  This paper investigates optimal portfolio strategies in a market where the
drift is driven by an unobserved Markov chain. Information on the state of this
chain is obtained from stock prices and expert opinions in the form of signals
at random discrete time points. As in Frey et al. (2012), Int. J. Theor. Appl.
Finance, 15, No. 1, we use stochastic filtering to transform the original
problem into an optimization problem under full information where the state
variable is the filter for the Markov chain. The dynamic programming equation
for this problem is studied with viscosity-solution techniques and with
regularization arguments.
