Job Market Paper
Journal of Financial Economics, R&R
JFI/FIRS Best Doctoral Paper Award (2022)
AsianFA Best Doctoral Paper Award (2022)
EEA/UniCredit Econ Job Market Best Paper Runner-Up Award (2022)
PIIRS Graduate Student Research Award (2022)
Selected Presentations: AFA 2024, NBER Chinese Economy Meeting 2022, MIT GCFP Conference 2022, EFA 2022, FIRS 2022, NFA 2022, MFA 2022, UConn Finance Conference 2022, Economics of Payments XI 2022, CB&DC JMC Workshop 2022
Abstract: This paper investigates how cashless payment affects credit access for the underprivileged using Alipay, a BigTech platform that offers various financial services to over 1 billion users. Leveraging a natural experiment and a representative Alipay user sample, I find that cashless payment adoption increases credit access by 56.3% and a 1% rise in payment flow increases credit line by 0.41%. These effects are stronger for the less educated and the older. Counterfactual analysis shows that cashless payment data increase credit lines by 57.7%, consumer surplus by 0.5% of median income, and lender profit by 41.3% of consumer surplus.
Selected Presentations: NBER Economics of Privacy Conference 2022, AFA 2022, CB&DC Virtual Seminar 2021
Abstract: We combine survey and behavioral data to analyze consumers’ data-sharing choices in a realistic setting in which they exchange personal data for digital services. We find that respondents with stronger privacy concerns authorize more, rather than less, data sharing, confirming the data privacy paradox. Instead of attributing this paradox to the respondents’ unreliable survey responses, resignation from privacy, or behavioral biases, we uncover that privacy-concerned respondents have greater demands for digital services, which offset their privacy concerns. Our findings highlight a key tension for the data economy—privacy concerns and digital demands both grow with the deepening of digital services.
Selected Presentations: AFA 2020, ERFIN Workshop 2020
Abstract: We measure the aggregate return to all equity investors in various funding rounds of a venture company with the founders’ investments valued at their first-round pre-money valuations. We examine 17,242 ventures that had their first funding rounds during 1980 and 2006 and follow them till their exits or 2018 whichever is earlier. Our measure, unlike round-to-round and round-to-exit return measures, does not require valuation information for interim funding rounds, which are mostly missing. The potentially large bias in reported post-money valuations pointed out by Gornall and Strebulaev (2020) does not affect our return measure.
The Impact of Generative Artificial Intelligence on Individual Manual Investment Decisions: Empirical Evidence from Mutual Funds [SSRN Link]
Abstract: The rapid ascent of generative artificial intelligence (GAI) has led individual investors to seek guidance from GAI-based consulting tools such as GAI-based investment consultants (GAICs). Yet, there is scant empirical effort examining the business impact of such GAI tools on individual investors' financial market investments. To fill the critical gap, we collaborate with Ant Fortune, Alibaba’s leading investment arm, and analyze data related to the rollout of Ant Fortune’s GAIC, Zhi Xiaobao. Our analyses provide the first empirical evidence showing that the use of GAIC positively influences investment decisions, redemption activities, and overall returns. Interestingly, contrary to the common belief that novice investors could benefit from AI investment tools for accessible investment information, we find that experienced investors harness more benefits from GAIC, utilizing their existing financial acumen. In addition, we document that the platform’s influence on returns is more significant for risk-seeking investors, suggesting that GAIC could amplify their market decision making efficacy. Despite that novice and risk-averse investors engage more redemption behaviors, they do not attain the equivalent investment return relative to experienced and risk-seeking counterparts, highlighting the role of financial literacy in harnessing the economic benefits of GAICs. In summary, GAICs enhance decision making for experienced and risk-tolerant investors but offer limited advantages to novice and risk-averse investors. Our research not only provides essential managerial insights for platform managers considering GAIC applications, but also sheds light for policy makers in understanding how to improve the use of GAICs for vulnerable investor segments.
Consumer Demand for Digital Money [preliminary draft available upon request]
with Cameron Peng
Abstract: The functioning of money increasingly relies on its digital forms rather than physical cash. Using comprehensive portfolio and consumption data of a representative sample of Alipay users, we study the drivers of consumer demand for digital money. In our setting, individuals allocate wealth between cash, digital money, and an illiquid asset. Digital money can be used immediately for consumption and bears time-varying interest. With an inventory framework, we quantify the welfare implications of the digital money adoption.
Investor’s Responses to Market Fluctuations [preliminary draft available upon request]
with Lina Han and Xuan Luo
Abstract: This paper examines how individual investors respond to the market price fluctuations, using unique individual-level transaction data from a trading experiment and the same individuals’ real trading history on the Alipay app. We find that, in response to exogenous price movements in the experiment, investors tend to be contrarian traders. The magnitude of investors’ response is asymmetric in downturn and upturn markets. Sophisticated investors tend to be more contrarian than the less sophisticated ones. We further document that investors’ contrarian styles are persistent in the experiment and real transactions. The results imply that investors use simple heuristics from the price movement when they make investment decisions in the real world.