Expert Opinions and Logarithmic Utility Maximization for Multivariate Stock Returns with Gaussian Drift
J\"orn Sass, Dorothee Westphal, Ralf Wunderlich

TL;DR
This paper develops a framework for optimal trading strategies in multivariate stock markets with unobservable drifts, incorporating expert opinions and filtering techniques to maximize expected logarithmic utility.
Contribution
It introduces a comprehensive analysis of filtering equations and covariance matrix properties for optimal trading with expert opinions in a multivariate setting.
Findings
Asymptotic behavior of covariance matrices with increasing expert opinions.
Conditions for convergence of covariance matrices over infinite horizon.
Derived optimal trading strategies based on the filter and covariance analysis.
Abstract
This paper investigates optimal trading strategies in a financial market with multidimensional stock returns where the drift is an unobservable multivariate Ornstein-Uhlenbeck process. Information about the drift is obtained by observing stock returns and expert opinions. The latter provide unbiased estimates on the current state of the drift at discrete points in time. The optimal trading strategy of investors maximizing expected logarithmic utility of terminal wealth depends on the filter which is the conditional expectation of the drift given the available information. We state filtering equations to describe its dynamics for different information settings. Between expert opinions this is the Kalman filter. The conditional covariance matrices of the filter follow ordinary differential equations of Riccati type. We rely on basic theory about matrix Riccati equations to investigate…
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