Garbage in, garbage out
Like all investment data and ratings, environmental, social and governance indicators need to be scrubbed clean and carefully analysed, writes Adam Seitchik Since I was appointed CIO of Boston-based responsible investment specialist Trillium Asset Management in 2004, I have been working towards the fuller integration of social and environmental analysis into institutional investment, as well as applying institutional-quality systematic research and portfolio management processes to responsible investing. Two interconnected trends are having a significant impact on this effort.
The first trend has been the inclusion in the mainstream of responsible investment through the commitment of some of the largest asset owners in the world to broad networks. The UN Principles for Responsible Investment (PRI) now include over 200 asset owner signatories representing trillions of dollars in assets. The massive expansion in investor interest has led to a proliferation of data providers, independent research firms and sell- side environmental, social and governance (ESG) research programmes. Bloomberg and Reuters have both invested significantly in this area, and there is also a diverse set of focused providers such as EIRIS, Trucost, Sustainalytics and Vigeo. Pioneering ESG research houses Innovest and KLD were gobbled up by Riskmetrics, which itself was purchased by MSCI. In just a few years sell-side ESG research has gone from next-to-nothing to pervasive, motivated in part by the powerful asset owners in the Enhanced Analytics Initiative.
Most investors now accept that ESG issues affect the bottom-line; the materiality of exposures such as oil spills, factory labour practices in less-developed countries, and the quality of board-level risk-management systems are manifest through both share prices movements and extensive press coverage.
Questions remain about the quality of data and hence the ability to make judgments between companies. After all, unlike traditional financial accounting, much disclosure of extra-financial information is voluntary. Despite the development of the ‘global reporting initiative’, what companies publish and when is still unhelpfully diverse. However, there is now a vast amount of available ESG data, ratings and research, much of it delivered in a systematic and comprehensive fashion similar to traditional financial variables.
So can ESG be implemented within a systematic investment process? The answer to this question has become more evident since I began working with Larry Abele, senior founding partner of Auriel Capital, focusing on fundamental, systematic equity investing. Auriel Equities’ expertise in data management and analysis provided the opportunity to fully integrate ESG research into a systematic investment platform.
This partnership has provided unusual opportunities to examine in detail the quality of ESG data and ratings. Researchers at Auriel Equities are not only financial analysts but also programmers. Our performance will sink or swim on the quality of our investment insights, however, because the implementation of our ideas is highly systematic, we have unusual capabilities in cleaning and maintaining a highly diverse set of databases. Our daily checking of downloaded financial variables flags on average 10-20 pieces of potentially bad data per week. Our systems search for outliers, missing data and stale indicators, and we have analysts following up to sort out the issues. Having gone through this same process with ESG data and ratings, we believe we are in a good position to assess the quality and reliability of ‘extra-financial’ information. No doubt, there is a potentially overwhelming number of ESG indicators. Just one of our four research vendors provides around 300 pieces of ESG data on thousands of companies going back seven years. As with financial variables, the challenge with ESG is in identifying the highest quality data which is most relevant to future company performance.
Our focus is on a relatively small number of variables in each sector based on our assessment of their forward- looking materiality. We care about carbon footprint data because it matters in the utility sector, and we assess responsible lending practices in less developed countries given the financial implications for the European banking sector. Having isolated the most important data, we conduct the same in-depth process of data checking and cleaning with ESG indicators that we use for traditional financial data.
Auriel Equities pursues large cap strategies, analysing highly traded companies. These companies have the scale that allows for widespread investments in ESG management and reporting systems. Data availability and quality issues are challenging even for this large cap universe, and it would not yet be possible to pursue a systematic approach to ESG as an emerging markets manager, a small cap manager, or even a mid cap developed markets manager.
By limiting our analysis to financially material areas, I believe that the data and rankings we utilise provide an acceptable assessment of ESG risks and opportunities in our large cap universe. For example, we can sort automobile manufacturers fairly clearly on key performance indicators such as CO2 emissions/km, the energy efficiency of their fleet of vehicles, R&D innovation and the effectiveness of their global sourcing and employment practices. These are relatively obvious material factors facing the automobile sector, and any investment analyst should be tracking these issues carefully.
Before incorporating any indicator into our investment process, data is checked rigorously. We look at the full history of the data for all companies in our universe and identify any missing data, strange outliers or stale information that does not seem to be updating on the schedule that we expected.
While most of the ESG data is good, we find the occasional outlier. For example, one set of indicators assesses a company’s carbon exposure, management and innovation. This is not fast-moving financial data. Many ESG indicators are only re-analysed and updated annually, or when a company has a material change in its operations. Yet monthly updates revealed unexpected volatility in carbon management ratings in several sectors, which needed to be checked with the provider to ascertain whether this was a data error or intentional.
This process of data checking and cleaning has two benefits. The first is ensuring the data we use are accurate and sensible. The second is improving the quality of ESG research. While we want to maintain our information advantage, the more investors who take an interest in this research the better ESG disclosures will be from companies, as well as the overall quality of data and ratings.
A final issue with ESG indicators is systematic biases. For example, we have found a positive skew towards mega caps stocks. ESG analysts often reward companies for developing formal strategies, making investments in management systems and producing sustainability reports. The largest companies are more capable of financing these investments and under the most pressure to do so. Also, we have found that a tilt towards low-carbon producers creates a large negative exposure in the portfolio to oil prices. If ESG investors are not careful they will end up with portfolios that systematically lag broad indices when, for example, oil prices are rising or mega caps are underperforming.
Investors should at least ensure that systematic tilts in their portfolios are intentional. Auriel seeks to identify the ‘pure alpha’ (idiosyncratic return) from companies, neutralising as many systematic exposures as possible. We ensure that tilts toward positive ESG profiles avoid systematic exposures to common risk factors such as market cap, commodity prices, sectors and growth versus value.
ESG indicators are improving, yet problems persist. The inherent challenges are comparable to those for traditional financial data. As always, the most important consideration is to have a strong investment thesis about the drivers of future performance. Professional responsible investing requires all of the best tools and practices of traditional investing, allowing for a broader assessment of the risks and opportunities facing large, complex businesses.