Abstract - Estimating a Structural Model of Herd Behaviour in Financial Markets
We develop and estimate a structural model of informational herding in financial markets. In the model, a sequence of traders exchanges an asset with a market maker. Herd behavior, i.e., the choice to follow the actions of one’s predecessors, can arise as the outcome of a rational choice because there are multiple sources of asymmetric information in the economy. We estimate the model using transaction data on a NYSE stock in the first quarter of 1995. We are able to detect the periods of the trading day in which traders herd, and find that they account for 20% of trading periods. Moreover, we find that in more than 20% of days, herding accounts for more than 50% of all
trading activity. Finally, by simulating the model, we estimate the informational inefficiency generated by herding. On average, because of herding, the actual price is 0.4% distant from the full information price. Moreover, in 4.3% of trading periods, the distance between actual and full information prices is larger than 5%. This suggests that the informational inefficiency caused by herding, although not extremely large on average, is very significant in certain days.
University College London