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?
How are quant managers navigating current volatility? A deep dive into instruments, hedging techniques, and risk modelling approaches.
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
A practical exploration of how firms are constructing portfolios across asset classes while optimising for risk, cost, and return.
- Raphaël Douady - Research Professor, University of Paris 1 Pantheon Sorbonne
How are systematic ETF strategies being used for liquidity, factor exposure, and tactical tilts?
- Aous Labbane - Founder and CEO, Jasmine Capital Consulting
- Deborah Fuhr - Managing Partner & Founder, ETFGI
- 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?
- Jun Yuan - Managing Director, Global Risk Analytics, Royal Bank of Canada
- Chris Kenyon - Global Head of Quant Innovation, MUFG Securities
- 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
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?
- Marcos Carreira - Head of Quants, XP Inc
- Leon Tatevossian - Adjunct Professor, New York University
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?
- Stefano Iabichino - Strategy Quant, UBS
What deserves in-house investment versus vendor outsourcing in today’s quant stack?
- Nicole Königstein - Chief AI Officer, Head of AI & Quant Research, quantmate