Dynamic Agency and Investment Theory under Model Uncertainty

• Published date: 01 June 2019
• Author: Zhentao Zou,Yingjie Niu,Jinqiang Yang
• DOI: http://doi.org/10.1111/irfi.12170
• Date: 01 June 2019

Dynamic Agency and Investment

Theory under Model Uncertainty*

YINGJIE NIU

†

,JINQIANG YANG

‡

AND ZHENTAO ZOU

†

†

School of Finance, Shanghai University of Finance and Economics, Shanghai,

China and

‡

Shanghai Key Laboratory of Financial Information Technology, School of Finance,

Shanghai University of Finance and Economics, Shanghai, China

ABSTRACT

We extend dynamic agency and investment theory by incorporating model

uncertainty. As concerns regarding model uncertainty induce a trade-off

between incentives and ambiguity sharing, the principal tends to delay the

cash payout to the agent. We find model uncertainty lowers the firm value,

the average qand marginal q, where qis defined as the ratio between a physi-

cal asset’s market value and its replacement value. Furthermore, model

uncertainty leads to insufficient investment, which provides an alternative

explanation for under-investment. Finally, the optimal pay-performance sen-

sitivity of the agent’s continuation value to the firm’s output is state depen-

dent and exceeds the lower bound when it is close to the payout boundary.

JEL Codes: D81; E22; G30

Accepted: 1 December 2017

I. INTRODUCTION

Many studies related to the principal-agent problem assume that both the prin-

cipal and the agent share the same belief about the uncertainty underlying the

outcome. However, there exist two good reasons for us to think about depar-

tures from this assumption. First, the Ellsberg (1961) paradox and related exper-

imental evidence demonstrate that risk and uncertainty are fundamentally

different. Risk refers to the case where the probability distribution over the state

of the world is known, while ambiguity refers to the situation where the distri-

bution itself may be unknown. Second, economic agents believe that the

observed economic data come from a set of unspecified models (Hansen and

* Jinqiang Yang acknowledges the support from the National Natural Science Foundation of China

(Nos. 71522008 and 71772112), Innovative Research Team of Shanghai University of Finance and

Economics (No. 2016110241), and Fok Ying-Tong Education Foundation of China (No. 151086).

Zhentao Zou is supported in part by the National Natural Science Foundation of China (NSFC):

71401095/G0115.

© 2018 International Review of Finance Ltd. 2018

International Review of Finance, 19:2, 2019: pp. 447–458

DOI: 10.1111/irfi.12170