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, (2024).
Abstract: I quantitatively assess the economic implications of two regulatory paradigms in the digital economy: data privacy laws and command-and-control regulations. To this end, I develop a production-based equilibrium model that (i) micro-founds firms' technology adoption decisions, (ii) incorporates "data emissions" as negative externalities of excessive data collection and data sharing arising from the non-rival nature of digital capital, and (iii) accounts for potential model misspecifications introduced by regulatory changes. The model implies a decomposition of the risk price associated with rising market concentration, driven by digital capital accumulation, into two components: short-term firm-level productivity gains from adopting data-driven technologies and long-term social costs stemming from data emissions. This theoretical implication aligns with empirical evidence showing that equity risk premia have turned negative since the early 2000s, coinciding with the rapid growth of data trading market and data-driven technologies over the past twenty years. The model further predicts that firms adopting data-driven technologies exhibit stock returns that co-move more with market concentration growth, resembling the return profiles of growth firms. Using a calibrated model informed by financial market data, I demonstrate that the marginal social cost of data emissions declines as technology adoption scales, and can be further mitigated by increases in total factor productivity or innovation intensity. Finally, counterfactual analysis suggests that the most effective regulatory paradigm combines data privacy laws with command-and-control regulations. This hybrid paradigm, when enforced through protocols that reduce uncertainty in data emissions while embracing 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