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)

Economic 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.

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 extend the Martin (2017) option bound 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, SDBs 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.

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

Presentations: 2023 Wolfe Research, 2022 AFA (Poster), 2021 CICF (China International Conference in Finance)

Abstract: This paper develops a new methodology to express the conditional forward return of the S&P 500 index with VIX derivatives. Within this framework, we obtain time-varying market autocorrelation in real-time. The derivatives market information unveils a robust short-term reversal pattern on the S&P 500, thereby the month return predictability. Moreover, we employ signals extracted from the autocorrelation to implement market-timing strategies, demonstrating the substantial economic value of this approach. Lastly, we present a stylized pro-cyclical term structure of the conditional forward returns.