Systematic sustainable investing: hype or trend?

The increasing prominence of Environmental, Social, and Governance (ESG) in investment decision-making reflects a growing awareness of sustainable and responsible business practices.

The increasing prominence of Environmental, Social, and Governance (ESG) in investment decision-making reflects a growing awareness of sustainable and responsible business practices. Sometimes even to the point of being controversial on its own. Quantitative investing, with its data-driven approach, has gained popularity in the realm of ESG and impact investing. Yet is often misunderstood. Criticism ranges from lack of interpretability, non-reliability to superficiality. In this article we explore the advantages and disadvantages of systematic ESG investing.

Quantitative investing employs vast datasets and complex algorithms to identify patterns and trends. Despite appearing opaque, it is actually the best way to comprehensively understand all aspects of a sustainable portfolio.

First, a systematic approach objectively measures a company’s sustainability performance, reducing the influence of subjective biases in decision-making. Most companies nowadays publish lengthy sustainability reports. If you look at any of them, you would quickly believe that the company in question is market-leading in ESG or is fully aware of its negative impacts and knows how to mitigate them. However, if all of them are market-leading, none of them truly is. A systematic and consistent approach sets all companies on a single reference and makes it possible to distinguish actual best-in-class practices versus superficial claims.

This brings us to the second point. Quantitative strategies are inherently scalable, making them well-suited for managing large investment portfolios. The 20 companies you look at manually and compare based on some metric might all turn out to have numbers worse than the 80 you did not consider. In the ESG space, where an increasing number of investors seek to align their portfolios with sustainable values, quantitative models can efficiently process and analyse vast amounts of ESG data. This efficiency not only enhances decision-making but also allows for the integration of sustainability considerations across a diverse range of assets.

Whether or not all sustainability metrics are indicative of long-term business resilience is a different question. Some of them might, most of them probably are not, as is the case with many other data sources. Particularly good governance practices have been shown to align well with a more general quality indicator. However, if certain preferences align with your moral values and do not have a negative influence on the portfolio, there is no reason to not incorporate it. Sometimes it is a free lunch to align your investments with your values.

There appears to be a growing trend to include as many ESG indicators as possible, completely disregarding the relative importance or relevance (e.g. financial materiality) of the different sub-indicators. This leads to the divergence of ESG ratings across providers. Each of them looks at different data points, transforms them differently. For example, with respect to a small group of peers, or against a larger group. This has a large impact on the interpretability and relevance of the ratings. The divergence in ratings is arguably a good feature. As in fundamental financial analysis, there are multiple ways to look at a company’s sustainability profile. Consequently, different approaches lead to different conclusions. The most important part is that you as an investor, or your fund manager, understands what determines the ratings and acts accordingly.

With more data comes more risk of data error, sometimes lower coverage. Using inaccurate data can lead to unreliable model outputs. Hence one of the most important steps is checking whether the input makes sense. Models for imputing missing data and correcting mistakes can be as complex and challenging to interpret as you can imagine. We are past the stage where missing data automatically means a bad or non-existing score. Care should be given that algorithms to impute and correct data are understandable and follow common sense. Sometimes a simpler model is better. It is a common trap to always try to apply the sexiest approach possible.

All of this is not very different from a bottom-up approach where a team of analysts thoroughly goes through each company in the portfolio. It is just scaled-up and systematized. To avoid any idiosyncratic risks, quant portfolios are usually more diversified to avoid company-specific risk that might not have been picked up by its models.

In conclusion, quantitative investing is the way forward for those seeking a data-driven, scalable approach to sustainable investing. However, challenges related to data quality, model complexity, and the interpretation of signals underscore the need for a balanced and nuanced strategy. Integrating quantitative insights with qualitative analysis and maintaining a keen awareness of the evolving sustainability landscape will be crucial for investors aiming to navigate the complexities of responsible investing successfully. Listening to investors and providing tailor-made portfolios is the way forward to properly align investments with the investor’s preferences and prioritize long-term stability and sustainability at the same level as financial returns.