  This paper investigates a financial market where stock returns depend on a
hidden Gaussian mean reverting drift process. Information on the drift is
obtained from returns and expert opinions in the form of noisy signals about
the current state of the drift arriving at the jump times of a homogeneous
Poisson process. Drift estimates are based on Kalman filter techniques and
described by the conditional mean and covariance matrix of the drift given the
observations. We study the filter asymptotics for increasing arrival intensity
of expert opinions and prove that the conditional mean is a consistent drift
estimator, it converges in the mean-square sense to the hidden drift. Thus, in
the limit as the arrival intensity goes to infinity investors have full
information about the drift.
