Research

What Can Cross-Sectional Stocks Tell Us About Core Inflation Shocks?, with Claire Yurong Hong, Jun Liu and Jun Pan, 2026

R&R at The Review of Financial Studies 

Abstract: We document active price discovery in cross-sectional stocks for core inflation shocks through the cash flow channel. By estimating stock-level core inflation exposures using an announcement-day approach, we find: 1) significant and persistent cross-sectional spread in core inflation exposure; 2) firms with positive inflation exposure later experience increased cash flow as inflation rises; and 3) the relative pricing of stocks with diverging core inflation exposures significantly predicts core inflation shocks and economists’ forecasting errors. This predictability is especially strong under heightened inflation risk, including the inflation surges of 2021 and 1973, and when the Fed is behind the curve.

Presented at: NBER Asset Pricing 2024, NFA 2024, EFA 2024, CICF 2023, ABFER 2023, BWFG Annual Conference 2023, Tilburg Finance Summit 2023, NBER SI 2023 Forecasting & Empirical Methods, 2023 Symposium on Recent Developments in Time Series Econometrics and Applied Macroeconomics, SOM at Fudan University, SAIF at Shanghai Jiao Tong University, MIT Finance PhD Workshop, Xi’an Jiaotong Liverpool University, Peking University, FISF at Fudan University, PBCSF Tsinghua, UC Irvine University, Chinese Academy of Sciences, University of Oxford, Imperial College Business School, Renmin University of China, Tongji University, Peking University HSBC Business School, CUHK Shenzhen, the Federal Reserve Board, the U.S. Treasury Office of Financial Research, the IMF, and the Bank of England. 

What Can Macro-Active Bond Funds Tell Us About Monetary Policy Change?, with Claire Yurong Hong and Jun Pan, 2025

R&R at Journal of Financial Economics 

Abstract: This paper focuses on actively managed bond mutual funds, whose performance is most sensitive to monetary policy shocks. We document significant and persistent FOMC-day outperformance by macro-active  bond funds, which is particularly pronounced during periods of heightened macroeconomic disagreement and attention. Consistent with their forecasting ability, these funds' pre-FOMC portfolio duration adjustments can predict FOMC-day monetary policy shocks beyond the information offered by economic data and asset returns. Furthermore, we show that this macro-investing skill extends to GDP and CPI announcements and is consistently shared across various fund styles within fund families. 

Presented at: SFS Cavalcade Asia-Pacific 2024, Finance Down Under (FDU) Conference 2023, New Finance Conference 2023, PGIM IAS-SAIF Asia Research Conference 2023, World Symposium on Investment Research 2022, ABFER Annual Conference 2022, Five Star Meeting 2022, CICF 2021, World Finance Conference 2021, FMA Annual Meeting 2021, SAIF at Shanghai Jiao Tong University, Shanghai University of Finance and Economics, Fudan University, China-ASEAN Institute of Financial Cooperation, and Xi’an Jiaotong-Liverpool University.

What Beauty Brings? Managers' Attractiveness and Mutual Fund Performance, with Chengyu Bai, 2023

Abstract: In this paper, we study the relationship between stock fund managers' facial attractiveness and fund outcomes. Utilizing the state-of-art deep learning technique to quantify facial attractiveness, we find that funds with facial unattractive managers outperform funds with attractive managers by over 2% per annum. We next show that good-looking managers attract significantly higher fund flow especially if the funds are available on Fintech platforms where their photos are accessible to investors. Good-looking managers also have greater chance of promotion and tend to move to small firms. The potential explanations for their underperformance include inadequate ability, insufficient effort, overconfidence and inefficient site visits.

Presented at: 16th BFWG annual conference, SWUFE Graduate-Student Forum, Shanghai Jiao Tong University.

Media coverage: Wall Street Journal, Financial Times. 

Academic Presentations

WFC 2021, FMA 2021, FDU 2023, China PhD Forum in Finance, China PhD Forum in Macroeconomics, CUFE Spring Financial Symposium 2025, CUFE Summer International Financial Symposium 2025, The 22nd Chinese Finance Annual Meeting.

Seminar at Shanghai Jiao Tong University, MIT Finance PhD Workshop, Central University of Finance and Economics.

Conference Discussions

FMA 2021, 13th Moscow Finance Conference, 11th Asia-Pacific Forum on Economy and Finance, CUFE Summer International Financial Symposium 2025, The 22nd Chinese Finance Annual Meeting.

Professional Services

Referee for Pacific-Basin Finance Journal, Review of Finance.

Teaching

Instructor: Big Data and Finance (BA, CUFE 2025), Green Finance (BA, CUFE 2025).

Guest Lecturer: Thesis Writing (Ph.D, CUFE 2024), Advanced Fintech (Ph.D, CUFE 2024), Advanced Capital Markets (Ph.D, CUFE 2025), Fintech and Inclusive Finance (Ph.D, CUFE 2025).

T.A. for Ph.D Classes at SAIF: Empirical Corporate Finance, Empirical Asset Pricing.

T.A. for MBA Classes at SAIF: Financial Markets, Derivative Securities, Financial Risk Management, Alternative Investments, Fixed Income Securities, Principal of Finance, Securities Analysis & Valuation, Impact investment, MBA Thesis Writing, Macroeconomics.

Honors

Outstanding Graduate in Shanghai, 2024

National Scholarship, 2022 & 2016

China Scholarship Council, 2022

Merit Student in Shanghai Jiao Tong University, 2022

Outstanding Graduate in Nanjing University, 2018

Merit Student in Jiangsu Province, 2017

Outstanding Student Model in Nanjing University, 2017