Thesis’s Thematic Intelligence Platform helps index providers build differentiated thematic rotation indices on top of their existing index ecosystem, creating a new, adaptive product designed to retain AUM in-family.
When a single theme cools, our deep-learning analysis helps providers keep assets in-family with a stronger thematic destination.
When a single-theme product weakens, assets often leave the ecosystem or migrate into lower-fee products.
We analyze existing thematic indices using our deep-learning architecture, then select, weight, and rotate across them to create a composite rotation index for an ETF or other product wrapper.
The result is a differentiated thematic product designed to attract additional AUM and reduce AUM lost to thematic fatigue.
A rare combination of thematic index experience, machine learning in index design, and deep-learning-based rotation and risk management.
Experience building institutional thematic indices and working within the governance and licensing structure around them.
Experience applying machine learning to index design with an emphasis on methodology, documentation, and auditability.
Experience developing and deploying deep-learning-based rotation and risk-management systems for hard-to-time sleeves, enabling a more adaptive methodology than static weighting, discretionary rotation, or simpler rules-based overlays.
Ready to Tackle Thematic Fatigue?
We work with thematic index providers, custom-index firms, ETF issuers, and adjacent ecosystem partners building more adaptive thematic products.