LQG 2026/09/09 to 2026/09/12 Autumn Seminar – Trinity College Oxford – IP *OLO* Hybrid

Large investors love committees. Committees signal seriousness, distribute blame, and produce copious minutes. Whether they actually improve portfolios is less obvious.
Investment committees (ICs) are ubiquitous in institutional asset management. Asset owners rely on committees to set strategic policy, approve tactical tilts, and demonstrate fiduciary process.
Asset managers often maintain ICs to deliver an in-house view, coordinate risk budgets, and reduce key-person dependence.
Committees sit at the intersection of portfolio choice and organisational design.
Despite their prevalence, the economic case for how committees should be structured—and whether they even should centralise investment decisions remains surprisingly underdeveloped.
In this talk Bernd will view and treat an IC as a technology used to aggregate information into a central portfolio. He will also ask how that technology performs under realistic frictions, including heterogeneous beliefs, career incentives, status hierarchies, and noisy deliberation.

The paper on which this talk will be based is available here.  

Head of Portfolio Implementation
Abu Dhabi Investment Authority (ADIA)

Bernd is a long term friend and frequent speaker at the LQG. Bernd is an experienced CIO and academic writer. He currently works as the Head of Portfolio Implementation at Abu Dhabi Investment Authority (ADIA), one of the world’s largest sovereign wealth funds.
He has more than 20 years of experience in senior management positions for various hedge funds, asset management companies, and banks in New York, London, Vienna, and Frankfurt, where he designed and implemented quantitative investment strategies, products and processes, and successfully reorganized investment teams. Bernd is also a former Professor of Finance at EDHEC Business School, where he taught and researched on asset pricing, machine learning, portfolio construction, and risk management. He published more than 80 peer-reviewed articles in leading academic and practitioner Journals and currently serves on the editorial board of the Financial Analysts Journal.

When Jason MacQueen owned and operated Quantec Investment Technology he ran his first investment seminar in 1986 – in Oxford.
40 years later it is called the London Quant Group.
As Jason was essentially the founder of the LQG it is only fitting that he deliver a talk at the 2026 seminar.
In a major break with tradition Jason will not discuss portfolio construction… he will convey the “Lessons learned in my life as a quant”.
Jason was 29 years old when he began his career. Like most 29 year olds, he thought he knew everything. Turns out he was wrong – surprise!
In this talk he will share some of the lessons learned over his career….
so far!

Jason’s slides are available by clicking here.

Jason MacQueen
Director of Research
Smart Portfolio Strategies

Jason MacQueen built the first equity risk models for all the developed markets outside the USA in the 1980s, and built the first Global risk model in the 1990s.
In 2000, he developed a patented risk-based technique which enabled the American Stock Exchange to offer Exchange Traded Funds (ETFs) on Actively-managed Mutual Funds, without knowing the underlying holdings.
In the 2000s he built Custom Hybrid Risk Models, including the Global Risk Model used by FactSet as the foundation of their Multi-Asset Class (MAC) product.
In the 2010s he developed a portfolio rebalancing algorithm to run Style factor strategies for Assetmark, growing their AUM from $260m to $2.38bn, a 9-fold increase in 4.5 years.
He is currently an Adjunct Professor at Fordham University in New York, where he teaches Masters in Quantitative Finance courses.
Naturally, very little of this was done on his own, and he is grateful for the support and help of various colleagues over the years. He is currently looking for his next challenge.
Jason has developed the theoretical framework of Markowitz and his successors into a practical set of tools for institutional fund managers. By his passionate pleas for a disciplined and logically coherent approach to portfolio management, he has acquired an international reputation as a speaker, consultant and iconoclast.
Jason began the QUANTEC Investment Seminars in 1986, which eventually metamorphosed into the London Quant Group, which therefore celebrates its 40th Anniversary this year, 2026.

Global investors may model the risk of their equity portfolios using one of two types of factor models. The first approach is a global equity model, which typically contains 100 or more factors (countries, global industries, and global styles). The second approach is an integrated model, which links together many local models (e.g., US, Japan, Europe) that collectively span the globe. Integrated equity models may easily contain over 500 local industry and style factors. In this presentation, we discuss some of the challenges involved in constructing integrated models and highlight the techniques used to solve them. We then conduct a systematic empirical analysis to compare the performance of global equity models versus integrated equity risk models. We find that integrated models typically provide more accurate risk forecasts and also produce more efficient optimized portfolios. Moreover, we find that integrated models typically provide more reliable representations of the sources of active return in performance attribution.

Head of Portfolio Analytics Research
Bloomberg

Jose Menchero serves as Head of Portfolio Analytics Research at Bloomberg. Jose and his team are responsible for developing the analytics and algorithms used for factor risk models, portfolio risk and return attribution, scenario analysis, tail risk, portfolio construction, and portfolio optimization.
Prior to joining Bloomberg, Jose was the founder and CEO of Menchero Portfolio Analytics Consulting. Before founding his consulting firm, Jose worked for eight years at MSCI, where he was Managing Director responsible for developing the Barra equity risk models, and for portfolio construction research. Jose also served for seven years as Director of Research at Thomson Financial, where he developed several risk and return attribution methodologies, as well as equity factor risk models.
Jose has over 30 finance publications in leading practitioner journals. Before entering finance, Jose was Professor of Physics at the University of Rio de Janeiro in Brazil. Jose holds a PhD in theoretical physics from the University of California at Berkeley, and a BS degree in aerospace engineering from the University of Colorado at Boulder. Jose is a CFA Charterholder.

2026/09/10 Thursday 13:45 – 14:45 Ronald Kahn Enhancing a Risk Model by Adding Transient Statistical Factors 

Global Head of Systematic Equity Research
BlackRock

Ronald N. Kahn, PhD, Managing Director, is Global Head of Systematic Equity Research at BlackRock. He has overall responsibility for the research underpinning the Systematic Active Equity (SAE) products.

His service with the firm dates back to 1998, including his years with Barclays Global Investors (BGI), which merged with BlackRock in 2009. Prior to joining BGI, he worked as Director of Research at Barra, where his research covered equity and fixed income markets.

Ronald Kahn is a well-known expert on portfolio management and quantitative investing. He has published numerous articles on investment management, and, with Richard Grinold, authored the influential book Active Portfolio Management: Quantitative Theory and Applications. The two of them are the 2013 winners of James R. Vertin award, presented periodically by the CFA Institute to recognize individuals who have produced a body of research notable for its relevance and enduring value to investment professionals. He is a 2007 winner of the Bernstein Fabozzi/Jacobs Levy award for best article in the Journal of Portfolio Management. He serves on the editorial advisory boards of the Financial Analysts Journal, the Journal of Portfolio Management and the Journal of Investment Consulting. The 2007 book How I Became a Quant includes his essay describing his transition from physics to finance.

He earned an AB degree in physics, summa cum laude, from Princeton University, and a PhD in physics from Harvard University. He was a post-doctoral fellow in physics at University of California, Berkeley.

AI agents participate in both rational and irrational bubbles. In a sequential bubble game, AI agents generate speculative trades even though there is a unique no-trade equilibrium. Increasing reasoning capacity reduces, but does not eliminate, such irrational bubbles. Chain-of-thought analysis shows that AI agents participate in irrational bubbles because they form incorrect beliefs through simplified expectations of the market, and they are sensitive to framing effects when forming their beliefs. When the environment admits both a no-trade equilibrium and a speculative bubble equilibrium, AI agents recognize this multiplicity and coordinate on the speculative equilibrium. Our results suggest that greater AI sophistication does not necessarily eliminate bubbles or improve market efficiency.

Alvaro’s slides can be accessed by clicking here.

Director
Oxford-Man Institute of Quantitative Finance

Álvaro Cartea is Professor of Mathematical Finance at the Mathematical Institute, University of Oxford, director of the Oxford-Man Institute of Quantitative Finance, and a Fellow in Mathematics at St Hugh’s College, Oxford. Before joining Oxford, Álvaro was Reader in Mathematical Finance at University College London. Earlier in his career, he held the JP Morgan Lecturer in Financial Mathematics, Exeter College, University of Oxford. Álvaro obtained his doctorate from the University of Oxford in 2003.

The Vivaldi of Finance – An appreciation and critical assessment of the life and works of Jason MacQueen

Head of Research for the Risk and Quantitative Analysis Group
BlackRock

Ed Fishwick, Senior Managing Director, is the The Head of Research for the Risk and Quantitative Analysis Group (RQA) at BlackRock.

Mr. Fishwick previously served as Chief Risk Officer and Global Head of RQA for four years, and before that as Global Co-Head of RQA for 15 years. His service with the firm dates back to 2003 including his time with Merrill Lynch Investment Management (MLIM) prior to its acquisition by BlackRock in 2006. At MLIM he was head of Risk and Quantitative Analysis, EMEA.

He is the Chairman of the London Quant Group.

Fundamental risk models are widely used for risk forecasting and portfolio optimisation. However, reliably estimating factor volatilities for these models is inherently challenging. Traditional approaches rely exclusively on historical returns, which gives rise to systematic biases: they tend to underforecast risk at the onset of crisis periods, when volatility spikes, and overforecast it during the subsequent normalisation, causing those risk models to react too slowly to regime shifts. To address this shortcoming, we move beyond purely return-based methods and incorporate information from additional conditioning variables, such as the option-implied market volatility, to better capture changes in the risk environment. In particular, we propose to estimate factor volatilities as a dynamic weighted average of multiple baseline volatilities, with blending weights determined by a machine learning (ML) model. Overall, we find that this ML-based framework can adjust the responsiveness of volatility estimates to changing market conditions while avoiding excessive noise in stable periods, resulting in lower bias and higher accuracy, especially during periods of market stress.

Portfolio & Risk Analytics Researcher
Bloomberg

Tizian Otto, Ph.D., is a Researcher in the Portfolio & Risk Analytics Team at Bloomberg in London where he is leading the acceleration of machine learning adoption.
Prior to that, he was a Research Fellow at Yale University as well as a Visiting Research Scholar at Stanford University and Harvard University.
During his doctoral and postdoctoral studies, Tizian worked on the development of machine learning-based models to forecasting return, risk, and liquidity in different financial markets. Holding a Ph.D. in Finance from the University of Hamburg, Germany, he reviewed and published several articles on this topic in leading academic and practitioner journals.

For decades, finance has relied on classical statistical models which are powerful, but limited in capturing the unruly realities of human behaviour and global markets. Current AI developments are changing that landscape: they can read markets with a nuance once unique to human analysts, and do so at a scale no analyst could match. This talk moves beyond the buzzwords to examine what has actually changed, and what is possible. It rests upon three key components under the bonnet of AI: tensor-based methods for large-scale data analytics, graph structures for capturing domain knowledge, and foundation models for language and reasoning. Together, they enable AI to read and interpret the constant stream of global financial events and news in real time, and to connect raw numbers to the events driving them.
None of this is unambiguously good news. The same capabilities that let AI solve harder problems can, if left unchecked, produce opaque systems whose reasoning we cannot audit and whose failures we cannot anticipate. The talk closes on what “beyond the hype” in finance really requires: not just bigger models, but explainable ones that protect sensitive financial data and support decisions that can be trusted, understood, and managed – because in finance, a decision we can’t explain is a risk we can’t manage.

Professor Mandic’s arXiv page can be found here:

The slides can be accessed by clicking here – this is a huge file so may take time! 

Danilo Mandic
Professor of Machine Intelligence
Imperial College London

Danilo P. Mandic is a Professor of Machine Intelligence with Imperial College London, UK, and has been working in the areas of machine intelligence, statistical signal processing, big data, and data analytics on graphs. He is a Fellow of the IEEE and past President of the International Neural Network Society (INNS). Dr Mandic has written five books, more than 700 articles and holds eight patents. He serves on the Council of Experts, within United Nations International Council on Environmental Economics and Development (ICEED).

Wuyang Zhou
Ph.D. Candidate and Lead Research Scientist in the Artificial Intelligence and Data Analytics Lab
Imperial College London

Wuyang Zhou received his M.Eng. degree in electrical and electronic engineering from Imperial College London. He is a Ph.D. Candidate and Lead Research Scientist in the Artificial Intelligence and Data Analytics Lab at Imperial College London, supervised by Prof. Danilo Mandic. His research interests include efficient, scalable methods for large language models & financial NLP and quantum-inspired tensor networks.

Giorgos Iacovides
Ph.D. Candidate and Lead Research Scientist in the Artificial Intelligence and Data Analytics Lab
Imperial College London

Giorgos Iacovides received his M.Eng. degree in electrical and electronic engineering from Imperial College London, with a year abroad at MIT. He is a Ph.D. Candidate and Lead Research Scientist in the Artificial Intelligence and Data Analytics Lab at Imperial College London, supervised by Prof. Danilo Mandic. His research interests include financial NLP, graph-based portfolio optimization, and tensor network models.

Hundreds of factors, and countless ways to combine them. The temptation is to surrender to the machines and let a black box hand you portfolio weights, with little sense of what drives them or when they might break. We go the other way, back to first principles: starting from the single pricing kernel that should price every asset, disciplined Bayesian model selection and aggregation turn the zoo from a statistical problem into an economic one. In the academic wonderland the payoff is striking: high but believable Sharpe ratios across stocks and corporate bonds. But does it survive the real world? We restrict to liquid names, charge realistic trading and price-impact costs at scale, and rebalance in real time. It does: the strategy stays market-neutral, with attractive Sharpe ratios, low turnover, and stable, scalable positions.

Christian’s slides are available by clicking here.

This talk comes with questions for attendees to consider which are available here AliceZoo_Questions:
or here: Link to Alice Zoo questions

OR:

Background readings for the talk:
1. Bayesian Solutions for the Factor Zoo (JF2023): https://onlinelibrary.wiley.com/doi/10.1111/jofi.13197
2. The Co-pricing Factor Zoo (JFE2026): https://doi.org/10.1016/j.jfineco.2026.104295

Associate Professor in the Department of Finance
Senior research associate of the Financial Market Group and the Paul Woolley Centre
Programme director of the Systemic Risk Centre
London School of Economics

Christian Julliard is an Associate Professor in the Department of Finance at the London School of Economics. He is also a senior research associate of the Financial Market Group and the Paul Woolley Centre, and a programme director of the Systemic Risk Centre. He has been the Finance PhD Programme director. He is also a research affiliate of the Financial Economics programme of the Centre for Economic Policy Research and has been a visiting assistant professor of economics and finance at the Tepper School of Business at Carnegie Mellon University.
His research interests span finance, macroeconomics, networks, and the frontier of applied econometrics.
His research has been widely published including in the Journal of Political Economy, The Journal of Finance, The Review of Financial Studies, the Journal of Financial Economics, Management Science, the Journal of International Economics, the Journal of Econometrics. He is/has been on the editorial boards of The Review of Economic Studies, the Journal of Empirical Finance and Economica.
He was awarded an M.Sc. and a Ph.D. in Economics by the Economics Department at Princeton University. He received a B.A. in International Economics and Business Studies from the University of Naples Partenope, an M.A. in Economics and Finance from the University of Naples Federico II, and a Dottorato di Ricerca in Public Economics from the University of Salerno.

2026/09/10 Friday 14:00 – 15:00 Pedro Castro The Architecture of Robustness

Managing Director and Head of Core Portfolio Management
Man Group

Pedro is a Managing Director and Head of Core Portfolio Management, responsible for portfolio construction, research and management for all Solutions’ multi-strategy products and bespoke client mandates.
Pedro joined Man Group in 2024 as Head of Portfolio Engineering. Prior to this, he was Director of Risk and Quantitative Analysis at BlackRock, covering Multi-Asset Strategies and Solutions for 13 years. He was also responsible for driving the adoption and evolution of BlackRock’s strategic portfolio construction research across their investment and consulting platforms. Before that, he was a finance assistant lecturer at the University of Warwick.
Pedro holds a MSc in Finance and Economics, with distinction, from Warwick Business School and a BSc in Economics from Nova SBE. He is a CFA charterholder and a certified Financial Risk Manager (FRM).

Quant:  The Truth

Head of Research for the Risk and Quantitative Analysis Group
BlackRock

Ed Fishwick, Senior Managing Director, is the The Head of Research for the Risk and Quantitative Analysis Group (RQA) at BlackRock.

Mr. Fishwick previously served as Chief Risk Officer and Global Head of RQA for four years, and before that as Global Co-Head of RQA for 15 years. His service with the firm dates back to 2003 including his time with Merrill Lynch Investment Management (MLIM) prior to its acquisition by BlackRock in 2006. At MLIM he was head of Risk and Quantitative Analysis, EMEA.

He is the Chairman of the London Quant Group.