ESG Investing: A New Approach to Thoughtful Investments

Environmental, Social, and Governance (ESG) investment has gained traction in recent times with growing patronage from institutional as well as millennial high net worth investors. ESG investments were pegged at $20 trillion in AUM in 2018, according to a Forbes article. Socially and environmentally aware millennial investors, with a pulse on key issues of our times like climate change/gender equality, can be credited for driving interest as well as investments into companies with strong ESG profiles.

Worldwide, lawmakers are looking at enforcing rules for companies to disclose enough ESG-related data. European Union’s Shareholder Rights Directive and ESG advocates urging the US SEC to step in and seek more disclosures, are some measures in that direction.

Despite increased adoption, ESG measurement for listed companies across the globe remains a challenge and requires deeper use of analytics techniques for performance assessment and reporting. Going forward, in a Business 4.0TM world, asset managers will not only need to ensure investments are aligned with the sustainable investing goals of their clients for performance measurement but will also have to assess the impact of investments based on a set of Capital Goals and Sustainable Development Goals (SDG).

Measuring ESG investment performance – Current Scenario

Standards for measuring ESG investments and the tools to analyze ESG metrics alongside financial returns are still evolving. There are several third party ESG rating agencies and independent research organizations that offer ratings, rankings or indices to help evaluate the alignment of investments with sustainability goals. Additionally, leading market data providers offer their own ESG score using AI-backed solutions and most of them have their own ranking system in place.

Due to the lack of standard metrics, investments in specific sustainable goals by companies cannot be measured using a common scale across industries/geographies. For fund managers, inconsistencies in ratings could potentially undermine their usefulness. Hence, looking at sub-components such as clean energy, quality education, climate action etc. makes more sense for them than using the overall ESG rating.

In this situation, the primary method for gauging ESG would be incorporating sustainability risks into business plans and the disclosure of relevant metrics for impact analysis.

Analytics to measure ESG Investments - Assessment and Reporting

ESG goals of companies are assessed by their impact on social parameters such as poverty, climate action, clean energy, economic growth, etc. However, at present, there are no standards in place to measure these goals on a comparative scale. In our view, asset managers must deploy intelligent analytics tools to assess the alignment of investments with the sustainable investment goals for performance measurement and impact assessment.

We believe multi-stage data acquisition and analytics to measure ESG investments must cover the following aspects:

• data collection for goals reported by companies, through data crawling, from sources such as published reports, disclosures, social media, web APIs and other internet sites
• understanding the unstructured data using natural language processing, artificial intelligence/ machine learning techniques and deriving meaningful insights
• classification into prominent themes such as industries, sectors and subsectors of companies
• calculation of goals achieved by target vs actual percentages at transaction level
• calculation of ESG score for each sub-component
• consolidation of ESG score at stock/fund level

Conclusion

Despite the current lack of standards in measurement and reporting, interest in ESG investments has grown over the past years. The interest from investors envisioning a better world and organizations creating better standards for ESG measurement across various parameters has fueled the need to evolve. Sophisticated analytics solutions will be helpful for fund managers to decipher structured and unstructured data for addressing current gaps in performance measurement.