Over the last twenty-five years, there has been a lot of interest in herd behavior in financial markets—that is, a trader’s decision to disregard her private information to follow the behavior of the crowd. A large theoretical literature has identified abstract mechanisms through which herding can arise, even in a world where people are fully rational. Until now, however, the empirical work on herding has been completely disconnected from this theoretical analysis; it simply looked for statistical evidence of trade clustering and, when that evidence was present, interpreted the clustering as herd behavior. However, since decision clustering may be the result of something other than herding—such as the common reaction to public announcements—the existing empirical literature cannot distinguish “spurious” herding from “true” herd behavior.

     In this post, we describe a novel approach to measuring herding in financial markets, which we employed in a recently published paper. We develop a theoretical model of herd behavior that, in contrast to the existing theoretical literature, can be brought to the data, and we show how to estimate it using financial markets transaction data. The estimation strategy allows us to distinguish “real” herding from “spurious herding,” or the simple clustering of trading behavior. Our approach allows researchers to gauge the importance of herding in a financial market and to assess the inefficiency in the process of price discovery that herding causes.

The Model
Let’s give an overview of the model that we brought to the data and try to explain why herding would arise. In the model, an asset is traded over many days; at the beginning of each day, an event may occur that changes the fundamental value of the asset. If an event occurs, some traders (informed traders) receive (private) information on the new asset value; although this information may be imprecise, these traders do know that something occurred in the market to alter the value of the asset. The other traders in the market trade for reasons not related to information, such as liquidity or hedging motives. If no event occurs, all traders only trade for non-informational reasons. (more…)