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?