Preconference Day: Summits & Workshops - GMT (Greenwich Mean Time, GMTZ)
- Aous Labbane - Founder and CEO, Jasmine Capital Consulting
A big picture look across asset classes. How are allocators and managers adapting to market shifts, evolving risk dynamics, and the rise of quant integration in multi-asset portfolios?
- Bohumil Vosalik - Chief Investment Officer, 319 Capital
How are investors implementing AI successfully? A deep dive into model development, data infrastructure, back testing frameworks, and real-world performance attribution.
- Robert van Kleeck - Managing Director, Head of Credit Portfolio Management, Assenagon Asset Management
How are quants adapting to tail risk in a year of sharp market moves? Lessons in stress testing and model resilience.
- Barney Rowe - Senior Quantitative Analyst, Fidelity International
- Christopher Cormack - Honorary Research Fellow, University College London
A practical exploration of how firms are constructing portfolios across asset classes while optimising for risk, cost, and return.
At mid-frequency and with meaningful capacity, a systematic crypto strategy is effectively trading a single asset, BTC or ETH, with only a few years of usable history. There is no cross-section to average away noise, so overfitting becomes the central problem. This talk walks through a strategy step by step, from idea to live trading, using the single-asset setting as the hardest possible test of whether an edge is real. We look at where an idea and its data come from; how a deliberately simple signal is built and sized, including the practical choices that quietly move PnL; how to tell whether a Sharpe is genuine, through parameter sensitivity, behaviour across coins, and a Monte-Carlo test on a single price path; and what changes in production. The methods are shown on real signals and carry over to any market with a short history and few instruments, in or out of crypto.
- Adrien Antonov - Portfolio Manager, Edo Theory
How are systematic ETF strategies being used for liquidity, factor exposure, and tactical tilts?
- Aous Labbane - Founder and CEO, Jasmine Capital Consulting
A big picture look at the technology stack powering modern quant finance. How are firms leveraging machine learning, quantum computing, GPU acceleration, and cloud infrastructure to navigate complexity, scale operations, and stay ahead in an increasingly data-driven landscape?
- Federico Fontana - Chief Technology Officer, XAI Asset Management
- Jun Yuan - Managing Director, Global Risk Analytics, Royal Bank of Canada
- Chris Kenyon - Global Head of Quant Innovation, MUFG Securities
- Brief introduction to RAG systems
- Metrics and automated evaluation frameworks
- Application to a use case
- Threshold calibration and monitoring
- Key conclusions
- Eulogio Miguel Cuesta - Head of the Internal Audit Team of Quantitative Analysis, Santander
- Daniel Mayenberger - Head of Quants Markets Solutions – Digital Products, J.P. Morgan
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
An overview of deep neural networks, activation functions, loss functions and training algorithms. Practical implementation in Pytorch.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Unsupervised hedging and pricing of derivatives, high-frequency asset return prediction.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Deep learning volatility and calibration of (rough) stochastic volatility models.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
Market generators and generative AI.
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
- Mikko Pakkanen - Reader in Data Science and Quantitative Finance, Imperial College
- Julien Guyon - Professor of Applied Mathematics, ENPC, Institut Polytechnique de Paris, and Visiting Associate Professor, NYU Tandon
- The different types of volatility and volatility derivatives
- The volatility smile and the term-structure of equity at-the-money skew
- Stylized facts of volatility
- Volatility modeling: a brief history
- Black-Scholes: P&L analysis and an insightful hedging quiz
- Links between spot volatility, local volatility, and implied volatility
- Static v. dynamic properties of volatility models
- Local volatility
- Julien Guyon - Professor of Applied Mathematics, ENPC, Institut Polytechnique de Paris, and Visiting Associate Professor, NYU Tandon
- Stochastic volatility models
- Variance curve models
- The smile of stochastic volatility models
- Stochastic local volatility
- Rough volatility
- Julien Guyon - Professor of Applied Mathematics, ENPC, Institut Polytechnique de Paris, and Visiting Associate Professor, NYU Tandon
- Empirical evidence
- Path-dependent volatility models in continuous time
- Path-dependent volatility models in discrete time
- Julien Guyon - Professor of Applied Mathematics, ENPC, Institut Polytechnique de Paris, and Visiting Associate Professor, NYU Tandon
- Stochastic local volatility: The particle method for smile calibration
- Calibration of multi-asset volatility models: local volatility/correlation, cross-dependent volatility/correlation
- Exact joint S&P 500/VIX smile calibration by entropy minimization
- P- and Q-calibration of path-dependent volatility models
- Julien Guyon - Professor of Applied Mathematics, ENPC, Institut Polytechnique de Paris, and Visiting Associate Professor, NYU Tandon
- Julien Guyon - Professor of Applied Mathematics, ENPC, Institut Polytechnique de Paris, and Visiting Associate Professor, NYU Tandon
2025 hackathon topic below - 2026 coming soon!
Description:
Hackathon participants will work to create prompts that prevent cognitive biases of AI from affecting the result across four categories of tasks.
Each task will be designed to trigger one or more of the cognitive biases and psychological effects in AI.
Biases and Psychological Effects:
The participants will design an approach to counter the following biases and psychological effects in AI:
- Confirmation Bias
- Truth Bias
- Framing Effect
- Priming Effect
- Informational Anchoring
- Priming-Induced Anchoring
Categories
1. Sentiment Analysis (predict the impact of a news headline on stock prices)
2. Regulatory Compliance (determine compliance or noncompliance with a regulatory clause)
3. Document Evaluation (evaluate the quality of a paragraph on the scale from 0 to 100)
4. Classification (assign one of several possible classes based on class descriptions)
Prizes:
There will be a prize for the best result in each category, as well as the Grand Prize that will be awarded to the best result across all four categories.
Notes:
The winning entries will be profiled in Alexander Sokol's AI workshop on Tuesday.
Scoring:
Before the competition, 50% of the tasks will be randomly assigned to the public dataset for use during the hackathon, and the other 50% will be used for scoring. Participants will use the public dataset to create a prompt for each of the competition categories they choose to participate in.
For scoring, each task in the scoring dataset will be presented in a way that triggers one of the cognitive biases and psychological effects in AI. The participant's prompt will be combined with the task, and the results will then be scored by running statistical analysis for the magnitude of bias.
Participants are free to use coding or any external tools (including proprietary tools) for prompt development, or develop their prompts using ChatGPT or any other software.
A tool from CompatibL for scoring the public dataset will be made available during the hackathon online and as an open source package on GitHub. The use of this tool is optional.
To prepare:
The participants are encouraged to review the book "Thinking, Fast and Slow" by Daniel Kahneman and other literature on cognitive biases and psychological effects.
- Alexander Sokol - Executive Chairman, CompatibL
Markets remain volatile, regulation are tightening, and alpha is harder to source. How are peers adapting strategy, risk budgets, and execution tools across asset classes?
Can AI truly enhance financial intelligence or simply amplify systemic risk? In other words, will machines help us manage risk better, or will they hallucinate us into the next crisis?
What deserves in-house investment versus vendor outsourcing in today’s quant stack?