Assistant Professor of Finance
Johns Hopkins University Carey Business School
Research Interests:
Empirical Asset Pricing, Financial Econometrics, Banking, Networks
Education:
Ph.D. in Financial Economics (Joint Program)
The University of Chicago, 2018
Bachelor of Economics, Tsinghua University, 2012
Publication
(with Bryan Kelly and Seth Pruitt)
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.
Working Papers
(with Leland Bybee and Bryan Kelly )
Review of Financial Studies, Forthcoming
We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text.
(with Federico Bandi )
We model predictive frequency-specific cycles. By employing suitable matrix representations, we express lead values of covariance-stationary multivariate time series in terms of conditionally orthonormal frequency-specific basis. The representations yield conditionally orthogonal decompositions of these lead values. They also provide decompositions of the conditional variances and betas in terms of conditional frequency-specific variances and betas capturing predictive variability and co-variability over cycles of alternative lengths without spillovers across cycles. Making use of the proposed representations within the classical family of time-varying conditional volatility models, we document the role of time-varying volatility forecasts in generating orthogonal predictive frequency-specific cycles in returns. We conclude by providing suggestive evidence that the conditional variances of the predictive return cycles may be priced over short-to-medium horizons.
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.
(with Bryan Kelly and Seth Pruitt)
Econometrics method used in ``Characteristics Are Covariances: A Unified Model of Risk and Return".
Working Papers
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.
Teaching
Managing Financial Risks (M.S. in Finance)
Contact
Email: ys@jhu.edu
Phone: 410-234-4505
Johns Hopkins University Carey Business School
100 International Drive, Baltimore MD 21202