A VIEW FROM THE MOUNTAINby Robert A. Gillam, CFA

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N O V E M B E R | 2 0 1 9

Our Perspective on Environmental Social and Governance (ESG)/SRI Investing

As a systematic manager how do you find the quality and robustness of ESG data?

While we find data quality to be improving in terms of breadth and depth, ESG data still suffers from challenges arising from the lack of standardization across vendors, inconsistent weightings, and that much of the data provided is voluntary and/or self-reported. Additional issues stem from slow revisions, materiality, lack of regulatory disclosure enforcement, and that there is limited point-in-time history for testing. While coverage is more robust for some major indices, it still falls short in areas like U.S. Small Cap and Emerging Markets. The chart below illustrates this along with how it varies depending on the vendor.

And how consistent is the data among vendors?

A perfect example of the lack of standardization among ESG vendors is that there is no single accepted definition of ESG itself! The varying degrees of what “ESG” means is illustrated on the next page where you will see how low the correlations can be between various vendors of choice.

96% 94% 91%

22%

94%

1%

100%

24%

75%

36%

79%

55%

0%

20%

40%

60%

80%

100%

Russell 1000 Russell 2000 MSCI World ex-USA MSCI EmergingMarkets

ISS

Sustainalytics

RepRisk

ESG Vendor Data Coverage by Index, 9/30/19

Source: ISS, Sustainalytics, RepRisk, FactSet, 10/11/19

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In your analysis have you found any efficacy in various vendor metrics for ESG?

We have found that raw, standalone ESG scores have limited predictive power (see chart below). Over the years, however, we have developed our proprietary approach to applying ESG data, especially the “G” component of our model. Additionally, we recently completed an ESG/SRI white paper which will be published in the Journal of Investing (JOI) highlighting the ESG/SRI work of Dr. John Guerard, Jr., Ph.D., McKinley Capital’s Director of Quantitative Research.

ESG Score Correlation Across VendorsMSCI World Universe, as of 9/30/19

SustainalyticsESG Rank

SustainalyticsEnvironmental

Rank

SustainalyticsSocial Rank

SustainalyticsGovernance

Rank

ISS Governance

Quality Score

RepRiskIndex

RepRiskRating

Sustainalytics ESG Rank 0.90 0.89 0.75 0.28 (0.20) (0.18)

Sustainalytics Environmental Rank 0.90 0.70 0.54 0.23 (0.20) (0.20)

Sustainalytics Social Rank 0.89 0.70 0.59 0.24 (0.18) (0.17)

Sustainalytics Governance Rank 0.75 0.54 0.59 0.29 (0.09) (0.05)

ISS Governance Quality Score 0.28 0.23 0.24 0.29 (0.06) (0.02)

RepRisk Index (0.20) (0.20) (0.18) (0.09) (0.06) 0.61

RepRisk Rating (0.18) (0.20) (0.17) (0.05) (0.02) 0.61

Avg Forward 1mth Excess Return vs. Equal Weighted, MSCI World Universe3/31/13 - 9/30/19

-0.15%

-0.10%

-0.05%

0.00%

0.05%

0.10%

0.15%

Best Quintile Quintile 2 Quintile 3 Quintile 4 Worst Quintile

Sustainalytics ESG Sustainalytics Environmental

Sustainalytics Social Sustainalytics Governance

ISS Governance RepRisk Index

Source: Sustainalytics, ISS, RepRisk, FactSet, 10/15/19. Past performance is not indicative of future returns.

Source: Sustainalytics, ISS, RepRisk, FactSet, 10/11/19

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A link to the paper may be found here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3011644.

AbstractUsing an earnings forecasting model has been shown to be useful and highly statistically significant in U.S. stock selection, we report find that incorporation of Socially Responsible Investing (SRI) and Environmental, Social and Governance (ESG) criteria can enhance portfolio Information Ratios and Sharpe Ratios. The novel approach here uses a normalization of ESG strengths and weaknesses ratings, applied in both robust simply-weighted and realistic optimized portfolio settings. In part, it confirms a now classical “no-cost” result for certain SRI/ESG investment constraints and, with Diversity, Human Rights, and Governance criteria, shows that SRI/ESG information can enhance portfolio returns. Thus, SRI / ESG investors may not have to expect lower portfolio returns and Sharpe Ratios.

Dr. Guerard, who won the inaugural 1996 Moskowitz award for outstanding quantitative research in SRI, recently submitted new findings with co-authors Chris Geczy of the Wharton School at the University of Pennsylvania and Mikhail Samonov at Forefront Analytics. This work, which focuses mainly on the U.S. market, finds that by excluding certain ESG categories (as defined by KLD), higher Sharpe and Information ratios can be achieved.

McKinley Capital Management, LLC is a quantitative growth asset manager with 30 years experience using a disciplined, data-driven investment methodology focused on identifying fast-growing opportunities in Alaska and around the world.

The views contained in this paper represent the views of the author and McKinley Capital Management, LLC. The views and abstract are not an offer to buy or sell a particular security. Please read complete disclosure on the following page.

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