Assistant Professor of Finance
Johns Hopkins University Carey Business School
Empirical Asset Pricing, Financial Econometrics, Banking, Networks
Ph.D. in Financial Economics (Joint Program)
The University of Chicago, 2018
Bachelor of Economics, Tsinghua University, 2012
Journal of Financial Economics
Fama-DFA Award, First Place Winner (Best Paper in JFE in the Areas of Capital Markets and Asset Pricing, 2019)
Whether, how, and which firm characteristics determine the cross-sectional variation of expected stock returns? We develop a latent factor model with time-varying loadings (Instrumented Principal Components Analysis or IPCA) which allows observable characteristics as instruments for the unobservable dynamic loadings. IPCA infers that characteristics affect expected return by driving the exposure to latent risk factors, and rules out characteristics-associated anomaly (compensation without risk). Four IPCA factors explain the cross-section of average returns significantly more accurately than existing factor models. Furthermore, among a large collection of characteristics explored in the literature, only eight are statistically significant in the IPCA specification and are responsible for nearly 100% of the model's accuracy.
R&R, Review of Financial Studies
We seek fundamental risks from news text. Conceptually, news is closely related to the idea of systematic risk, in particular the “state variables” in the ICAPM. News captures investors' concerns about future investment opportunities, and hence drives the current pricing kernel. This paper demonstrates a way to extract a parsimonious set of risk factors and eventually a univariate pricing kernel from news text. The state variables are reduced and selected from the variations in attention allocated to different news narratives. As a result, the risk factors attain clear text-based interpretability as well as top-of-the-line asset pricing performance. The empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso).
In any market with uninformative flows, the maximum Sharpe-ratio portfolio can be separated into two. The first portfolio uses only fundamental information to maximize the Sharpe ratio. The second portfolio provides liquidity to uninformative flows and maximizes price impact ratio, which is defined as a portfolio’s price impact over its fundamental risk. We develop the factor model of price impacts to empirically investigate the maximum-price-impact-ratio (MPIR) portfolio. For U.S. equity mutual fund flows, we find that the MPIR portfolio constructed using flows into Fama and French (1993) factors is a good choice.
Interbank lending is beneficial but subject to coordination failure. With interbank wholesale funding, banks' balance sheets become inflated, which give the retail depositors a sense of safety to allow the bank to have more illiquid investments. In interbank runs, banks run on banks as they mutually reinforce each other to withdraw interbank lending. Banks' individually precautionary liquidity hoarding strategies are connected by the pairwise lending relationships. Mean-field analysis extracts the systemic behavior from the network of strategic interactions. I show such dispersed and indirectly linked interactions also lead to discontinuous and system-wide liquidity crunches as if the interactions are centralized. Local insolvency shocks trigger the interbank run if the network is unraveled beyond a critical point. The model is applied to identify the optimal capital injection targets of government bailouts and study the systemic effects of the proposed regulations on restraining the highly connected banks.
Econometrics method used in ``Characteristics Are Covariances: A Unified Model of Risk and Return".
This paper studies the general equilibrium effects of industry-specific productivity shock in an economy in which sectors are connected via input-output linkages. My central finding is productivity shocks do not only travel downstream as is standard in the literature, but also trigger demand change at the final consumption industries, which propagates upstream. I label this novel mechanism "reflection channel". Differences of the elasticity of substitution of consumption and production for the final consumption industries drive the demand change. Empirically, the magnitude of the reflection channel is around three times greater than the previously studied downstream channel. When a positive productivity shock reaches a final consumption industry, consumers substitute towards it much more than producers substitute away, increasing the demand of its upstream industries, and vice versa.
Managing Financial Risks (M.S. in Finance)