What’s hot: ESG exposure and portfolio construction (part 2 of 3)

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The October 2018 Inquire Europe seminar brought together about 100 investment professionals and academics in Budapest, Hungary, to discuss “ESG exposure and portfolio construction”. Here is part 2 of the key points from the various presentations related to an environmental, social and governance (ESG) criteria driven investment approach. For part 1, click here and for part 3, click here.

Integration: sustainability integration in factor portfolios does not harm returns

Lars Kaiser of the University of Liechtenstein addressed the question of what happens when sustainability considerations are integrated into traditional factor strategies. The focus was on combining ESG company information with traditional value, growth and momentum factors.

First, the ESG data was cleaned by removing large embedded size and industry exposures. Then, Kaiser built quintile portfolios on the basis of the 50/50-average of a company’s traditional factor rank and its sustainability rank (treating E, S and G independently, adding even an extra risk materiality variable aimed at measuring future company risk).

The Morningstar Style Box for Value, Growth and Momentum was used in the study. The ESG factors used were selected based on the Sustainability Accounting Standards Board (SASB) materiality maps for each industry. The empirical results concerned both US and European companies. The main conclusion was that ESG aspects can be successfully integrated in traditional factor approaches and that this leads to improved sustainability ratings for the factor strategies without hampering their risk-adjusted returns.

Integration: extend universe coverage using ESG exposures instead of ESG ratings

Margaret Stumpp from Quantitative Management Associates (QMA) presented an approach to integrate ESG into portfolios that was based on two premises. The first is that classifying companies into good and bad ESG companies should be done using ESG items that are material in that industry. For that purpose she relied on the SASB material items.

This required mapping the 79 SASB industries into the 157 GICS sub-industries and then mapping the 52 Bloomberg ESG items, a third-party vendor, into the 30 SASB material items. Companies classified as material SASB Good and Bad ESG, respectively were only a small portion of the sample. Stumpp found that materially Good ESG companies had higher abnormal returns than materially Bad ESG companies, but the difference was statistically insignificant.

When using all ESG items, Bad ESG companies outperformed Good ESG companies, but the differences were statistically insignificant. She also found that Bad ESG stocks were smaller, lower priced, had higher historical and expected growth, were more volatile, more costly to sell short and their expected returns appeared positively skewed.

The second premise is that it is possible to overcome the sparse voluntary ESG data reported by companies by constructing an ESG Good-Minus-Bad (GMB) factor and then finding those companies whose returns load significantly on this factor. The betas were estimated with trailing 60 monthly observations using a Fama-French five-factor model modified with the additional ESG GMB factor. Companies with significantly positive ESG betas were considered “good” and conversely, those with significantly negative ESG betas were “bad.”

The main objective of the approach was to capture commonalities within the data and not to argue that GMB is a priced common factor. Nevertheless, she found that the mean annual abnormal return of material SASB Good ESG companies based on exposures to the GMB factor was +2.7%, statistically greater than the -3.2% of Bad ESG companies.

However, she also found that in four of the eight years considered, Bad ESG companies outperformed Good ESG companies. For this reason, she was cautious about the results. It is important to say that the number of Good and Bad ESG companies increased by over 200% when using exposures instead of ratings. “Good” ESG stocks appeared less risky, expensive and had lower expected returns. Expanding the ESG classification using the ESG GMB factor loadings can materially increase coverage with little change in characteristics.

Integration: ESG risk exposures more important than ESG ratings for portfolio risk

The issue of how to measure and integrate sustainability aspects into investment portfolios via a factor model was also considered by Benjamin Hübel of the University of Erlangen-Nürnberg, who won the Autumn Seminar 2018 prize for his presentation. He pointed out that ESG risks affect whole markets and therefore are difficult to diversify away.

This reasoning reveals the logic of adding separately E, S and G return factors as extra risk factors to existing asset pricing models. If these factors increase the explanatory power of common models, then it makes sense to take into account E, S and G risk exposures (betas) of individual companies in the portfolio construction process. The beauty of such an approach is that the sensitivities to E, S and G risk factors are simple to estimate using stock returns and standard asset pricing techniques (e.g. extending the Fama-French five-factor model with E, S, and G factors), even for companies that are not yet on the radar screens of the sustainability community.

Hübel’s results cover the European stock market. His key conclusions were that the ESG factors significantly add explanatory power to common asset pricing models and those improve and enrich risk management and portfolio construction.


For more posts by Erik Kroon, click here

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Erik Kroon

Analyst, Quant Research Group, BNP Paribas Asset Management

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