Publication Type
Journal Article
Version
submittedVersion
Publication Date
12-2022
Abstract
We study a dynamic stopping game between a principal and an agent. The agent is privately informed about his type. The principal learns about the agent’s type from a noisy performance measure, which can be manipulated by the agent via a costly and hidden action. We fully characterize the unique Markov equilibrium of this game. We find that terminations/ market crashes are often preceded by a spike in (expected) performance. Our model also predicts that, due to endogenous signal manipulation, too much transparency can inhibit learning. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.
Keywords
Asymmetric information, learning, signal manipulation, venture capital
Discipline
Finance | Finance and Financial Management
Research Areas
Finance
Publication
American Economic Review
Volume
112
Issue
12
First Page
3995
Last Page
4040
ISSN
0002-8282
Identifier
10.1257/aer.20211158
Publisher
American Economic Association
Citation
EKMEKCI, Mehmet; GORNO, Leandrro; MAESTRI, Lucas; SUN, Jian; and WEI, Dong.
Learning from manipulable signals. (2022). American Economic Review. 112, (12), 3995-4040.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7105
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1257/aer.20211158