About
Xiaojing Meng joined New York University Stern School of Business in July 2011 as an Associate Professor of Accounting. Her research interests include financial reporting and disclosure, corporate governance, debt contracting, and strategic information transmission. She is particularly interested in how different parties strategically communicate their information and how accounting information is used in firms' and investors' decision-making processes. Her work has been published in reputable journals such as the Journal of Accounting Research, Journal of Financial Economics, The Accounting Review, and Review of Accounting Studies. Professor Meng holds a B.A. in Economics from the Central University of Finance and Economics, an M.A. in Accounting from Beijing University, and a Ph.D. in Accounting from Columbia Business School, which she completed with distinction.
Research topics
- Computer Science
- Finance
- Economics
- Microeconomics
- Business
- Accounting
- Management
- Actuarial science
- Econometrics
Selected publications
Dynamic Voluntary Disclosure and Investment Efficiency
SSRN Electronic Journal · 2026-01-01
preprintOpen access1st authorCorrespondingIEEE Access · 2024-01-01
articleOpen accessSenior authorAttention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. Although numerous intelligent methods have been applied for its diagnosis, they seldom address symptom prediction, which is crucial for establishing the relationship between symptoms and subjective biosignals. We propose a severity prediction model, namely BSP-Net, which uses amplitude of low-frequency fluctuation (ALFF) data and constructs severity predictors within a binary hypothesis testing (BHT) framework. Specifically, we designed a dual-branch network for symptom severity prediction, with each branch corresponding to an assumed label for the test subject. Building on the accurate ADHD identification achieved by the existing auto-encoding network (AENet) within the BHT framework, we integrate its network and introduce symptom score predictors in each branch. By comparing high-level features from both branches using the AENet, we derive an estimated label. Once an assumed label is confirmed, the corresponding branch’s score predictor is activated to generate the final symptom assessment. Using the BSP-Net, our experiments achieved severity prediction accuracies of 92.4% and 84.9% on specific ADHD-200 datasets, with a score tolerance threshold of 3. Moreover, the mean squared errors on these datasets were lower than 16, significantly outperforming other methods. Importantly, discriminative brain regions corresponding to typical ALFF data in the BSP-Net were identified as ADHD biomarkers. These biomarkers align with existing research on abnormal brain regions in children with ADHD. Consequently, our method demonstrates its validity by providing biological explanations derived from ADHD mechanisms.
Information, Incentives, and CEO Replacement
The Accounting Review · 2023-10-11 · 3 citations
article1st authorCorrespondingABSTRACT There are instances where CEO turnover occurs, even if the company has not made any significant strategy changes, and the new CEO possesses similar abilities as the predecessor. This paper aims to provide a rational explanation for this seemingly irrational phenomenon. One possible reason for this “aggressive” CEO turnover is the board’s desire to reduce the information rents earned by the privately informed CEO. Specifically, the incumbent CEO has a temptation to “sandbag” the board about profitability prospects to secure more generous incentive pay for future implementation, and a (seemingly aggressive) replacement policy helps discourage this kind of gaming. That is, instead of “information-based entrenchment” as suggested by the literature (Laux 2008; Inderst and Mueller 2010), this paper shows a countervailing effect that the CEO’s private information (combined with the later-stage moral hazard problem) may lead to her dismissal more often than the ex post efficient benchmark. JEL Classifications: D86; G34; M41.
SSRN Electronic Journal · 2023-01-01
articleOpen accessSSRN Electronic Journal · 2023-01-01
articleOpen accessReview of Accounting Studies · 2021 · 13 citations
- Computer Science
- Business
- Accounting
The internal procedures of the accounting community
2021-01-01
article1st authorCorrespondingThe internal procedures of the accounting community for generating and disseminating knowledge. We contend that academic journals on accounting research are scarce, publish few articles and apply high rejection rates, and the review process is lengthy and expensive. Additionally, an academic elite has unparalleled predominance in comparison to other business disciplines, reflected in an unusual share of published articles with authors affiliated to a small number of academic institutions, and the predominance of certain topics and methodologies. The discipline does not allow the collaborative, iterative and flexible features of innovative knowledge communities. The discipline's internal procedures favour restriction, control, slowness, and expiration, rather than participation, speed and renewal. They are ill suited for advancing knowledge and bode badly for successful research. As a result, accounting academics present low research performance and the discipline is facing steady decline. More importantly, the discipline is handicapped in producing innovative knowledge able to contribute to critical research and long term social well-being.
The Effect of Voluntary Disclosure on Investment Inefficiency
The Accounting Review · 2020 · 44 citations
Senior authorCorresponding- Business
- Finance
- Actuarial science
ABSTRACT We introduce real decisions (a project choice decision, an investment scale decision, and an information acquisition decision) to the Dye (1985) voluntary disclosure framework and examine how the prospect of voluntary disclosure affects managers' real decisions. Riskier projects lead to more volatile environment and hence entail higher efficiency loss at the subsequent investment scale decision stage if managers are uninformed. If managers are informed, they can withhold bad information, and the value of this option is higher for riskier projects. We show that the voluntary nature of managers' disclosure may lead to two types of inefficiencies: (1) managers may choose riskier projects, which generate lower expected cash flow due to the higher efficiency loss at the subsequent decision stage, and (2) managers may over-invest in information acquisition, because informed managers with bad information have the option to pool with uninformed managers and benefit from being overpriced.
Board Expertise and Executive Incentives
Management Science · 2020 · 26 citations
1st authorCorresponding- Computer Science
- Business
- Accounting
We investigate how board expertise affects chief executive officer (CEO) incentives and firm value. The CEO engages in a sequence of tasks: first acquiring information to evaluate a potential project, then reporting his or her assessment of the project to the board, and finally implementing the project if it is adopted. We demonstrate that the CEO receives higher compensation when the board agrees with the CEO on the assessment of the project. Board expertise leads to (weakly) better investment decisions and helps motivate the CEO's evaluation effort; however, it may induce underreporting and reduce the CEO's incentives to properly implement the project. Consequently, if motivating the CEO to evaluate projects is the major concern (e.g., innovative industries), board expertise exhibits an overall positive effect on firm value; however, if motivating the CEO to implement projects is the major concern (e.g., mature industries), board expertise can harm firm value. This paper was accepted by Shiva Rajgopal, accounting.
Information, Incentives and CEO Replacement
SSRN Electronic Journal · 2019-01-01 · 1 citations
articleOpen access1st authorCorresponding
Frequent coauthors
- 6 shared
Tim Baldenius
- 4 shared
Lin Qiu
- 2 shared
Nahum D. Melumad
Columbia University
- 2 shared
Ilan Guttman
New York University
- 1 shared
Yibin Tang
Hohai University
- 1 shared
Ying Chen
- 1 shared
Tim Baldenius
- 1 shared
Jie Tian
University of Waterloo
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