| Contents |
| \begin{abstract}
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.
\end{abstract} |
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