Why you should not use the LSV herding measure

Staff working papers set out research in progress by our staff, with the aim of encouraging comments and debate.
Published on 07 January 2022

Staff Working Paper No. 959

By Simon Jurkatis

Here are three reasons. (a) This paper proves that the popular investor-level herding measure is a biased estimator of herding. Monte Carlo simulations demonstrate that the measure underestimates herding by 20% to 100% of the estimation target. (b) The bias varies with the number of traders active in an asset such that regression type analyses using LSV to understand the causes and consequences of herding are likely to yield inconsistent estimates if controls are not carefully chosen. (c) The measure should be understood purely as a test on binomial overdispersion. However, alternative tests have superior size and power properties.

Why you should not use the LSV herding measure

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