Cong Zhang
PhD Candidate
The University of Chicago
Booth School of Business
PhD Candidate
The University of Chicago
Booth School of Business
Cong Zhang is a PhD candidate at the University of Chicago Booth School of Business, advised by Prof. George M. Constantinides, Prof. Lars P. Hansen, Prof. Chad Syverson, and Prof. Jeffrey R. Russell.
His research develops econometric methodologies and theoretical frameworks to better understand how various forms of uncertainties impact financial market dynamics. He aims to improve the measurement of risk prices incorporating regime changes and to deepen the understanding of market anomalies. His work bridges the gap between these anomalies and rational market behavior, providing new insights into how markets respond to various forms of uncertainty.
Cong is the first PhD candidate from Chicago Booth to hold both a Master of Legal Studies from the University of Chicago Law School and an MBA from the Booth School of Business. His interdisciplinary expertise in finance, law, and applied econometrics allows him to examine the interactions between institutional and regulatory frameworks and financial markets. Prior to his graduate studies, he earned a Bachelor's degree from the University of Michigan, with a triple major in Mathematics, Honors Economics, Statistics.
Asset Pricing in Digital Economy with Regulations
Abstract: I quantitatively assess the economic implications of two potential regulatory shifts in the digital economy: data privacy laws and command-and-control regulations. I develop a production-based equilibrium model that incorporates the non-rival nature of digital capital while accounting for negative externalities of excessive data collection and data sharing, referred to as data emissions. The model decomposes the risk price associated with rising market concentration, driven by digital capital accumulation, into two components: immediate firm-level gains in output from adopting data-driven technologies and potential long-term social costs from data emissions. The model implication aligns with empirical evidence showing that equity risk prices have turned negative over the past twenty years, coinciding with significant growth in data trading markets and data-driven technologies. The model predicts that firms adopting data-driven technologies have stock returns that co-move with increasing market concentration and resemble those of growth firms. Furthermore, I explore adoption trajectories for data-driven technologies and evaluate the social costs of data emissions under various economic conditions and model uncertainties introduced by command-and-control regulations. My counterfactual analysis suggests that the most effective regulatory paradigm combines data privacy laws with command-and-control measures. This combination, when enforced through protocols that reduce uncertainty in data emissions while introducing uncertainty in innovation dynamics, can enhance social welfare.
The Effects of Economic Uncertainty on Financial Volatility: A Comprehensive Investigation, (2023), with Zhuo Huang, Tianyi Wang, and Chen Tong. Journal of Empirical Finance, Volume 73, September 2023, Pages 369-389.
Optimizing Return Forecasts: A Bayesian Intermediary Asset Pricing Approach, (2024), with Ming Gao. Under Review.
Impact of AI Adoption on Economic Dynamics through Habit Formation: Decision Making and Asset Pricing, (2024). Under Review.
Synchronized Shifts: Decoding the Co-movement of Stock and Bitcoin Returns, (2024).
Corporate Bond Pricing: A Bayesian Intermediary Approach, (2024), with Ming Gao, Ben Marrow, and Steven Wu.
Cross Sectional Asset Pricing with Text Embedding, (2024), with Mark He.
Robust Long-Term Investment Strategy via Causal Reinforcement Learning, (2024), with Yueyang Zhong.
TA for Executive MBA Business Statistics (Instructor: Prof. Jefferey Russell): Spring 2022
TA for Executive MBA Investment (Instructor: Prof. John Heaton): Fall 2019
TA for PhD Time-series Analysis (Instructor: Prof. Jefferey Russell): Winter 2018-2021
TA for MBA Business Statistics (Instructor: Prof. Bryon Aragam): Winter 2022
TA for MBA Competitive Strategy (Instructor: Prof. Yoad Shefi): Spring 2019
Winner of the 2024 Arnold Zellner Doctoral Prize, Chicago Booth
MLS Full Tuition Waiver and Stipend (Inaugural Recipient), The University of Chicago Law School
Stevens Doctoral Program Research Funding Support, Chicago Booth
Doctoral Program Research Funding Support, Chicago Booth
The Eugene Fama Endowed Ph.D. Fellowship, Chicago Booth
The Beryl W. Sprinkel Ph.D. Stipend, Chicago Booth
Financial Economics of InsuranceWorkshop Grant, Bendheim Center for Finance at Princeton
Chicago Booth Ph.D. Fellowship, Chicago Booth
Phi Beta Kappa, University of Michigan
James B. Angell Scholar, University of Michigan
High Honors and High Distinction, University of Michigan