The U.S. Treasury’s central role in the financial system rests on its liquidity and safe-haven status. Both may be eroding. Find out how, why and what may happen next. Forewarned is forearmed…

2026
LQG events in 2026.
LQG 2026/09/09 to 2026/09/12 Autumn Seminar – Trinity College Oxford – IP *OLO* Hybrid
Arrive on the afternoon / evening of 2026/09/09 to enjoy a welcome buffet. On Thursday 2026/09/10 and Friday 2026/09/11 the seminar will be held in Trinity College’s Garden Room, in which we will enjoy the research and insights of eleven speakers of international renown.
LQG 2026/07/14 – 18:30 – Alternative data, traditional expertise and ownership – Maxime Bonelli – IP *OLO* Hybrid
How the availability of alternative data affects the performance and investment strategies of active mutual funds and much, much more more!
LQG 2026/06/09 – 18:30 – Making sense of this (…) world and the resurgent Chinese empire. – Brunello Rosa – IP *OLO* Hybrid
Exploring the intersection of geopolitics, energy, digital assets and Cold War 2; making sense of this (…) world… or trying to!
LQG 2026/05/12 – 18:30 – Adventures in high-dimensional space – Robert Macrae – IP *OLO* Hybrid
Linear factors have a vice-like grip on quant thought. What makes them so successful? How should we move beyond them, and what happens when we try? What could possibly go wrong…?
LQG 2026/04/14 – 18:30 – Better Together: Fundamental and Systematic Views in Credit Investing – Simon Polbennikov – IP *OLO* Hybrid
Fundamental and systematic views in credit are complementary and mutually reinforcing…and incorporating systematic signals can strengthen fundamental conviction and improve performance.
LQG 2026/03/10 – 18:30 – Thematic Investing as Missing Factors – Wai Lee – IP *OLO* Hybrid
A framework to isolate thematic exposures from established market, industry, and style factors which incorporates narrative information to capture theme evolution.
LQG 2026/02/17 – 18:30 – What we learned about risk – and what we didn’t – Ed Fishwick – IP *OLO* Hybrid
This talk reflects on the gap between calculated risk and experienced uncertainty – and on the uneasy task of investing when probability offers comfort, but not protection.
LQG 2026/01/15 – 18:30 – Limits to (Machine) Learning – Semyon Malamud – IP *OLO* Hybrid
Machine learning (ML) models are highly flexible but face fundamental limits when learning from finite data. We show that a universal lower bound—the Limits-to-Learning Gap (LLG)—captures the unavoidable gap between the true (population) benchmark and linear ML models.








