests. Focusing on Sharpe ratio as the key performance metric, they validate their approach via Monte Carlo simulations. Based on derivations and simulations, they conclude that: (more…)