In today’s rapidly changing world, the ability to consider environmental, social and governance (ESG) issues as part of overall security research is crucial if fund managers are to make a thorough assessment of investment opportunities and portfolio risks. Fortunately, the ongoing increase in the quantity and quality of ESG-related data sources can give fund managers the opportunity to assess ESG risks and opportunities that have not previously been available from company disclosures alone – and to rank the sustainability of companies consistently, on a global basis.
The game changer for ESG research has been the development of artificial intelligence (AI) and machine learning technologies, which can analyse the vast amount of data that is now available, and highlight significant ESG risks – or potential opportunities – related to individual companies. The ESG issues that are identified can be used to build an ESG score for each of the companies that they currently invest in, or that they are considering investing in.
By utilising the power of AI, fund managers can review large amounts of ESG-related data from corporate reports, articles and many other sources, and interpret this data quickly and efficiently to reveal ESG issues, or to verify company reports and third-party ESG research. The output can reveal how companies are positioned across a range of ESG themes, including how they are managing their impact on the Earth’s natural resources and the environment, the effectiveness of human capital development programmes, supply chain risks, and the approach to customer welfare, to name but a few examples.
Understanding ESG risks
Once all this ESG data has been reviewed and interpreted, fund managers can process the results into comparable ESG rankings that can be used to supplement the existing company research of portfolio management and analyst teams, and help support the inclusion of ESG factors in all active investment decisions.
However, turning ESG analysis into ESG scores is a challenge, as ranking companies according to the ESG issues that they face requires certain judgements to be made. Essentially, fund managers need to be able to apply a weighting to the different ESG issues that have been identified.
Thanks to their broad coverage across markets and asset classes, we believe larger managers with active research capabilities will have an advantage in this area. At J.P. Morgan Asset Management, for example, our ESG scoring system leverages company disclosures, third-party estimates of environmental impact, data science signals, and alternative data sets provided by external vendors. We attach weights to the different ESG issues identified based on the insights of our sector analysts who, crucially, have many years’ experience identifying financially material ESG factors – and who therefore understand the specific challenges within sectors and regions.
A comprehensive ESG scoring system
As visibility of companies’ performance on key ESG indicators improves, it is becoming possible for fund managers with the relevant expertise and resources to make more accurate sustainability assessments and, therefore, take better-informed investment decisions on behalf of their clients.
We believe ESG scoring systems that combine active research with data science have the ability to make full use of the available data, allowing fund managers to drill down into individual data points, such as greenhouse gas emissions and supply chain-related metrics, so that they can integrate financially material ESG risks and opportunities into their broader security research.