There are a multitude of approaches to integrating ESG into a firm’s investment process, and quantitative methodology is one. Quantitative (or systematic) investing uses advanced mathematical modeling, sophisticated computer systems and data analysis to calculate the optimal probability of executing a profitable trade.
At UBS-AM we have a dedicated team - Quantitative Evidence and Data Science (QED) team – that is responsible for integrating data science into our sustainable investment process. QED blends multiple data sources to create more holistic, efficient alpha and striving for better client outcomes, that are then refined by developing scoring and reporting tools.
Centralize SI data
- Early 2021, QED assumed responsibility for all data related to sustainable investing
- As a dedicated data science team, QED ensures that the investment business have industry leading data at hand
Enhance data and insights
- QED uses extensive machine learning expertise to enhance data and address data challenges
- Derive systematic insights which inform the investment decision process
- Enables faster idea generation, efficient assessment of investment value, and provides scientific justification for investment decisions
Drive innovation
- Machine learning and natural language processing is used to drive thematic, often SDG-related, investment strategies
- Driving thought leading work on impact with the ambition to establish investment framework
Team
- Dedicated team comprised of 16 professionals with strong investment experience and technical expertise, exhibiting deep knowledge of stock picking, portfolio management and analysis
- The team works directly across asset classes to communicate the drivers of new ideas once a month and review its relevance to their portfolio
Diversity and Inclusion
Diversity and Inclusion
QED built a proprietary approach that leverages natural language technology to “read” through thousands of revenue categories, company descriptions and corporate filings and then combines relevant topic exposures with alternative data and traditional ESG data to create a thematic universe.
Proprietary Carbon Blend
Proprietary Carbon Blend
QED built a statistical model that intelligently combines carbon footprint estimates from multiple vendors in order to more accurately predict the actual carbon footprint as opposed to relying on a single vendor that may include estimates as well.
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Introducing our leadership team
Meet the members of the team responsible for UBS Asset Management’s strategic direction.
