Overview
Environment, social, and governance (ESG) intelligence refers to a combination of resources and tools that asset managers can use to incorporate ESG considerations into their investment processes. Asset managers can leverage ESG intelligence to track and report on the ESG performance of their portfolio companies and identify companies that are better positioned to manage ESG risks and opportunities.
There are several ESG intelligence suppliers, each with unique advantages and disadvantages. While some companies specialize in a particular ESG topic, such as climate change, others offer a wide spectrum of ESG data and analytics. Before choosing an ESG partner, asset managers should carefully examine their requirements and select a suitably aligned vendor that can perform custom research, if required.
ESG intelligence can help asset managers keep up to pace with changing consumer preferences and meet increased legal obligations. Asset managers can thereby stay on top of the game in a market that is changing quickly.
Benefits of Using ESG Intelligence
- Effective risk management: Asset managers can identify companies that are susceptible to ESG risks by incorporating ESG topics in their investment analysis. ESG intelligence can help these managers react to rapidly changing market conditions by providing timely and better results that help them to identify and manage ESG risks. This aids in creating stronger and resilient portfolios.
- Long-term financial performance: Asset managers can identify companies that are well positioned to seize growth opportunities by judging the strength of their ESG performance.
- Increased shareholder trust: ESG intelligence helps investors to discover companies with sustainable business models and sound governance practices, both of which can enhance shareholder value by boosting the firm’s valuation and investment returns.
- Enhanced customer loyalty: Asset managers can better satisfy and retain their clients by using ESG factors to align their investments with the clients’ changing needs.
- Compliance with regulatory requirements: Asset managers can use ESG intelligence to ensure compliance with current and forthcoming rules related to ESG reporting and disclosure obligations.
- Gaining a competitive edge: Asset managers can showcase a strong ESG emphasis and track record to stand out from competition and attract investors that value responsible investment methods.
- Access to opportunities in clean energy, low-emission technology, and related areas: ESG intelligence enables asset managers to leverage new trends and market disruptions and access a variety of investment opportunities.
Approaches to Data Analysis for ESG Intelligence
Asset managers can incorporate ESG analysis into their investment process through qualitative methods, quantitative methods, or a combination of the two.
- Qualitative analysis: This is an investment process in which asset managers examine ESG data for a company to evaluate its capacity to handle specific ESG concerns. Such analysis tends to be more subjective as it involves human judgement; however, technological advancements in areas such as natural language processing have the potential to reduce this subjectivity.
- Quantitative analysis: This method expands quant models to include ESG factors or scores created from ESG data. Such analysis typically uses a large set of companies, instead of individual company assessments. Some asset management companies may, however, use individual company assessments to produce their custom scores.
Regardless of the choice of approach, ESG analysis and financial analysis are typically combined to make wise investment choices. ESG factors can act as screening criteria to identify companies to include in a portfolio or can be used to design a quantitative model that modifies valuations based on ESG scores.
Sources to Gather ESG Intelligence
Asset managers must use multiple intelligence sources to ensure comprehensive and accurate ESG assessments. Here are two key approaches they can use.
Inside-out Approach: This approach puts more emphasis on self-reporting by the company, as against external sources. The company’s own disclosures are typically more consistent, making ESG analysis easier over the years; there is, however, the risk of bias in such reporting. Examples sources include:
- Company resources
- Annual reports, including integrated report, sustainability report
- Policy documents such as code of conduct and human rights policy
- CSR / ESG section on company website
- News releases by company
- Industry associations
- TCFD
- Principles of Responsible Investment (PRI)
- Non-profit organizations
- CDP (formerly the Carbon Disclosure Project)
- Global Reporting Initiative (GRI)
- Sustainability Accounting Standards Board (SASB)
Outside-in Approach: The approach focuses on ESG data from external sources which can affect a company’s ESG performance or provide comprehensive information on how a company is performing on various ESG topics. Such data is less likely to be biased or manipulated; however, it might be more expensive and less frequently available. Example sources include:
- ESG News Sources
- Responsible Investor
- Chemical Watch
- ESG Today
- Culture 500
- Glassdoor
- Tools and platforms to track ESG risks and opportunities
- Reprisk
- Insightsfirst
- Alternative data sources
- Geo-spatial data
- Social media data
- Credit data
- Web traffic data
Steps of ESG Intelligence
- Step 1: Identify material ESG topics: Prioritize the ESG topics most relevant to the company and helpful to identify its exposure to ESG risks and opportunities.
- Step 2: Collect ESG data: Conduct in-depth research to compile ESG data of a company from internal and external sources. Asset managers often outsource ESG data collection for their portfolio firms to an ESG data vendor that can cover a wide range of data across KPIs and provide timely updates to ESG data.
- Step 3: Analyze ESG data: Analyze the collected ESG data to screen companies according to their involvement in questionable industries (such as gambling or tobacco, for instance) or other criteria. Examine the data further to produce comprehensive company assessments based on unique frameworks and data models and to assign scores as per the companies’ ESG performance.
- Step 4: Report ESG performance: Transparent and comprehensive reporting on ESG performance includes information on how asset managers assess ESG risks and incorporate ESG factors into investment decisions. Along with reporting the financial and ESG performance of their portfolios, asset managers must also disclose the methodology they use to select and monitor investments.
- Step 5: Monitor on a timely basis: Monitor ESG performance to generate timely insights and pick up warning signs. For instance, a company that lacks a majority of independent directors on its remuneration and audit committees may be flagged as having high ESG risks or needing further investigation.
Key Concerns Related to ESG Intelligence
- Data availability and quality: ESG data may be incomplete and inconsistent, making it difficult to compare data across industries and businesses. Lack of standardized reporting frameworks for ESG information further exacerbates the ESG data challenge.
- ESG data time series: ESG data time series (7-15 years) are typically much shorter than financial data making it difficult to identify data trends and patterns.
- Lack of in-house ESG expertise: To fully utilize ESG intelligence, asset managers should either develop an in-house team or partner with external vendors – such as MSCI, Evalueserve, or Sustainalytics – to capture the benefits of timely ESG research and analysis.
- Changing regulatory requirements: Asset managers are required to stay updated with regulatory developments surrounding ESG standards and continuously work toward aligning their investment strategies with recent updates.
- Cost: ESG intelligence may not be cheap, as it requires resources and tools to create long-term value for stakeholders.
Best Practices for ESG Intelligence
- Build a strong team: Asset managers should invest in setting up a strong team with expertise in ESG data and analysis as well as in financing.
- Partner with third-party vendors: By collaborating with third-party ESG providers (such as MSCI and ISS ESG), asset managers can access the required data and expertise to improve their investment processes and decision making. Some ESG data providers, such as Evalueserve, can apply bespoke frameworks, providing analysis to assess ESG performance and create custom ESG ratings.
- Clearly define the ESG approach: Asset managers must clearly specify the investment objective or strategy they plan to follow by incorporating ESG factors.
- Integrate ESG throughout the investment lifecycle: From portfolio construction to risk management and reporting, ESG intelligence is critical to make informed investment decisions.
- Be transparent about the investment process: Inform clients and stakeholders about the ESG approach, processes, and performance results through consistent and transparent reporting.
- Track performance of the ESG fund: Once they integrate ESG considerations into investment strategy, asset managers need to watch the results to ensure they are on track as per the defined objectives. This will enable them to modify the process as necessary.
- Keep up with the latest trends: Since the ESG landscape is continuously evolving, it is essential to adapt to the most recent trends and regulatory developments to be able to make intelligent investment decisions.
- Learn and improve continuously: Keep abreast of new developments in legislation, industry best practices, and emerging trends in the ESG sector as well as with changing investor expectations and ESG requirements.
- Engage and collaborate: Asset managers should engage with industry peers, participate in ESG initiatives, and work toward building standardized ESG practices.
The Future of ESG Intelligence
With companies and investors increasingly recognizing the importance of creating non-financial value through investments, the future of ESG intelligence appears to be quite promising. Here are the trends likely to create a bright future for ESG intelligence.
- Increasing availability and standardization of ESG data: As more companies report on their ESG practices, more data is likely to become available for analysis. This might make it easier for investors and analysts to evaluate a company's ESG performance. The development of common frameworks and standards could further help streamline ESG reporting and make it more meaningful for investors.
- Advancements in technology: Advancements in machine learning and natural language processing will play a crucial role in extracting and analyzing large volumes of ESG data and identifying data patterns and correlations.
- Tools such as the ESG Intelligence tool by PwC and the AI-enabled Insightsfirst by Evalueserve can offer greater value to asset managers by allowing them to focus more on strategic and value-addition tasks. By combining AI with alternative data such as satellite imagery, sensors, and data analytics, asset managers can identify and assess ESG risks such as climate change and social controversies at an early stage, and accordingly mitigate ESG risks and increase returns.
- Regulatory requirements: Regulatory pressure may impact the direction of ESG intelligence. Governments and regulators may strengthen ESG-related disclosure requirements and reporting standards in a few geographies or introduce them in others. This might drive the need for corporate transparency and accountability, which in turn can fuel demand for ESG intelligence.
- Integration of ESG in financial analysis: Conventional financial analysis and ESG intelligence are likely to be more closely entwined. ESG topics might be incorporated in sophisticated valuation and risk assessment models created by investors and financial institutions.
- Higher focus on stakeholder engagement and reporting: This could play a key role in the drive to measure and report ESG performance responsibly and transparently, thereby boosting the prospects for ESG intelligence.
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