Research Interests
Asset Pricing, Macroeconomics, Firm Dynamics
Publications
The Collateralizability Premium, with Hengjie Ai, Kai Li, and Christian Schlag, Review of Financial Studies, 2020
Credit Market Frictions and the Linkage Between Dispersion and Macro Uncertainty, Management Science, 2024
Working Papers
Equilibrium Value and Profitability Premiums, with Hengjie Ai and Jincheng Tong
R&R, Journal of Finance
WFA (2021, Virtual), MFA (2022, Chicago), FIRS (2023, Vancouver)
Abstract: Standard production-based asset pricing models cannot simultaneously explain the value premium and the gross profitability premium. Empirically, we show that value and profitability sorted portfolios differ in the persistence of productivity. We develop a general equilibrium model where firm-level productivity has a two-factor structure with different persistence. We demonstrate that with capital adjustment costs and variable capital utilization, our model can simultaneously account for both the gross profitability premium and the value premium.
Markup Shocks and Asset Prices, with Alexandre Corhay and Jincheng Tong
EFA (2022, Barcelona), AFA (2023, New Orleans), WFA (2023, San Francisco), SFS Cavalcade NA (2023, Atlanta), Santiago Finance Workshop (2022), UT Dallas 2022 Finance Conference, SFS Cavalcade Asia-Pacific (2022, Virtual), MFA (2022, Chicago), Kelly Junior Finance Conference (2022), HEC-McGill Finance Conference (2023), UConn Finance Conference (2023), Stanford SITE (2023), CUHK-RAPS Conference (2023, Hong Kong)
Abstract: We explore the asset pricing implications of shocks that allow firms to extract more rents from consumers. These markup shocks directly impact the representative household's marginal utility and the firms' cash flow. Using firm-level data, we construct a measure of aggregate markup shocks and show that the price of markup risk is negative, that is, a positive markup shock is associated with high marginal utility states. Markup shocks generate differences in risk premia due to their heterogeneous impact on firms. Firms with larger exposures to markup shocks are less risky and have lower expected returns. We rationalize these findings in a general equilibrium model with markup shocks.
Data, Markups, and Asset Prices, with Alexandre Corhay, Kejia Hu, Jincheng Tong, and Chi-Yang Tsou
MFA(2024, Chicago), WFA(2025, Snowbird), COAP(2025, Luxembourg), CICF(2025, Shenzhen), AEA(2026, Philadelphia)
Abstract: This paper investigates how data technology affects firms' market power and asset prices. Using a novel dataset tracking firms' employment of data scientists, we document three key empirical findings: firms with higher proportions of data scientists exhibit larger markups, have higher information quality proxied by lower sales forecast errors, and earn higher stock returns. Specifically, a long-short portfolio strategy based on firms' data scientist ratios generates significant annual excess returns of approximately 4%. To quantitatively rationalize these empirical findings, we develop a heterogeneous firm model in which firms optimally hire data scientists to learn about unobserved consumer tastes. The model demonstrates how data enables firms to improve demand forecasting accuracy and extract higher markups. Importantly, supply-constrained firms have stronger incentives to hire data scientists, leading to countercyclical data scientist hiring that amplifies their exposures to aggregate risk through an operating leverage channel. We provide empirical evidence supporting our model mechanism.