Portfolio Optimization under Partial Information with Expert Opinions: a Dynamic Programming Approach

TL;DR

This paper develops a dynamic programming approach to optimize portfolio strategies in markets with unobserved states, incorporating stock data and expert signals to improve decision-making under partial information.

Contribution

It introduces a novel method combining stochastic filtering and viscosity solutions to solve portfolio optimization with expert opinions and partial information.

Findings

Effective filtering of unobserved market states.

Enhanced portfolio strategies using expert signals.

Application of viscosity solutions to dynamic programming.

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.