Implicit incentives for fund managers with partial information

Flavio Angelini (), Katia Colaneri (), Stefano Herzel and Marco Nicolosi
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Flavio Angelini: University of Perugia
Katia Colaneri: University of Rome, Tor Vergata

Computational Management Science, 2021, vol. 18, issue 4, No 5, 539-561

Abstract: 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. 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. Therefore, we solve the problem by combining the martingale method and a concavification procedure and we obtain the optimal wealth and the investment strategy. A numerical example shows the effect of learning on the optimal strategy.

Keywords: Portfolio management; Optimal control; Learning (search for similar items in EconPapers)
Date: 2021
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Working Paper: Implicit Incentives for Fund Managers with Partial Information (2020)
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DOI: 10.1007/s10287-021-00404-w

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