Associate Professor of Finance (without tenure) at Saïd Business School
University of Oxford
Job Market Paper
Journal of Financial Economics, R&R
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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)
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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.
Working Papers
with Long Chen, Yadong Huang and Wei Xiong
Journal of Financial Economics, R&R
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Media Coverage: VoxEU, VoxChina
Abstract: We combine survey and behavioral data to analyze consumers’ data-sharing choices within a major finance platform. Intriguingly, respondents with stronger privacy concerns authorize more data sharing, underscoring the data privacy paradox. To explain this paradox, we uncover a novel mechanism: the deepening of the data economy amplifies consumers' demand for digital services, even as their privacy concerns heighten. This suggests a nuanced market dynamic. While privacy concerns have been on the rise, the benefits from increasingly efficient digital services, fueled by consumer data, may offset or even dominate these concerns, encouraging continued data sharing.
How Ethical Should AI Be?
How AI Alignment Shapes the Risk Preferences of LLMs [SSRN Link] [arXiv Link] [pdf]
with Hayong Yun and Xingjian Zheng
Abstract: This study examines the risk preferences of Large Language Models (LLMs) and how aligning them with human ethical standards affects their economic decision-making. Testing 50 LLMs across self-reported and simulated investment tasks, we find wide variation in risk attitudes. Notably, models scoring higher on safety metrics tend to exhibit greater risk aversion. Through a direct alignment exercise, we establish that embedding human values—harmlessness, helpfulness, and honesty—causally shifts LLMs toward more cautious decision-making. While moderate alignment improved financial forecasting, excessive alignment led to overcautious decisions that hurt predictive accuracy. This trade-off underscores the need for AI governance that balances ethical safeguards with domain-specific risk-taking, ensuring alignment mechanisms don’t overly hinder AI-driven decision-making in finance and other economic domains.
with Ravi Jagannathan and Jiaheng Yu
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Abstract: We measure the aggregate lifetime return to equity investments in venture companies. We consider the aggregate portfolio of all equity investments made in various funding rounds of 17,242 ventures that had their first funding round between 1980-2006. We track these ventures till their exits or till 2018 to get a complete picture of their lifetime return. The Kaplan and Schoar (2005) Public Market Equivalent (PME) measure of performance is 1.42, i.e., each dol- lar invested returned 1.42 dollars after adjusting for risk and time value. We use an imputation model following Hall and Woodward (2010) to address missing data issues.
The Impact of Generative Artificial Intelligence on Individual Manual Investment Decisions: Empirical Evidence from Mutual Funds [SSRN Link]
with Yue Guo, Siliang Tong, Tao Chen, and Subodha Kumar
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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
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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
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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.
Invited Articles and Non-Refereed Publications
Venture Investment Returns [Link]
with Ravi Jagannathan and Honghao Wang
The Palgrave Encyclopedia of Private Equity, 2023
Editors: Douglas Cumming, Benjamin Hammer