Portfolio Optimization under Partial Information with Expert Opinions

In this talk we study portfolio optimization  in a market with partial information on the drift. The drift is modelled as a function of  a continuous-time Markov chain with finitely many states which is not directly observable. Information on the drift is obtained from  the observation of stock prices. Moreover, expert opinions in the form of signals at random discrete time points  are included in the analysis. We derive the filtering equation for the return process and incorporate the filter into the state variables of the optimization problem. This problem is studied with dynamic programming methods. In particular, we use a regularization method to compute approximately optimal strategies. Numerical results are presented at the end.
(based on joint work with R. Wunderlich, Cottbus)