Implicit Incentives for Fund Managers with Partial Information

TL;DR

This paper investigates how fund managers optimize asset allocation under partial information about market risk, considering performance-based incentives, and demonstrates how learning influences optimal strategies through a martingale approach.

Contribution

It introduces a novel framework for optimal asset allocation with partial information and incentive effects, solved via martingale methods and concavification.

Findings

Learning improves the accuracy of risk estimates.

Optimal strategies are affected by the information available.

Numerical results illustrate the impact of learning on investment decisions.

Abstract

We study the optimal asset allocation problem for a fund manager whose compensation depends on the performance of her portfolio with respect to a benchmark. The objective of the manager is to maximise the expected utility of her final wealth. The manager observes the prices but not the values of the market price of risk that drives the expected returns. The estimates of the market price of risk get more precise as more observations are available. We formulate the problem as an optimization under partial information. The particular structure of the incentives makes the objective function not concave. We solve the problem via the martingale method and, with a concavification procedure, we obtain the optimal wealth and the investment strategy. A numerical example shows the effect of learning on the optimal strategy.