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Abstract The out-of-sample $$R^2$$ R 2 is designed to measure forecasting performance without look-ahead bias. However, researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample. Using ensemble machine learning techniques, we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations. We apply this approach to robust monitoring, exploiting a dynamic shrinkage effect by switching between a proposed forecast and a benchmark. Considering stock return forecasting as an example, we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15% (in terms of mean-squared-error) and reduces the variance of its relative performance by 46% while avoiding the out-of-sample $$R^2$$ R 2 -hacking problem. Our approach, as a final touch, can further enhance the performance and stability of forecasts from any models and methods.