Research

3. Mispricing and Anomalies: An Exogenous Shock to Short Selling from JGTRRA, Journal of Empirical Finance 78, 2024, 101537 (with Yufeng Han, Weike Xu, and Guofu Zhou)

   • Data: Net Overpriced Score (NOPS)
   • On the SFS Cavalcade North America Program (2021)



4. An On-line Machine Learning Return Prediction, Pacific-Basin Finance Journal 79, 2023, 102049 (with Weidong Tian)

   • INFORMS Seth Bonder Foundation Ph.D. Grant (2020)



5. Addressing Systemic Risk Using Contingent Convertible Debt - A Network Analysis, European Journal of Operational Research 290(1), 2021, 263--277 (with Aparna Gupta and Runzu Wang)

   • FMA Best Paper Award Winner in Derivatives & Options (2018), INFORMS Best Student Paper Award Finalist (2018), Global Association of Risk Professionals Research Fellowship (2017)

[NEW] Conditional Expected Subsequent Market Returns by VIX and Index Derivatives (with Steven P. Clark and Weidong Tian)

[NEW] All vs Long-Short: A New Class of Anomalies (with Yufeng Han and Guofu Zhou)

Presentations: 2025 EasternFA

Abstract: In this paper, we develop a novel framework to exploit asset pricing anomalies without relying on traditional long-short portfolio construction, providing new insights into their risk-return dynamics. Unlike the long-short strategy, which primarily concentrates on extreme deciles, our all-decile equity (ADE) portfolio incorporates all ten deciles and employs volatility timing to achieve a more balanced risk-return trade-off. The ADE portfolio consistently outperforms across various market conditions, effectively capturing upside potential while mitigating downside risk. From an asset pricing perspective, it demonstrates strong efficiency in cross-sectional pricing.

Market Risk Premium Expectation: Combining Option Theory with Traditional Predictors (with Hong Liu, Weike Xu and Guofu Zhou)

Presentations: 2024 AFA, 2023 CUHK-RAPS, 2023 CFEA, 2023 Wolfe Research

Abstract: We propose a new option bound on the expected market risk premium by incorporating economic state variables, linking option-based bounds to the traditional predictability literature. Our state-dependent bounds (SDBs) significantly improve out-of-sample predictions of the market risk premium, outperforming models that rely solely on either option prices or traditional stock market predictors. Moreover, using SDBs can substantially increase portfolio Sharpe ratios and enhance investor utility. In a cross-sectional analysis of expected stock returns, we show that option-based information provides incremental value beyond conventional firm characteristics. Our novel findings highlight the importance of integrating information in both option prices and economic state variables.

Macro Financial Trends and Market Expected Returns (with Yufeng Han and Guofu Zhou)

Presentations: 2023 Hong Kong Conference for Fintech, AI and Big Data in Business (part of the keynote address)

Abstract: This paper shows that economic trends, typically used for monetary policy guidance, are also effective in predicting market excess returns. Using a linear combination method across 14 financial variables, we find that moving-average trends outperform the variables' current values in forecasting market returns. Incorporating neural networks further enhances these predictions. Our findings underscore the importance of economic trends, supporting the Federal Reserve’s emphasis on trends over lagged variables. When accounting for nonlinearity, we find that market return predictability is significantly greater than commonly believed. Our results are robust across both U.S. and global equity markets.

The Conditional Forward Return and Autocorrelation from VIX Derivatives (with Weidong Tian and Steven P. Clark)

Presentations: 2022 AFA (Poster), 2021 CICF (China International Conference in Finance)