What’s hot: ESG exposure and portfolio construction (part 3 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 3 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 2, click here.

Institutional investors: those with better sustainability footprints performed better

Institutional investors dominate equity markets. That is why in the second key note presentation, Philipp Krüger from the University of Geneva and the Swiss Finance Institute focused on the sustainability footprint of such investors, in particular since little is still known about their environmental and social preferences.

Krüger looked at the sustainability of US institutional investors by analysing their portfolio holdings in 13F filings and found that those with better sustainability footprints delivered better risk-adjusted performance. He also found that the relationship was stronger for those with longer-term investment horizons, e.g., lower turnover, and that the impact on performance resulted essentially from the environmental footprint, not the social dimension.

The results were robust to a number of controls including the number of stocks or industries in the portfolios, the size of the portfolio, the institution type, the country where the headquarters was located and the exposure of the portfolio to the factors in the Fama-French five-factor model. The study used environmental and social metrics from both the MSCI and Thomson Reuters ESG databases, combing the two at stock level.

Institutional investors: ESG preferences explain under-reaction to mispricing signals

Xintong Zhan from the Chinese University of Hong Kong tested the hypothesis that institutional preferences for ESG may lead to under-reaction to stock mispricing signals and thus to further stock return predictability. In particular, such a hypothesis predicts that overvalued stocks with high ESG ratings have the most negative future and, conversely, that undervalued stocks with low ESG ratings have the most positive future abnormal stock returns.

In her test of the hypothesis, she considered the portfolio holdings given by US institutional investors in 13F filings. ESG ratings were taken from the MSCI ESG Stat (KLD Research & Analytics database). Sustainalytics was used to confirm the results. ESG ratings measure community relations, product characteristics, environmental impact, employee relations, workforce diversity and corporate governance, and reflect both benefits and harm caused by companies.

To our knowledge, no materiality filter other than that of the ESG rating agencies was imposed on the ratings. Assessment of mispricing was based on 11 known stock anomalies: Net Stock Issues, Composite Equity Issues, Accruals, Net Operating Assets, Asset Growth, Investment-to-Assets, Distress, O-score, Momentum, Gross Profitability Premium and Return on Assets. Data for detecting mispricing comes from Stambaugh, Yu, and Yuan (2015) monthly MISP scores. The universe considered was the Russell 3000 US companies between 2003 and 2013.

The study focused only on stocks with both ESG ratings and MISP scores available. That covered 31% of the universe, 66% in market-cap terms. Stocks were sorted into 100 groups, assigned with the group rank. The scores of the anomalies ranged from 1, most underpriced, to 100, most overpriced. Double-sorts according to last month-end mispricing score and last year-end ESG ratings were constructed.

Xintong confirmed that preference for social performance such as non-monetary incentives, preferences or constraints has been affecting investment decisions leading to under-reaction to mispricing signals and further stock return predictability. Overpriced stocks with high ESG ratings did show the lowest abnormal return in the future, making them too good to dump.

Underpriced stocks with poor ESG ratings showed the highest abnormal return in the future, making them too bad to buy. Alphas were calculated using the Fama-French three-factor model and the Carhart four-factor model. Results were robust to exposure to traditional risk factors. Putting it in another way, it seems financial mispricing is more important than ESG for alpha generation.

Institutional investors: longer-term investors show aversion to environmental and social (ES) weaknesses

For US stocks, Abhishek Varma of Illinois State University presented the relationship between ES ratings and institutional ownership, based on 13F filings. His starting point was the idea that institutional investors are primarily driven by economic motives and not necessarily by altruism.

Novel in his approach is the distinction between positive and negative indicators of E and S. The main takeaway was that institutional investors, especially those with a long horizon, have asymmetric preferences. They avoid stocks with (negative) ES indicators showing weakness and hardly look at ratings showing strength.

Varma made the case that ES weaknesses create future liabilities (e.g. litigation and regulatory action). ES weaknesses were associated with higher crash risk (i.e. lower future skewness, bigger chance on delisting/bankruptcy).

Investors, in particular those with long-term horizons, have economic incentives to avoid firm-specific risks. While ES weaknesses are associated with higher crash risk, ES strengths appear irrelevant for crash risk.

Evidence in favour of the economic motives rather than altruism hypothesis came from an examination of changes in institutional ownership in reaction to changes in the economic environment regarding companies in controversial industries such as tobacco, gambling and nuclear energy. The results of the analysis were based on MSCI ES ratings and the Russell 1000 universe between 2001 and 2013.

To read more on our Environmental, social and governance theme, click here.


Raul Leote de Carvalho

Deputy Head of Quant Research Group

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