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…
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…?
Fundamental and systematic views in credit are complementary and mutually reinforcing…and incorporating systematic signals can strengthen fundamental conviction and improve performance.
A framework to isolate thematic exposures from established market, industry, and style factors which incorporates narrative information to capture theme evolution.
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.
Oil trading – a unique opportunity to understand stochastic inventories with boundary conditions from “stock-outs” to “tank-tops” followed by practical risk premia and other trading strategies from a successful and experienced trader.
Investors face a new regime. Macro mega-forces including demographic change, public debt, climate and deglobalisation indicate higher inflation and lower growth. New portfolio strategies and designs are required to protect purchasing power.
Memories of the LQG 2025/09/03 – 2025/09/06 – Autumn Seminar – at Cambridge – to immortalise the speakers; inspire members who did not attended; and reawaken fond memories among those who did.
Optimizing portfolios using normalised returns delivers a factor-like awareness of underlying exposures while addressing the problem of sensitivity to model inputs with relative risk tolerance.
Explaining the returns of trend-following systems and analysing their diversifying properties.
The LQG will debate the motion: This house believes that: “This time is different”. AI and other technologies will boost global growth and justify current asset prices.
The LQG 2024 Autumn Seminar – 11 great speakers and topics – in its 38th year.
A framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning
Evaluating and comparing risk model performance for risk forecasting, portfolio construction and return attribution – a comparison framework.
Ben introduces Causal AI techniques with examples and then discusses applications and the assumptions, limitations and weaknesses to separate success potential from possible or probable pitfalls.
This paper models weights and parameters as uncertain. Evaluating a portfolio across the range of possibility measures risks better and surfaces latent fragility. Contributions to risk are reported with a center and spread.