This note investigates via Monte Carlo simulation the finite‐sample performance of two identification schemes that provide unique measures of Hasbrouck‐type information share in price discovery. The Lien and Shrestha (2009) method is based on factorization of the full correlation matrix and the Grammig and Peter (2013) method is based on different correlations of price innovations in the tails and in the center of the distributions. We find that the GP method performs poorly under the chosen data generation processes. The LS method provides at most marginal improvement over the method based upon the upper/lower bound midpoint of the Hasbrouck measure. The results, therefore, support the common practice of the midpoint approach. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 36:1108–1124, 2016