Portfolio optimization under partial information with expert opinions: a dynamic programming approach

We study 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 from expert

opinions in the form of signals at random discrete time points. 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. This problem is studied with dynamic

programming techniques and with regularization arguments. Finally we

discuss a number of numerical experiments