Portfolio Optimization under Partial Information with Expert Opinions: a Dynamic Programming Approach
Portfolio Management
2016-02-03 v2
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.
Keywords
Cite
@article{arxiv.1303.2513,
title = {Portfolio Optimization under Partial Information with Expert Opinions: a Dynamic Programming Approach},
author = {Rüdiger Frey and Abdelali Gabih and Ralf Wunderlich},
journal= {arXiv preprint arXiv:1303.2513},
year = {2016}
}
Comments
31 pages