That’s why we launched our ‘Innovation Superstars’ research series last year, to better understand this process and to help us quickly spot success stories.
In the first instalment – Identifying the most productive US-listed firms – we used productivity as a way to benchmark the effects of innovation across firms in the Russell 3000 Index. This uncovered large, and sustained, differences between innovators and laggards.
For the second paper in this series – The search for clusters of innovators – we compiled a bespoke database of innovation indictors, using millions of datapoints, to better understand the similarities across the most innovative US companies.
This is what we found.
Bringing together innovation proxies
We looked at a range of innovation indicators for individual firms in the Russell 3000 Index over a 20-year period:Source: OECD, Worldscope, Refinitiv, abrdn, as of 2019
For example, ‘Patent machines’ and ‘Innovation all-rounders’ boast the highest productivity, but their returns profile does not differ much from other clusters. At the other end of the spectrum, ‘Inefficient innovators’ are least productive, but returns aren’t that much different either.
It is likely that other firm characteristics, such as their size, the sector they operate in, or external factors like business cycle effects, are clouding this relationship.
It is likely that other firm characteristics, such as their size, the sector they operate in, or external factors like business cycle effects, are clouding this relationship
This means a more sophisticated analysis will be needed to explain the relationship between productivity and returns.
Final thoughts
Our database of innovation indicators helps shine a light on those firms working hardest to deliver breakthroughs – with many of them outperforming across traditional metrics such as investment in R&D and intangible assets, patent quantity and quality.
Importantly, these companies are often the most productive, hinting at a tangible payoff from innovation efforts in the form of corporate performance. However, this relationship isn’t perfect.
Company rankings across our innovation indicators have been relatively stable over the past two decades. This suggests a strong culture of innovation within those firms trying to innovate most intensively, which is hard to replicate elsewhere. This makes it important for investors to identify these ‘Innovation Superstars’.
Less clear is the relationship between innovation activities and returns. In our next paper, we will deepen the analysis by testing the relationship between innovation activities, productivity and returns using a more sophisticated model which allows us to control for additional factors, such as other firm characteristics and the business cycle.
That study lays the foundation for the abrdn Innovation Index, which we hope will help us to identify the next generation of ‘Innovation Superstars’.