The interest in ESG investing creates greater demand for ESG data, ratings, rankings, and methodologies (Abhayawansa & Tyagi, 2021). Different ESG ratings, rankings, and methodologies produce significantly different assessments of the ESG performance of companies (Abhayawansa & Tyagi, 2021). A lack of transparency about the data sources, weightings, and methodologies can also make it difficult to ensure the true ESG performance of companies was accounted for when making security selection and portfolio investment decisions (Abhayawansa & Tyagi, 2021).
Giese et al. (2019) provided a framework for the integration of ESG into benchmarks at various strategic levels, from the top policy benchmark level to the performance benchmark of individual allocations. Giese et al. highlighted the different investment objectives that asset owners might pursue when integrating ESG and how they could reflect these in their choice of ESG benchmarks. The findings of Giese et al. (2019) revealed integrating ESG into benchmarks made sense as a framework to achieve consistency because benchmarks were not only used at different strategic levels but also across all areas of asset management, index-based, factor-based, and active management to define the underlying investable universe and to provide a yardstick for performance.
Henriksson et al. (2019) recommended an approach to integrate ESG issues into portfolios that was based on two premises. The first is that classification of firms as good or bad ESG companies should be performed using ESG items that are material in that industry (Henriksson et al., 2019). The second premise is that it is possible to overcome the sparse voluntary ESG data reported by firms by constructing an ESG good minus bad (GMB) factor and then finding those firms whose returns load significantly on this factor (Henriksson et al., 2019).
Dimson et al. (2020) examined the extent of, and reasons for, disagreement among the leading suppliers of ESG ratings, and found the weightings given to each pillar of an ESG rating varied across agencies. Dimson et al. also reviewed the investment performance of portfolios and of indexes screened for their ESG credentials. The findings of Dimson et al. (2020) indicated ESG ratings, used in isolation, were unlikely to make a material contribution to portfolio returns. Surprisingly, Dimson et al. provided evidence that ESG indexes did not outperform the parent index from which they were derived over the longest available period. While the findings of Dimson et al. indicated FTSE4GOOD and MSCI-ESG, the two indexes that most closely represented what ESG investors did, showed no evidence of underperformance.
Unlike Dimson et al. (2020), Abhayawansa and Tyagi (2021) examined the causes of differences in the ratings and rankings generated by different agencies. The results of Abhayawansa and Tyagi (2021) indicated the divergences among raters could be attributed to differences in the definitions of ESG constructs, which was a theorization problem, and methodological differences, a commensurability problem. The results also revealed while users of ESG ratings were advised to study the definitions and methodologies before their use. Gidwani (2020) used the CSRHub data set to show that ESG ratings regressed strongly toward the mean. Specifically, Gidwani found these ratings included both data from most commercial ESG ratings firms and another 640 sources, and the observed regression persisted within the ratings data across nine years, for a sample set of more than 8,000 companies. Gidwani also found newly rated companies showed even more reversion than seasoned companies. Elsenhuber and Skenderasi (2020) indicated central banks had introduced ESG factors mainly for their pension fund investments, with the aim of further integrating sustainable investing into their own funds and in foreign exchange reserves portfolios. This is because the strategic asset allocation of the latter tends to be less diverse, and it focuses on the asset classes that does not have a conventional ESG approach.
Bahra and Thukral (2020) conducted a study to understand how different ESG scores were from traditional agency credit ratings. Specifically, Bahra and Thukral aimed to determine (a) whether E, S, and G scores were correlated; (b) whether ESG scores could enhance the investment process; and (c) whether an active, ESG-tilted corporate bond portfolio strategy generated superior performance versus a relevant benchmark that did not explicitly take ESG scores into account. Bahra and Thukral found evidence that ESG scores could be used to enhance portfolio outcomes via lower drawdowns, reduced portfolio volatility, and marginally increased risk-adjusted returns. Employing backtesting, Bahra and Thukral suggested E, S, and G scores were not related to one another and that ESG scores were additive to traditional credit ratings.
Using ESG scores of firms belonging to the MSCI World universe, Alessandrini and Jondeau (2020) measured the impact of score-based exclusion on both otherwise passive investment and smart beta strategies. The results of Alessandrini and Jondeau (2020) revealed exclusion led to improved scores of initially standard portfolios without deterioration of the risk-adjusted performance. Alessandrini and Jondeau found smart beta strategies exhibited a similar pattern, often in a more pronounced way. Moreover, the results of Alessandrini and Jondeau demonstrated exclusion also implied regional and sectoral tilts as well as undesirable risk exposures of the portfolios. Alessandrini and Jondeau showed ESG screening could substantially improve ESG scores for both otherwise passive and smart beta portfolios without reducing risk-adjusted returns.
Chen and Mussalli (2020) categorized the broad types of ESG investing in the market and introduced an ESG investment framework, which resulted in a portfolio that optimally combined the dual objectives of alpha and sustainability outperformance. Chen and Mussalli indicated it was possible for ESG factors to also generate alpha, provided materiality was taken into consideration. Branch et al. (2020) presented six quantitative ESG strategies for building or restructuring portfolios to align with investors’ ethical considerations and financial goals. In exploring these strategies, Branch et al. proposed certain practices and analyzed options for ESG portfolio construction that balanced risk and the ESG preferences of investors. The quantitative methods outlined by Branch et al. (2020) could lower tracking error but might also increase exposure to unwanted stocks or sectors. To mitigate against such exposure, Branch et al. recommended investors tap the expanding set of high-quality ESG-scored company data during portfolio construction.
Henriksson et al. (2019) suggested their approach was particularly suitable for quantitative investment approaches that invested in portfolios with large number of positions and many small active exposures, wherein vendor ESG data could be used in portfolio construction efficiently without the need to employ detailed ESG analyses of many individual firms. With such portfolios, Henriksson et al. argued it would be less about the ESG classification of an individual company than about the aggregate portfolio tilt toward good ESG and away from bad ESG at the portfolio level. Chen and Mussalli (2020) suggested in situations where the asset owner’s sustainability values and alpha generation did not align, a quantitative approach could be used to graph an ESG-efficient frontier.
Dimson et al. (2020) explained why different raters’ appraisals diverged, and whether ESG was associated with subsequent fund or index outperformance. The results of Gidwani (2020) suggested it was rare that a company maintained an especially high or low ESG rating. Conducting cross-sectional correlations, Bahra and Thukral (2020) suggested E, S, and G scores were not related to one another. The results of Bahra and Thukral (2020) also suggested ESG scores were additive to traditional credit ratings.
Anson et al. (2020) identified a sustainable beta factor that was successful in screening both companies and asset managers as green or nongreen, which was an important step in building a factor model for sustainable investing. Alessandrini and Jondeau (2020) suggested starting from initially passive multicounty portfolios, ESG screening might lead to substantial regional tilts, such as overweighting Europe and underweighting the US and emerging countries or sectoral bets, for instance in favor of information technology and against financial and energy stocks.
More recently, Sorensen et al. (2021) focused on the challenges associated with ESG investing and how quantitative approaches may address them. Sorensen et al. found as compared to fundamental methods of sustainable investing, quantitative methods had several advantages. Quantitative methods to ESG investing can build on and extend the vast analytical toolbox of modern portfolio theory to incorporate investor preference in portfolio construction (Sorensen et al., 2021). Sorensen et al. also found these quantitative methods could leverage the recent data explosion to obtain insights on many intangible sustainability metrics, and they did not have the black box label.
Two key challenges hold many asset owners and managers back from applying ESG to investment portfolio management: (a) confusion over the differences among the vast array of sustainable and impact investing disciplines; and (b) lack of clarity on whether and how investors who serve in a fiduciary capacity can incorporate these disciplines (Hays & McCabe, 2021). Hays and McCabe (2021) introduced a taxonomy of sustainable and impact investing approaches, mapped to a set of guidelines for fiduciaries to consider in practice. Hays and McCabe indicated this framework, based on a mix of market, legal, academic, and internal risk/return research, could provide investors with guidance on the applicability by ESG approach by account type, ranging from investment management accounts, both nondiscretionary and discretionary, to revocable and irrevocable trusts, to ERISA accounts. Hays and McCabe also indicated sustainable and impact investing could be split into four distinct approaches: (a) ESG integration; (b) ESG mandated; (c) thematic; and (d) high impact concessionary.
Atta-Darkua et al. (2021) used a responsible investing debate to critique two methods of responsible investment, negative screening and engagement. Atta-Darkua et al. illustrated the importance of selecting an ESG score provider by examining the differences in metrics among different providers. Focusing on the process for constructing portfolios that factored ESG principles into a strictly return-oriented model, Chen and Mussalli (2021) developed an approach based on three pillars: (a) ESG factors that might also be alpha factors; (b) a unique materiality value that linked ESG considerations to alpha; and (c) a portfolio construction framework that was informed by an investor’s ESG preferences. Chen and Mussalli indicated the key strengths of this integrated ESG modeling framework included its flexibility, relevancy, and dynamic nature.
Summary and Conclusion
There has been a wide range of research in academia and the asset management industry about the financial benefits of ESG investing. However, the equally important question about how to achieve consistency when integrating ESG and what methodologies to use has not received the same level of attention (Giese et al., 2019). As a result, ESG integration is often applied inconsistently and incompletely across portfolios (Giese et al., 2019).
By its nature, ESG assessment requires a forward-looking approach that static metrics may miss. What’s accepted practice today may be considered unacceptable tomorrow, as rules, regulations and popular opinion continue to evolve. Therefore, capturing ESG benefits and identifying risks requires an integrated approach.
Elsenhuber and Skenderasi (2020) highlighted the most critical challenges were the lack of a commonly adopted ESG taxonomy, and the limitations on the application of various ESG approaches in some of the portfolios they managed. Dimson et al. (2020) argued data were essential for making investment decisions, and most institutions relied wholly or partly on external providers of ESG data; however, minimal correlation existed between ESG ratings from alternative agencies. Hays and McCabe (2021) argued despite significant growth in interest and inflows over the past three years, sustainable and impact investing had reached an inflection point where the industry is being held back by a lack of clarity on definitions and fiduciary applicability.
The results of Sorensen et al. (2021) suggested quantitative methods had unique advantages for sustainable investing in the areas of portfolio construction, data application, and scaling domain knowledge. Sorensen et al. also suggested the skillful quantitative practitioner could create the optimal blend of human insight and computing power to extract sustainability insights from data. Sorensen et al. (2021) argued a thoughtful analytical system could be applied to a large universe of stocks, and quantitative methods might also be leveraged to predict popular ESG vendor ratings. Sorensen et al. highlighted subjective judgement applied to building the quantitative system was essential.
Giese et al. (2019) argued ESG ratings might be suitable for integration into policy benchmarks and financial analyses. Henriksson et al. (2019) provided evidence that showed the superiority of using material, industry specific ESG items, and the merits of expanding the ESG classification using the ESG GMB loadings. Gidwani (2020) argued ESG ratings exhibited behavior that might make them difficult to use in an investment process. Gidwani also argued ESG-based investment strategies that sought to buy the best and sell the worst might not perform as well as expected. Based on the results, Bahra and Thukral (2020) argued the contingent liabilities related to ESG issues were not necessarily factored into rating agencies’ assigned credit ratings. Chen and Mussalli (2020) argued standard methods of materiality definition, based on sectors, could be limiting and were not the optimal axis to measure materiality. Chen and Mussalli also argued ESG investing was based on investor’s sustainability values, which must be incorporated as part of ESG portfolio construction. Alessandrini and Jondeau (2020) argued although the broad conclusion of improved ESG profile without affecting risk-adjusted performance also held for smart beta portfolios, aggressive exclusion of ESG low-scoring firms might lead to some reduction in exposure to targeted factors. Employing backtesting, Bahra and Thukral (2020) suggested E, S, and G scores were not related to one another and that ESG scores were additive to traditional credit ratings. Madhavan and Sobczyk (2020) provided evidence the composition of ESG scores mattered, with environmental score most closely related to fund volatility. Hays and McCabe (2021) offered a framework to align different types of ESG investments with various investment and fiduciary mandates.
Previous researchers that have make attempts to investigate ESG ratings and methodologies have made practical implications. Gidwani (2020) proposed investors and company managers both realize that ESG ratings were likely to change toward the mean and that this pattern did not necessarily mean that a good company was getting worse or a bad one is getting better. Gidwani further proposed both investors and corporate managers adjust their understanding of the significance of ESG ratings and their expectations about how they changed. Acknowledging the challenges faced by investors who want to do well by doing good, Branch et al. (2020) stressed the need for investors to clearly understand their goals and constraints, as well as the complexities intrinsic to trade-offs between risk control and exposure to unwanted securities.
Hays and McCabe (2021) proposed a strong fiduciary framework with limits for the application within each approach be guided by a focus on rigorous risk-adjusted return analysis, clear documentation, and checks and balances on implementation and ongoing monitoring. Chen and Mussalli (2021) proposed a novel quantitative framework for optimizing both alpha and the ESG aspects of a portfolio. Abhayawansa and Tyagi (2021) argued instead of attempting to compare and contrast ratings and rankings of different agencies, investors should determine the ESG constructs that were material to their own investment strategies, and then matched them with an ESG rating or ranking product that closely resembled those constructs.
Rather than outsource ESG assessments to third-party providers, investors must conduct in-depth, hands-on research and engage actively with issuers. This approach enables investors to achieve real insight into a business and its activities, and to get a proper understanding of its future prospects as well as its past.
Lastly, developing a complete picture of corporate behavior also requires engagement with management, visiting facilities and understanding the ecosystem in which a firm operates. It also needs sufficient analyst coverage and the ability to do exhaustive fundamental homework and to validate data. Broad assumptions about industries, countries and risks can lead to suboptimal conclusions, improperly understood risks or missed opportunities.
Keywords: ESG investing, fixed-income portfolio management, portfolio theory, portfolio construction, style investing, portfolio management, multi-asset allocation, factor-based models, security analysis and valuation, risk management, equity portfolio management, performance measurement, wealth management, sustainable investing, socially responsible investing, ESG, social impact, statistical methods, and analysis of individual factors/risk premia
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