Long-Term Asset Class Forecasting and R-Factor™
Using ESG Scoring to Adjust Country-Level Risk Expectations
State Street Global Advisors’ Long-Term Asset Class Forecasts (LTACF) form the backbone of our Investment Solutions Group’s (ISG’s) strategic asset allocation process; these forecasts are rigorously vetted throughout our organization and updated quarterly. The LTACF process incorporates a wide range of relevant macro and financial variables to produce a forward-looking estimate of total returns. In this paper, we discuss why (and how) ISG has incorporated State Street’s proprietary environmental, social, and governance (ESG) data architecture, known as R-Factor™, into our LTACFs in order to help investors better understand their total risk exposures and make informed investment decisions.
State Street’s R-Factor is a comprehensive scoring system that is used to assess individual companies’ adherence to a broad range of ESG criteria. We developed R-Factor to address market infrastructure challenges around ESG data quality and consistency. By offering companies a transparent road map for how to improve their ESG practices and disclosures, we aim to build more sustainable markets.
Each R-Factor score draws on data from four ESG data providers.1 R-Factor leverages the Sustainable Accounting Standards Board’s (SASB’s) widely accepted, transparent materiality framework, and also considers 17 corporate governance codes, to generate a unique ESG score for listed companies. We currently score more than 7,500 companies and are continuously expanding our coverage universe.
We use company-level R-Factor scores to formulate country-level R-Factor scores and modify risk expectations accordingly within our asset class forecasts. Some countries demonstrate R-Factor scores that are semi-permanently higher than others. We believe this is related to structural differences in culture, law, or environment and is largely priced in.
Given this backdrop, our analysis does not focus on the absolute aggregate R-Factor score for a particular country, but rather on whether this ESG metric is improving or declining over time. We believe that incorporating changes in R-Factor country-level scores into LTACFs is likely to reduce tail risk associated with environmental and governance issues.
How We Use R-Factor in LTACFs
At first we look to define the universe as the intersection set of all the companies present in the MSCI ACWI All Cap index and the R-Factor universe. The securities in our universe are grouped by their MSCI Country classification, and each country-level R-Factor score is built using market capitalization weighting across components. Security-level R-Factor raw scores are aggregated up to country-level and then regional-level scores.
We identify and track changes in country-level aggregated and normalized R-Factors scores as input into our forecasting approach. We incorporate these changes into LTACFs by incrementally reducing risk expectations for countries that exhibit improvements in R-Factor scores and incrementally increasing risk expectations for the countries in which scores deteriorate (see Figure 1).
Figure 2 provides an example of how select country and region risk estimates (expressed as volatility) might be adjusted based on R-Factor scoring. The overall R-Factor score for Europe is highest amongst all regions, as one might intuitively expect. However, over the last five years Europe’s R-Factor score has deteriorated, and therefore an upward adjustment in risk expectation (volatility) would be incorporated into our Europe LTACF.
Similarly, the overall R-Factor score of US Large Cap stocks has improved, and therefore a downward adjustment in risk expectation (volatility) would be incorporated into the US LTACF. It is also possible that changes to the US administration, including an increased focus on climate change, will be a catalyst for further ESG improvements and ultimately an improving R-Factor for the US over time. Within key US equity indices, sector weightings also explain the improvement in ESG metrics, with Information Technology being the main sector contributing to the overall score for the US.
The Way Forward
We believe that identifying and incorporating material ESG issues is integral to our role as asset managers and fiduciaries for our clients. By adjusting risk expectations for country-level equity forecasts using R-Factor and other variables, we aim to provide a thorough comparison of the relative attractiveness of asset classes inclusive of ESG metrics. Ultimately, we aim to continuously improve our Long-Term Asset Class Forecasts, as they are key inputs to robust asset allocation guidance for our clients.
The inclusion of R-Factor and the adherence to ESG factors within our LTACFs is an example of State Street’s dedication to enhancing our decision making and investment processes. As one of the world’s largest asset managers, State Street has a responsibility to drive ESG transformation throughout the industry and utilize our research and in-depth experience to identify where ESG really makes a difference – and adjust how investors approach price formation and the risk estimation of assets. By incorporating R-Factor into State Street’s investment solutions, client reporting, and stewardship program we are using our position as a large global investor and a Principles for Responsible Investment (PRI) signatory to help create sustainable capital markets.
1Sustainalytics, Vigeo EIRIS, Institutional Shareholder Services (ISS) Governance, and ISS Oekom (as of February 22, 2021).
The views expressed are the views of the Investment Solutions Group as of February 22, 2021, and are subject to change based on market and other conditions. All information is provided in good faith, and there is no representation nor warranty that such statements are guarantees of any future performance. Actual results or developments may differ materially from the views expressed.
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Responsible-Factor (R-Factor) scoring is designed by State Street to reflect certain ESG characteristics and does not represent investment performance. Results generated out of the scoring model are based on sustainability and corporate governance dimensions of a scored entity.
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