Abstract
This paper investigates optimal portfolio strategies in a mar- ket where the drift is driven by an unobserved Markov chain. Information on the state of this chain is obtained from stock prices and expert opin- ions 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 ltering to transform the original problem into an optimization problem under full information where the state variable is the lter for the Markov chain. The dynamic programming equation for this problem is studied with viscosity- solution techniques and with regularization arguments.
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