Errors in Trade Classification: Consequences and Remedies

The consequences of errors in trade classification are potentially worse than documented in existing empirical research. This is demonstrated by the use of a formal model of classification errors in a generic regression-type microstructure model. The bias is a function of the probability of trade-reversal in addition to the probability of an error. These parameters depend on stock and trade characteristics in addition to trading procedures and trade reporting standards. The bias is highly sensitive to the background variables, thus causing concern about the validity of empirical studies applying possibly erroneous classification methods without controlling for such effects. The theory, outlined in the paper, predicts that given empirical evidence on error rates, effective spreads must realistically be expected to be downward biased by more than 50%. However, the bias one can observe from using the TORQ database is less severe and has the opposite sign. This is due to special features of the NYSE trading process which may not carry over to other markets. This research also emphasizes the need for proper adjustment of classification error bias. Therefore, the paper proposes a GMM estimator for improved estimation. Simulation evidence indicates that in medium and larger sized samples the method is capable of removing virtually all the bias in market quality statistics like e¤ective, realized, and adverse selection spreads. This is empirically verified in an application to data from TORQ.