Joanne (Juan)  Chen 

Working Papers

Optimal Managerial Authority  ( Revise and Resubmit, Journal of Finance)

• 2022 Best Paper in Finance Theory on the Job Market, Runner-up, awarded by the Finance Theory Group

• 2023 Yihong Xia Best Paper Award at CICF

Abstract: This paper formulates a dynamic agency framework to explore optimal managerial authority and compensation over a manager's career in a firm: Initially, authority is assigned independently of the manager's outside options or recruitment costs, unlike compensation, which increases in both. Over time, managerial authority and compensation rise after good performance and decline otherwise.  Authority-performance sensitivity decreases with increased authority — reflecting rational entrenchment — while pay-performance sensitivity grows. Moreover, costly authority adjustment causes inefficient self-dealing, and early-career luck followed by late-career misfortune can lead to massive self-dealing; this analysis underscores early-career luck's disproportionate impact on a manager's authority and lifetime utility.

Presentations: EFA 2023, CICF 2023, WFA 2023, Paris December 2022, BU Economics Micro Theory workshop 2022, Finance Theory Webinar 2022, NFA 2022, Cambridge Corporate Finance Theory Symposium 2022, Finance Theory Group Summer Meeting 2022,  SFS Cavalcade North America 2022, FMCG PhD Symposium, Queen Mary University of London, Northwestern University (Kellogg), UNC-Chapel Hill (Kenan-Flagler), Erasmus University Rotterdam, Boston University (Questrom), UT Dallas (Naveen Jindal), Warwick Business School, Copenhagen Business School, Shanghai Advanced Institute of Finance, University of Amsterdam, EWMES 2021, Toulouse School of Economics Brownbag, EFA Doctoral Tutorial 2021, World Finance Conference 2021, London School of Economics

Can Corporate AI Adoption Backfire?  with Brandon Han 

Abstract: Firms are increasingly adopting predictive artificial intelligence (AI) to improve decision-making by combining advanced data analysis with human judgment. While AI enhances managers' ability to predict project success probabilities, we show that its adoption can unintentionally backfire, leading to suboptimal decisions that diminish shareholder profits. Specifically, the additional information provided by AI raises the likelihood that the manager's estimate of the project's success probability falls within a range where the sensitivity of managerial compensation to their decision decreases significantly, incentivizing the pursuit of private benefits over shareholder interests. To address this, we propose AI-contingent contracts that align managerial compensation with AI predictions, thereby mitigating AI-induced agency conflicts.  Our study highlights the necessity of updated corporate governance structures to effectively navigate the challenges posed by AI-augmented decision-making.

Presentations: FTG Meeting in Hong Kong (Scheduled), BU Economics Micro Theory Workshop 2024, BU Questrom Brownbag 2024