The commodities market present unique opportunities and risks requiring specialised strategies and deep market insight.

• Explore the risk-return profile of private commodity investments like energy, metals, and agriculture
• Understand key challenges around pricing, liquidity, and operational risk
• Learn how investors and funds are accessing and managing exposure in the commodity markets

Aleksandar Subotic

Quantitative Research & Alpha Capture
BP Trading

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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.

Caio Natividade

Managing Director - Global Head of Quantitative Investment Solutions Research
Deutsche Bank

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Francesco Pozzetti

Vice President, QIS Research
Deutsche Bank

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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:

• Explore how technology, automation, and real-time data integration are reshaping the investment lifecycle
• Understand how to structure portfolios to maximize risk-adjusted return in uncertain markets
• Learn how to build robust, resilient, and continuously optimized investment processes
• Assess how much portfolio optimization truly improves the investment process - and where the untapped gains lie

Bruno Pajusco

Founder and CEO
Keridion Capital

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Artur Sepp

Global Head of Investment Services Quant Group
LGT Bank

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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:

• Advanced strategy diagnostics and regime detection
• Adversarial testing techniques and synthetic market scenario building
• Collaborative critique and resilient model innovation

Paul White

CEO and Co-Founder
QuantBot

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In today’s data-driven market, fixed income and FX strategies are rapidly evolving so investment approaches must be reshaped:

•  Explore how data analytics, machine learning, and alternative data are transforming fixed income and FX investment strategies

Panel questions:

1. How can a bottom-up, factor-driven approach enhance alpha generation and risk management in fixed-income portfolios?
2. What are the key challenges in identifying and applying systematic factors within diverse fixed-income asset classes?
3. With quant factors, how do you set up portfolio construction?
4. Can you explain, in your own words, the role of automation and AI in optimizing execution and managing risk in these traditionally more stable markets?

Hamza Chaudhry

Fixed Income Quantitative Researcher
AllianceBernstein

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Alexandra Billon

Managing Director - Global Head of Portfolio Analytics Group /Fixed Income
Blackrock

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Justin Xu

MD Head of Investments
Millenium Global

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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?

Antonia Lim

CIO
Impact Cubed

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Dr Christopher Cormack

Associate Business Fellow
UK Centre for Greening Finance and Investment

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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?

Stephan Kessler

Global Head of Quant Research - MD
Morgan Stanley

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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.

• Why fragmentation leads to latency, reconciliation errors, inconsistent datasets, and missed alpha opportunities.
• How quant-driven organizations are uniquely positioned to benefit from tighter integration.

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

Vlad Mereuta

Head of Risk Engineering
Man Group

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Vincenzo Pota

Data Scientist - Director
Barclays Capital

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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

• The Four Behavioural Styles: Understand Dominant/Direct, Influential/Interactive, Steady/Supportive, and Conscientious/Cautious approaches
• Communication Strategies: Learn how to adapt your communication style to connect effectively with colleagues, clients, and team members
• Team Building: Discover how to leverage different behavioural strengths and create balanced, high-performing teams
• Leadership Skills: Develop techniques to motivate and manage different personality types more effectively

Ras Gohil

Senior Manager, Corporate Partnerships
Women in Banking & Finance (WIBF)

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