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Jupiter Asset Management

PRI reporting framework 2020

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Innovation

SG 18. Innovative features of approach to RI

18.1. Indicate whether any specific features of your approach to responsible investment are particularly innovative.

18.2. Describe any specific features of your approach to responsible investment that you believe are particularly innovative.

Innovation can take various forms and over the period we have allocated additional resource and investment into data science and applied this focus to ESG data. The commentary below does not refer to new product lines or a complete dataset. However, interestingly, we wish to highlight our skills and innovative approach of working behind the scenes to highlight issues with ESG data.

The innovation around this is uncovering aspects which have evaded data providers using our data science resource. This is not a critique of our data providers, but it very much showcases our partnership ethos.

ESG data is heralded as a tool to be able to help efficiently monitor portfolios and draw out themes for engagement. Asset managers readily discuss the benefits and their own innovations in this regard. However, the whole area is not without its critics i.e. is the data simply backward looking? How accurate is the data? Is it too slow moving? And why is there little correlation between providers?

Internally, we are developing an internal ESG data portal to provide our investment teams with a dashboard to identify ESG risks in our portfolios. The project is being developed by our Head of Data Science and is being trialled across the investment team. The portal will integrate third party ESG data with an objective to blend this information into an internal metric to reflect the views and analysis of our fund managers and ESG specialists. The ultimate goal is to use this data with the combination of machine learning to be able to see if we can identify trends and pinpoint the future direction of some of these ESG metrics to benefit our stock picking and integration.

However, before any of this can be completed, we focused on the point about data accuracy. Using our Data Science Team we cleansed the third party data and painstakingly looked back at this information over a ten year period including through testing and integration of the model. This involved checking millions of lines of data. We independently tested this and identified anomalies and worked with our data provider and their leadership to help refine their data and approach. This is a long-term project but we spent six months doing background work before we released any of the data to our fund managers.

Therefore, we highlight our innovation in this aspect in terms of a 'behind the scenes' partnership with the provider which was very much appreciated. We used our resource to look at this in a manner which they confirmed no other asset manager had done so. This outcome will allow us to fulfil further innovations. We also raised the PRI during our many calls and meetings with the service provider that is by working together we can improve data transparency that aids many stakeholders.


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