The private 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

In a fast-moving market environment, firms are under pressure to innovate at the front while maintaining rock-solid infrastructure in the back:

· Discover strategies for aligning flexible front-office tools with reliable back-office systems

· Learn how firms are managing real-time data, automation, and integration challenges

· Explore the trade-offs between speed, control, and compliance in trading operations

This interactive session walks through a real trade gone wrong to uncover where breakdowns happen and how to fix them

· Follow a trade from execution to settlement and see where misalignments occur

· Learn how communication gaps between desks, systems, and teams lead to costly errors

· Explore practical solutions for improving workflows, reconciliation, and exception handling

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

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?

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?

As technology becomes more complex, integrating systems across offices is crucial to ensure smooth operations and mitigate risk. This session explores strategies for closing the gaps:

• Discover best practices for achieving seamless data flow and system integration across all offices
• Learn how real-time collaboration and automation can eliminate operational inefficiencies
• Understand the challenges and solutions for maintaining regulatory compliance while integrating tech

As quantitative finance evolves, so too must the talent and skills needed to drive innovation.

• Explore the shifting skill sets required for success in the evolving quant landscape
• Learn how firms are adapting hiring strategies to attract and retain top talent in a competitive field
• Understand the role of interdisciplinary skills, such as machine learning and data science, in modern quant finance

As foundation models like GPT, Claude, and Gemini enter finance, the next quant frontier is extracting latent signals from massive unstructured datasets. This masterclass explores how LLMs can be fine-tuned and interrogated to extract market-moving insights from earnings calls, filings, news flow, and more. Participants will prototype alpha pipelines using LLM outputs and debate their interpretability, drift, and compliance viability.

Structure:

Part 1: Applied LLM Briefing

Walkthrough of practical use cases: fine-tuned models for sentiment extraction, risk signal detection, and meta-feature creation

Part 2: Group Alpha Sprint

Teams receive an unstructured dataset (e.g., snippets from 10-Ks, call transcripts, macro headlines) and a mission: generate 1–2 usable features or signals using a foundation model output (pre-provided or simulated)

Part 3: Debate Round

Teams justify their signal’s validity, interpretability, and potential drift exposure; the group votes on robustness

Part 4: Expert Close

Discussion on prompt engineering, retraining pipelines, and alpha lifecycle for LLM signals

Takeaways:

• Practical LLM integration in quant workflows
• Feature engineering from unstructured sources
• Risk assessment of opaque models in a high-compliance context