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Mengnan "Cliff" Zhu

I am an Assistant Professor in Finance at Dickinson College. I received my Ph.D. in International Economics and Finance from Brandeis University in 2022.


I specialize in applying machine learning techniques along with established econometric methods to investigate empirical corporate finance issues, such as capital structure, initial public offering (IPO), and the impact of financial regulations. The combination of solid knowledge in literature and work experience in the industry has allowed me to ask critical questions that are grounded in financial-economic theories.

My dissertation, “The Analysis of Corporate Security Issuance Using Machine Learning Techniques,” focuses on developing new perspectives on the security issuance choices at different stages of a firm’s life cycle. In my solo-authored job market paper, I present the first analysis that focuses on the confidential IPO registration process adopted by 86% of firms since the JOBS Act of 2012. Using textual analysis, I am the first to compare the information content of draft registration statements to its formal prospectus and document that valuable information is produced during the confidential revise-and-resubmit process between the firm and SEC. I construct a novel proxy for the content of SEC comment letters before their release, which allows investors to make informed investment decisions before the IPO and obtain a 4.5% cumulative abnormal return.

Does the Confidential IPO Registration Process Create Value? (job market paper)

Select Presentations: FMA 2021 DSC; NFA 2021; UT Austin PhD Symposium; FMCG 2021

Who You Are Matters More Than What You Do: Financing Expectations and
Firm Performance 

with Mark Kamstra, Debarshi Nandy, Pei Shao

Are IPOs Systemically Overvalued? Insights From Prospectus

with Debarshi Nandy

Is Going Public via SPAC Regulatory Arbitrage? A Textual Analysis Approach

with Yaxuan Wen

Contact Information

Mengnan Zhu

Brandeis International Business School

415 South Street, MS 032

Waltham, MA 02453, USA

Mobile:  (+1) 732-331-2665


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