Machine learning has become increasingly central to both forecasting and portfolio construction. But how do you separate hype from real, actionable value?

• What ML models are most effective for return and risk forecasting?
• How are firms integrating AI and ML signals into quant investment strategies?
• When does ML add value over traditional quant techniques?

Traditional risk models often fall short in capturing the complex dynamics across multiple asset classes, regions, and liquidity profiles. Gain insights into:

• The development and implementation of functional, adaptable, data-driven risk models
• Cross-asset volatility, as well as fact and correlation modelling.
• Practical challenges in stress testing and scenario analysis.

Panel Questions:

• How can we develop and implement functional, adaptable, data-driven risk models that effectively capture the complexities across multiple asset classes, regions, and liquidity profiles?
• In the context of cross-asset volatility, what are the best practices for factor and correlation modeling to ensure accurate risk assessment and management?
• What are the practical challenges faced in conducting stress testing and scenario analysis, and how can these be addressed to improve risk model robustness?
• Given the dynamic nature of financial markets, how can risk models be designed to adapt to changing conditions and provide reliable insights during periods of market stress?

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