Before revision:  The LSM estimator contains  look-ahead bias, and the conventional technique of removing it necessitates an independent set of simulations. This study proposes a new approach for efficiently eliminating look-ahead bias by using the leave-one-out method , a well-known cross-validation technique for machine learning applications.
After revision:  The LSM estimator contains look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the leave-one-out LSM (LOOLSM) algorithm for efficiently eliminating look-ahead bias by using the leave-one-out method , a well-known cross-validation technique for machine learning applications.