About

Welcome to my website! I am an Assistant Professor of Finance at CUHK Business School, The Chinese University of Hong Kong. My research interests include AI for finance, empirical asset pricing, investment strategies, asset management, and high-frequency finance.

My research studies how information, investor behavior, and market structure shape asset prices. I am particularly interested in using large-scale data and AI methods to understand how expected returns vary across assets and over time, how investors respond to changing market environments, and how heterogeneity in beliefs affects trading and price formation.

I received my Ph.D. in Finance from Washington University in St. Louis, where I was fortunate to be advised by Prof. Asaf Manela and Prof. Guofu Zhou.

Contact

• songrunhe@cuhk.edu.hk

Education

• Washington University in St. Louis, Olin Business School
  Ph.D. in Finance, 2026

• The University of Chicago, Social Science Division
  MA in Economics, 2021

• Central University of Finance and Economics, School of Finance
  BSc in Finance, 2019

Working Papers

1. Interpretable Systematic Risk around the Clock (Job Market Paper)

   Abstract

   In this paper, I present the first comprehensive, around-the-clock analysis of systematic jump risk by combining high-frequency market data with contemporaneous news narratives identified as the underlying causes of market jumps. These narratives are retrieved and classified using a state-of-the-art open-source reasoning LLM. Decomposing market risk into interpretable jump categories reveals significant heterogeneity in risk premia, with macroeconomic news commanding the largest and most persistent premium. Leveraging this insight, I construct an annually rebalanced real-time Fama-MacBeth factor-mimicking portfolio that isolates the most strongly priced jump risk, achieving a high out-of-sample Sharpe ratio and delivering significant alphas relative to standard factor models. The results highlight the value of around-the-clock analysis and LLM-based narrative understanding for identifying and managing priced risks in real time.
   

   Selected Conferences: ABFR Doctoral Research Symposium (2025), IMIM Rising Stars Seminar Series (2025), 21st Annual Olin Finance Conference Poster Session (2025), AFA Poster Session (2026)

2. Chronologically Consistent Large Language Models (with Linying Lv, Asaf Manela, and Jimmy Wu)
   Accepted at Journal of Financial Economics

   Selected Conferences: NBER Summer Institute (2025), AFA (2026), ABFR Webinar (2025), Federal Reserve Board (2025)

3. Fundamentals of Perpetual Futures (with Asaf Manela, Omri Ross, and Victor von Wachter)
   Revise & Resubmit at The Review of Financial Studies

   Selected Conferences: Utah Winter Finance Conference (2024), Virtual Derivatives Workshop (2024)

4. Empirical Asset Pricing with Probability Forecasts (with Linying Lv, and Guofu Zhou)
   Revise & Resubmit at Management Science

   Selected Conferences: AFA (2025), 8th Wolfe Research QES (2024)

5. ETFs, Anomalies, and Market Efficiency (with Ilias Filippou, Sophia Zhengzi Li, and Guofu Zhou)

   Selected Conferences: WFA (2023), NFA(2023), AFS(2025), FutFin(2023)

6. Principal Portfolios: The Multi-Signal Case (with Ming Yuan and Guofu Zhou)
   

7. How Accurate are Survey Forecasts on the Market (with Jiaen Li, Linying Lv, and Guofu Zhou)

8. Instruction Tuning Chronologically Consistent Language Models (with Linying Lv, Asaf Manela, and Jimmy Wu)