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In this paper we study optimal trading strategies in a financial market inwhich stock returns depend on a hidden Gaussian mean reverting drift process.Investors obtain information on that drift by observing stock returns.Moreover, expert opinions in the form of signals about the current state of thedrift arriving at fixed and known dates are included in the analysis. Driftestimates are based on Kalman filter techniques. They are used to transform apower utility maximization problem under partial information into anoptimization problem under full information where the state variable is thefilter of the drift. The dynamic programming equation for this problem isstudied and closed-form solutions for the value function and the optimaltrading strategy of an investor are derived. They allow to quantify themonetary value of information delivered by the expert opinions. We illustrateour theoretical findings by results of extensive numerical experiments.

Power Utility Maximization with Expert Opinions at Fixed Arrival Times in a Market with Hidden Gaussian Drift