  We introduce and solve a model that mimics the herding effect in financial
markets when groups of agents share information. The number of agents in the
model is growing and at each time step either (i) with probability $p$ an
incoming agent joins an existing group, or (ii) with probability $1-p$ a group
is fragmented into individual agents. The group size distribution is found to
be power-law with an exponent that depends continuously on $p$. A number of
variants of our basic model are discussed. Comparisons are made between these
models and other models of herding and random growing networks.
