Creating connections in ESG investing – the significant inroads being made by quant investors

One of the surprising outcomes from COVID-19 was the rise in importance of environmental, social and governance (ESG) driven investing. While the professional investment community in Australia had long been prioritising ESG in its decision criteria, the last 12 to 18 months has seen retail investors increasingly interested in using their savings to ‘do good’ and driving investment managers to offer ESG products that deliver performance.

The challenge for quantitative fund managers is to include these factors in a robust, data-driven, and systematic way. The good news, as Pierre Lenders, Head of ESG at investment manager CFM explains, is that exponential growth in alternative data sources combined with advances in artificial intelligence and machine learning place quant investors in a better position than ever before to get ahead of the game and make sense of how ESG factors relate to pricing mechanisms.

One of the few positive outcomes from COVID-19 has been an increased focus on ESG factors by investors and wider society. The effect of the pandemic combined with increased activism and scrutiny from shareholders means we may be at a major turning point for ESG investing. Once seen as ‘nice to have’ and embraced wholeheartedly only in a bull market, investors now acknowledge that we are witnessing a fundamental shift towards sustainability.

According to Morningstar Direct, flows into sustainable funds increased in 2020, and investors allocated $12.2 billion into ESG funds in the first four months of the year as the pandemic gathered speed. That was more than double the amount directed into ESG funds over the same period in 2019.

ESG and quant investing

Quant managers, including CFM, differ from traditional investment managers who invest using both quantitative and qualitative analysis. Our investment strategies capture and analyse data using mathematical and statistical models and techniques, with a three-step process - analysis and statistical testing to identify patterns, correlations and factors that drive price movement, building models and backtesting, and finally implementing models. 

The nature of ESG factors means it has historically been challenging to analyse ethically driven restrictions and factors in a systematic and meaningful way and then connect these to financial value. Environmental factors include everything from environmental management systems, pollution and carbon emissions to product innovations designed to improve environmental protection. Social factors focus on human rights, safety standards, public health, diversity and ethics and governance on the behaviour of the board of directors, shareholder rights, diversity, and other non-financial factors. Such factors are difficult to measure and even more difficult to compare.

Reputable ESG data score providers, whose job it is to score companies based on ESG factors, do exist. However, given there is no one way of measuring, each of the scores can be measured differently which means final scores for the same company can vary across data providers, making it difficult for investors, and particularly quant investors to compare like for like. Contrast this with accounting frameworks that look at costs, income, and expenses according to specific rules, and the difficulties are clear.

Quant investors may now be ahead of the game

Advances in technology and our ability to collect and analyse data mean that quant investors are making giant inroads into ESG investing, and there are two main reasons that quant investors are increasingly able to find statistically significant patterns in what is inconsistent and ‘dirty’ data.

The first is that scrubbing dirty data and making sense of it is something we have a great deal of experience with; it’s our bread and butter, and the tools at our disposal are becoming more sophisticated all the time. Secondly, alternative data sets are growing exponentially. This creates larger and larger data sets which we are becoming better at interpreting using technology – specifically advances in artificial intelligence and machine learning.

It is in alternative data sets that quant managers have a distinct advantage. Alternative or unstructured data accounts for 90% of data produced in the world, according to Forbes, is growing exponentially and represents the largest type of ESG data source. Structured data is data that is clearly defined with patterns, which make it easily searchable, whereas unstructured, alternative data is everything else – data not easily searchable, like audio, social media postings, and machine-generated unstructured data such as satellite imagery, seismic imagers, landforms, military movements, traffic and weather data and so on.

Unstructured data can’t be implemented directly into any investment model, but must first be processed, researched, and analysed, using sophisticated statistical techniques, which is why quant-based investors have a significant advantage over other investors in extracting value. A good example of this is neural networks - which are proving valuable in extracting meaning from unstructured data. A neural network is a type of machine learning technique in which a series of algorithms allows a computer to learn to recognise underlying relationships in a set of data in the same way a human brain does. These neural networks are becoming increasingly sophisticated and therefore effective.

At CFM, we continue to develop new strategies which make use of the large data sets more readily available - taking a scientific approach to using new data sets, extracting information, and building signals which can be used as predictors for stocks. Right now, it’s the ‘E’ in ESG which is most important, because if we don’t fix that, everything else is irrelevant. Importantly, it is also the factor that is the most quantifiable now.

In recent years, corporate commitments to more sustainable business practices have become increasingly prominent, most notably in relation to the environment and climate change, and this trend is accelerating. With stakeholders raising their demands to curb emissions, and as greener technologies are developed, stricter policies are put in place, and as carbon pricing becomes more widespread, there will be clear winners and losers across a number of sectors.

At the same time, we are becoming better at measuring the ‘S’ and the ‘G’ factors. There’s no question that executives are increasingly judged on ensuring that proper diversity and strategies are put in place, but how, as a quant manager do we measure those things? One way is through advances in artificial intelligence, specifically natural language processing, which allows us to ‘listen’ to billions of conversations in all places and outlets, even those which may not seem to be directly material to the market.

We hope to be among the first to be able to detect signals that are material to price. For example, green transportation was no doubt being discussed in conversations over 20 years ago, but these may have been in academia, rather than in investment circles. However, as the conversation moved into the mainstream, discussions about green transportation can start influencing price. We’re starting to hear those conversations about green food production now.

Looking forward, rather than to the past, to learn lessons is crucial. Looking to the past is problematic because ESG doesn’t have sufficient history to make backtesting meaningful. What we need to think about is the way ESG themes will drive change and then invest systematically based on more than one thematic – as we try to identify potential drivers of value in the future.

As a quant manager, CFM seeks to leverage the growth in sustainability-related data to deliver alpha for investors, building on CFM’s considerable expertise in the statistical analysis of large, multi-dimensional datasets and in systematic, process-driven equity trading strategies.

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