A dynamic morning where the brightest minds from academia and the world’s leading quantitative finance teams unveil groundbreaking strategies and fresh innovations. This isn’t just a lecture — it’s a conversation. In the lead-up to the event, you’ll have the opportunity to submit your questions and steer the discussion, making sure the topics you care about are front and centre.
Overview objectives:
In this session, Dr. Oleksiy Kondratyev, Visiting Professor at Imperial College London and Risk Magazine's 2018 Quant of the Year, will delve into the forefront of quantum machine learning (QML) and its transformative impact on systematic investment strategies.
Drawing from his extensive experience in quantitative finance, Dr. Kondratyev will explore:
This session explores novel modelling approaches to speculative asset pricing, distinguishing between classic bubbles and more subtle "balloon-like" behaviour—characterized by rapid rises and gradual declines. Using high-frequency Bitcoin data, Andrew Harvey presents a series of score-driven and quasi score-driven models that integrate volatility, non-normality, and dynamic tail behaviour. The findings challenge traditional bubble narratives and offer practical tools for forecasting and risk modelling in speculative markets.
Overview objectives:
The commodities market present unique opportunities and risks requiring specialised strategies and deep market insight.
In this presentation, we’ll cover opportunities in currency options going well beyond volatility carry. FX options markets provide unique opportunities including trades in the correlation and forward start domain which the quant investor can harness systematically while limiting exposure to traditional risks.
As markets become more volatile and data-driven, investment leaders must strike a delicate balance between front-office agility and back-office resilience—while unlocking the full potential of portfolio optimization. This panel explores how technology, structure, and process design can shape stronger investment outcomes.
Join this session to:
This talk presents a quantitative trading approach grounded in probability theory and inspired by concepts from statistical mechanics. We explore how models like the Boltzmann equation and probability distributions inform market modelling and options pricing.
Insight will be given from the US and UAE markets, illustrating the practical implementation of long/short and machine learning-based strategies, highlighting performance and risk-adjusted returns.
Areas for consideration:
Foundations of predicting and modelling market behaviour
· The role of probability in systematic trading:
· Modelling market variables using functions of random variables
The Boltzmann Equation in Finance:
· Unified integral formulation adapted from statistical mechanics
· Practical Applications in Options Trading
Tested Prop Strategies:
US Markets:
· Long/short equity strategy based on the Boltzmann-driven trading framework
· Performance overview including Sharpe ratio, drawdowns, and backtest insights
UAE Markets:
· ML-based and long/short strategies adapted to lower-liquidity environments
· Feature selection, model performance, and risk control
Overview:
This advanced, interactive session dives into building robust strategies that survive changing regimes, liquidity shocks, and structural breaks. Participants will work in peer teams to reverse-engineer failure points in popular strategy types (e.g. momentum, stat arb, macro), then apply alternative risk frameworks and synthetic data stress-testing to strengthen model resilience.
Structure:
Part 1: Failure Mode Breakdown
A lead quant shows real examples of how well-performing strategies break during volatility spikes, macro shifts, or data leakage
Part 2: Group Diagnostic Labs
Groups are assigned different strategy types with embedded weaknesses; their task is to identify fragilities and propose mitigations using noise injection, adversarial scenarios, or market regime labeling
Part 3: Cross-Team Stress Test
Teams test each other’s improved strategies under stress scenarios (e.g., flash crash, rate shock, liquidity freeze)
Part 4: Wrap-up Discussion
Best practices in scenario generation, model robustness, and beyond-traditional VaR approaches
Takeaways:
In today’s data-driven market, fixed income and FX strategies are rapidly evolving so investment approaches must be reshaped:
Panel questions:
This session will explore the development of sophisticated climate risk models to tackle the economic challenges posed by climate change.
· What are the key challenges in integrating climate risk factors into traditional asset pricing models, and how can new financial instruments be developed to address these gaps?
· How can asset managers and hedge funds better align their strategies with sophisticated climate risk models rather than relying on simplistic ESG factor models?
· How can novel model concepts—such as fundamentals-based climate risk frameworks—reshape ESG investment strategies, and what opportunities do they unlock for alpha generation and risk hedging?
Overview: From Signals to Strategy: Unlocking Alpha through data innovation and machine learning
The explosion of data and advances in machine learning are transforming how quants identify and act on signals. This session delves into the practical techniques being used to drive alpha-from smarter signal extraction and model refinement to the strategic deployment of AI. We'll also explore how external macro and geopolitical factors subtly shape the datasets we rely on, and how leading firms are building adaptive models that account for this evolving landscape.
Key Questions:
· What are the most impactful techniques you’re using to extract signals from complex or unconventional datasets?
· How are you applying machine learning in a way that enhances—not obscures—strategic decision-making in alpha generation?
· In what ways do broader macro or geopolitical shifts manifest in the data you work with, and how do you factor that into your models?
This chat will explore the challenges, opportunities, and lessons learned in integrating technology and data infrastructure across the front, mid, and back-office functions in quantitative finance - with perspectives from both engineering and data users.
The session will examine how to break silos, streamline workflows, and improve the usability and consistency of data across the trade lifecycle.
Intro insight:
· Brief overview of how the front, mid, and back offices have traditionally operated in silos.
From the Engineering Perspective:
From a systems design point of view, what’s been the hardest part of aligning front-office speed with back-office stability?
From the Data User Perspective: Quality, Access, and Context
How often are quant models or risk signals impacted by incomplete or misaligned data from mid or back office?
Lessons Learned Across Front, Mid, and Back Office
What was one turning point in getting front-to-back integration right in your organization?
The Integration Frontier: What’s Next?
· What an ideal front-to-back data architecture looks like in 2025+.
· Explore the trade-offs between speed, control, and compliance in trading operations
Unlock the secret to better workplace communication and team performance.
Join us for an engaging workshop exploring how different behavioural styles impact professional relationships and team dynamics. You will discover practical strategies to enhance communication, improve collaboration, and create more effective working relationships.
What we will cover
In the ultra-competitive landscape of quantitative finance, top talent is the most valuable (and volatile) asset. But what truly makes a quant stay? In this session, senior leaders share how they’ve built high-performing, research-led teams—without relying solely on compensation. We’ll explore strategies to recruit exceptional minds, assess career pathways for existing team members, and create a culture where quants thrive long-term.
Through a blend of quant-industry insights and lessons from leaders, this session will challenge firms to think beyond salaries and bonuses and toward what actually drives loyalty, innovation, and performance.
Discussion Points:
· Talent without turnover: What Big Tech can teach quant firms about cultivating belonging, purpose, and professional growth
· Career architecture for quants: How to define and communicate clear development pathways for research, trading, and engineering roles
· Why people really leave: Exploring the impact of team dynamics, leadership, flexibility, and legacy tech on retention
· Hiring with intention: Balancing technical excellence with long-term culture fit when recruiting into high-performance environments