Hi, I’m Yuhan Ye

Ph.D. Candidate in Finance, Swiss Finance Institute (SFI) & University of Lugano (USI). I am on the 2025–2026 academic job market.

Research Interests

Corporate Finance
Machine Learning in Finance
Human vs. Machine Decision Making

Contact: yuhan.ye@usi.ch

Job Market Paper

Does Better Information Favor Humans or Machines? Evidence from Global Earnings Forecasts [SSRN]

Abstract

This paper investigates whether better information favors humans or machines. Using detailed analyst forecasts and machine-learning predictions for 47 global stock markets from 1985 to 2024, I study how information environments affect the relative forecasting performance of human analysts and algorithms. Human forecasts are relatively more accurate in stronger information environments, characterized by greater data availability, transparency, and access to information. These results indicate that richer information environments favor humans over machines. The evidence is consistent with humans’ comparative advantage in processing qualitative and contextual information when credible data and transparent institutions are present. In contrast, in weaker information environments, machines complement humans by providing stable, data-driven signals. Cross-market patterns further show that institutional transparency amplifies humans’ relative advantage, underscoring the role of information environments in shaping human–machine complementarity in financial forecasting.

Presented at:
AFA 2026 (Poster, scheduled), EPFL Memento Brown Bag Seminar, SFI Academic Job Market Workshop, USI IFin Brown Bag, CREDIT Venice 2025- Emerging Global Financial Systems: Exploring Polarization, Systemic Risks, Innovation, and Sustainable Solutions, 2025 HEC Paris PhD Workshop, 2nd HKUST IAS–SBM Joint Workshop – Financial Econometrics in the Big Data Era (Poster), 2025 PhD Summer School on Finance and Product Markets, UZH Rising Scholars Conference 2025, and SFI Research Days.

Working Papers & Work in Progress

Soft Information and Home Bias: Decomposing the Local Advantage of Human Analysts

(with Laurent Frésard)

We decompose global analyst forecasts into soft information, bias, and noise, providing a structural framework to revisit the home bias and local advantage puzzles.

Who Adapts Faster in Crises? Global Evidence from Human and Machine Forecasts

Humans adapt faster at the onset of crises, while machines catch up and outperform as new regimes stabilize, revealing fundamental differences in learning dynamics.

Presentations: SFI Research Days 2024, USI Lugano Reading Group

Gravity in Global Equity Networks

I uncover a gravity structure in global equity markets, showing that unsupervised clustering of equity returns naturally reflects economic and geographic proximity.

Presentations: Gerzensee Advanced Course on Recent Advances in International Finance, USI Lugano Reading Group