Junjun Quan
Welcome! I am a finance Ph.D. candidate at Columbia Business School.
My research interests are corporate finance, corporate governance, and macro finance. My work combines empirical analysis of novel data sets with theoretical frameworks and structural estimations to uncover new insights.
My research agenda examines how the rise of data as a new asset reshapes firm resource allocations, investment decisions, and innovation. I study the welfare implications of privacy regulations, which give consumers better control of their data but limit firms' access to this valuable resource. Moreover, I apply the models from information economics to estimate the price informativeness in the corporate bond market and the information production in corporate voting events.
Contact Information: jq2291@columbia.edu
You can find my CV here.
References
Working Papers
Tracing Out International Data Flow: The Value of Data and Privacy (Job Market Paper) [SSRN]
Presentations: AFA 2024, NFA 2023, SFA 2023, CEPR Endless Summer Conference 2023, USC Marshall PhD Conference in Finance 2023, Lang Center PhD Fellowship Research Showcase 2023, Deming Doctoral Fellows Seminar 2022, Columbia Business School 2022, Columbia Financial Economics Colloquium 2022
Poster Session: 21st Macro Fiannce Society Workshop
Grants and Awards: Eugene Lang Entrepreneurship Center PhD Fellowship 2021, Deming Doctoral Fellowship 2021, Chazen Doctoral Research Grant 2022, Best 4th Year Paper Award at Columbia Business School, SFA Best Paper Award
Abstract: I measure firms' value of data and consumers' privacy preferences by analyzing the supply and demand-side reactions to the EU’s General Data Protection Regulation (GDPR). While previous research has focused on consumer reactions to privacy regulations, my study also incorporates firm responses. After GDPR limits firms’ access to data, the EU sales share of US data-intensive firms declines by 8%. EU consumers, who can choose to share less data, suffer a 6% deterioration in user satisfaction as measured by app ratings. I develop an equilibrium model to map these empirical findings and estimate the value of data and privacy. Privacy-conscious consumers gain from privacy protection. However, the quantitative model reveals that the digital welfare of other consumers declines because firms also use data to enhance productivity and customize digital products. In aggregate, EU digital welfare declines by 4%.
Bond Price Informativeness (with Nina Boyarchenko, Or Shachar, and Laura Veldkamp)
(Draft Available Upon Request) [contact me]
Abstract: We develop and deploy a new methodology to measure the information content in the corporate bond market, that accounts for the skewness of bond returns. We estimate how much information investors have about the future payoffs of the assets they invest in. We decompose the time-varying demand elasticity of bond investors into two components: information precision and state-dependent risk aversion. Our estimation uncovers a large fluctuation in the information content of the corporate bond market over time. We find that, during the 2007-2008 financial crisis, the drop in demand elasticity is driven by both an increase in effective risk aversion during bad times and a decrease in information content. In contrast, during the COVID-19 period, the decrease in demand elasticity is mostly driven by heightened effective risk aversion.
Works In Progress
Voter IQ (with Alon Brav, Wei Jiang, and Doron Levit)
(New Version Coming Soon) [contact me]
Abstract: We study mutual funds' voting performance from their voting records in proxy contests and introduce a new measure of voting intelligence (Voter IQ), in contrast to fund managers' skills in generating high portfolio returns. We explore both a market-based non-parametric approach and a structural framework. We find that, on average, active funds exhibit higher voting intelligence than passive funds, and top fund families with a large fraction of assets passively managed have low Voter IQ. Our measure is effective at differentiating voting ability---a fund that votes randomly or mechanically in one direction will be placed at the bottom of the IQ distribution. High-IQ funds guide their voting decisions with information and vote similar to each other, especially in contentious proxy fights. Furthermore, we test the external validity of our results by analyzing the regular shareholder and management proposal and show that high-IQ funds exhibit similar voting patterns.
To Vote or Not to Vote: Passive Governance and Share Lending
Presentations: Bernstein Research Lightning Talks 2022
Grants: Bernstein Center Doctoral Research Grants 2021
Abstract: I study how the share lending practice of passive funds reshapes the power dynamics between activist shareholders and short-sellers in contested board elections (proxy contests). I build a voting model to formalize the interaction among activist shareholders, short-sellers, and passive funds. Short-sellers borrow shares from uninformed passive funds and take away their voting power with high lending fees, which leaves the activist shareholders with a more informed voter base. I test the empirical predictions with proxy contest voting, institutional ownership, and short selling data. The study explores a novel channel thorough which passive asset management affects corporate governance.
AI Euphoria
Abstract: This project explores the dynamics of corporate investment in AI technologies amid the rapidly evolving generative AI landscape. Utilizing diverse data sources such as Lightcast's job postings, Coresignal's LinkedIn profiles, and the USPTO's patent records, this study assesses how companies are expanding their data processing and AI capabilities. Furthermore, this study investigates the stock market's reaction to firm AI investments and analyzes conference transcripts to examine the corporate discourse on AI developments and strategic expenditures.