Dr Stephanie Yuen - From quarks to quant: Where high-energy physics meets high-frequency markets

Wednesday 25 Jun 2025, 13:30 → 14:30 UTC

610 (G.O. Jones Bulding)

https://fnal.zoom.us/j/93197574512?pwd=bGlJTHlGTnVtTFFwRnlpYnhrdm5rdz09

About the speaker: 

Dr. Stephanie Yuen is an eFX Quant Trader at UBS, where she applies quantitative techniques to navigate global financial markets. She earned her PhD in physics at CERN and Rheinische Friedrich-Wilhelms-Universität Bonn, where she was an ATLAS author until 2019, with publications on the Higgs boson discovery in decays to hadronically decaying tau leptons (H → τhad τhad), as well as precision measurements of Higgs boson coupling and Charge-Parity (CP) properties, key parameters in explaining the matter-antimatter asymmetry of the universe. For her research, she is a 2025 Laureate of the Breakthrough Prize in Fundamental Physics, founded by Sergey Brin, Priscilla Chan and Mark Zuckerberg et al.

13:30 → 14:30 From quarks to quant: Where high-energy physics meets high-frequency markets

The foreign exchange (FX) market is the world’s largest financial market, far surpassing equities and bonds in daily turnover. In the spot FX segment, market makers manage risk by hedging externally, often at the cost of crossing the bid-offer spread and exposing trading intent. To navigate this trade-off, machine learning models are increasingly used to forecast adverse price movements and reduce market impact, while mitigating the risk of information leakage.

This challenge of extracting signal from noise parallels high-energy physics at CERN’s Large Hadron Collider, where real-time algorithms trigger on rare events—such as Higgs boson decays, produced in about 1 of every 10 billion collisions, and phenomena beyond the Standard Model—amid 600 million particle collisions per second. These are modelled as Poisson point processes, assuming event independence. In contrast, financial time series are inherently sequential, requiring models that capture temporal dependencies. In this talk, I’ll introduce the structure of the spot FX market and present a case study on using machine learning to predict alpha-generating opportunities. I’ll also discuss the limitations of applying AI to time-dependent data and explore the conceptual bridges and key differences between experimental physics and electronic trading.

Convener: Dr Stephanie Yuen (UBS)