  This paper investigates the optimal selection of portfolios for power utility
maximizing investors in 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 randomly over time. The arrival dates
are modeled as the jump times of a homogeneous Poisson process. Applying Kalman
filter techniques we derive estimates of the hidden drift which are described
by the conditional mean and covariance of the drift given the observations. The
utility maximization problem is solved with dynamic programming methods. We
derive the associated dynamic programming equation and study regularization
arguments for a rigorous mathematical justification.
