Webinar recording: Harnessing scenario-based machine learning (SBML) for private asset investment decisions

Private assets are increasingly playing a significant role in institutional portfolio optimization, offering diversification and attractive return potential alongside traditional public investments.

Institutional investors face industry-wide challenges due to the fundamental differences between private and publicly listed assets and their impact on modeling approaches that underpin investment decision-making. To address these challenges, many are turning to innovative technologies, such as scenario-based machine learning (SBML), to develop more resilient and robust investment strategies.

In this webinar, Saiyan Raya, Investment Solutions Director and Hens Steehouwer, Chief Innovation Officer discussed:

• The challenges with portfolio optimization in today’s institutional investment landscape 
• How SBML can address the unique complexities of private assets, including illiquidity and broader portfolio risk implications 
• How SBML can be applied to build optimal portfolios using a real-world case study, highlighting a three-dimensional lever approach across multiple portfolios with varying risk, return, and liquidity objectives 

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Additional private asset webinars

This webinar is part of our five-part ‘Private Assets in focus: Navigating investment decisions’ webinar series, learn more about the other topics here.

Empowering investment decisions across private assets

We offer extensive capabilities in risk and return scenario modelling, pacing and liquidity risk management, climate scenario analysis, and performance attribution help pension funds, insurance companies, sovereign wealth funds, and asset managers navigate private market complexities, and address its fundamental differences from publicly listed markets.

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