Nasdaq has partnered with Yewno, a provider of AI-driven solutions to the financial sector, to launch a new suite of indices targeting firms that are highly exposed to a range of innovative and disruptive technologies.
The new indices provide exposure to 35 innovative and disruptive technology themes.
The indices capture 35 technology themes that the partners believe are poised to disrupt the real economy.
These include blockchain, 3D printing, satellites, solar power, and electric vehicles, among others.
“Emerging and disruptive technologies have developed into a powerful force in the modern economy, driving change at a rapid pace,” said Dave Gedeon, Vice President and Head of Product Development and Research for Nasdaq Global Indexes. “Partnering with Yewno in creating new indexes will enhance benchmarking and classification of companies engaged in emerging and disruptive technologies across all industries.”
Nasdaq will run and calculate the indices in real time and liaise with asset managers on product development opportunities such as ETFs.
To create the indices, Yewno aggregates a large amount of structured and unstructured data, identifying key concepts inferentially related to each technology, then leveraging its artificial intelligence framework to correlate them to public companies.
“Yewno is able to surface connections between increasingly fragmented, structured and unstructured data sets, using our proprietary dynamic knowledge graph,” said Yewno CEO Ruggero Gramatica. “As we tailor products for the financial services space, we are deeply engaging with key partners such as Nasdaq to build solutions that are both innovative and actionable.”
Nasdaq applied the methodology to their global Nasdaq NQGI Index. The result is coverage of over 1,000 listed disruptive technology companies. Constituent weightings are assigned based on each entity’s level of involvement in disruptive technologies.
The initial indices are:
Nasdaq Yewno Global Disruptive Technology Benchmark Index
Nasdaq Yewno Global Innovative Technologies Index Family