INNORBIS’ Lips da Cruz: “Market has not priced ESG and is working in the dark”

• They stressed that ESG ratings and scores are rankings that are inherently ineffective in comparisons
• ESG and SDG target considerations are not integrated in financial analysis and therefore rarely drive outcomes of rating agencies
• INNORBIS aims to avoid the pitfalls of precise backward-looking data in favour of predictive models to deliver sustainable outcomes

In our data-rich society, the financial community still lacks the ability to effectively measure impact, particularly of environmental, social, and governance (ESG) investments. This hampers efforts to scale up assets in a way that enforces positive change.

Sweden-headquartered INNORBIS, a predictive impact and sustainable investments business intelligence accelerator, is working to provide solutions by  challenging the orthodox approaches to impact measurement through predictive patterns-based analysis.

The intelligence provider applies measurement techniques in its integrated assessment model and automation to provide quality-assured metrics capable of supporting certified financial markets. Companies and countries in different sectors and regions can be incorporated in one global comparison based on relevant open source data.

The Global Impact Investing Network (GIIN) Annual Impact Investment Survey 2020 found that nearly nine in 10 impact investors are currently using an external measurement tool or system to measure impact, up from only 15% of respondents in 2010, when 85% used their own system.

In terms of the impact investors are seeking to make and how to measure it, the most popular alignment is with sustainable development goal (SDG) number 8: ‘decent work and economic growth’, with nearly three-quarters of respondents targeting this theme, according to the survey.

As part of the Wealth and Society dialogue, we asked INNORBIS CEO Angelica Lips da Cruz and chief science officer (CSO) William P. Fisher, Jr. six pertinent questions on the state of measurement in the sustainable investment landscape. The areas include the backward-looking nature of impact metrics, the pitfalls and usefulness of ESG rating agencies, and how to price and monetise human and environmental capital.

Chris Georgiou (CG): You’ve mentioned the backward-looking nature of impact metrics and contended that predictive models are more effective at moving the market. Could you explain the main differences between your proprietary measurement model and the external frameworks currently available such as GIIN’s IRIS+?  How do you connect the patterns and structures between the uncorrelated data?

Angelica Lips da Cruz (ALDC): I would not like to make any comparison between INNORBIS and IRIS+ as the efforts are quite different. Typical impact assessments address an intended outcome, such as, for example, access to clean drinking water. These kinds of metrics have two major flaws.

The first flaw concerns the fact that counts of people with access to clean water do not tell us anything about how much confidence we should have in the water quality being delivered. Nothing is said at all, for instance, about exactly how clean the water is, how accessible it is, how well integrated into existing cultural norms it is, or if the pump installed is actually being used by the children and the population in that location. Easily counted events are not usually the kinds of things we need to know if we want to measure impacts.

The second flaw concerns the fact that counts, ratings and percentages are not really measures. This is not some mathematical technicality with no practical consequences. On the contrary, our entire ability to make sustainable impacts manageable, communicable, ownable and profitable hinges on modelling and estimating individual level interval unit quantities with known uncertainties.

On another note, ESG rating providers also set themselves up with proprietary indicator schemes and make the mistake of prioritising how they compare against competitors. The misguided assumptions drive a focus on applications of pure data collection generating scores for investors, without any quality assurance as to the outcomes.

Avoiding the pitfalls of precise backward-looking data in favour of predictive models

William P. Fisher, Jr. (WPF): Past performance is no guarantee of future results, as is so often said. This is particularly the case when descriptive models restrictively constrain the depiction of results. What I mean by this is that we become locked into a very tightly focused backward-looking perspective when we define impacts in the specific terms of just one way of "measuring" them.

Traditional impact metrics prioritise the objectivity of data and seek to describe it. In the end, these metrics describe the data to death, killing the very thing they are supposed to bring to life. This occurs, ironically, because of the precision of the descriptions. The data are so specifically defined in the terms of the indicators that the impacts reported are completely unique to that local one-time situation. The impacts are then by definition not comparable with any other impacts.

How do we set up predictive models that bring the impacts to life? We do that by prescribing data structures prioritising the objectivity of a unit quantity that remains invariant across data sets, indicators and samples (persons, events, countries, firms, etc.). Instead of just focusing on the data, statistically, we advocate a science that situates data in relation to calibrated instruments and explanatory theory. Instead of just focusing on centralised statistical data analysis, we envision an actual economy of distributed actors thinking and deciding together in an ecosystem structured by instruments traceable to unit standards.

This is how we arrive at a forward-looking perspective that identifies repeating and reproducible patterns in past data—patterns we can use to predict the future with confidence because they occur so regularly.

The measurement method is not proprietary. Our applications and expression of widely used measurement theory and principles certainly are proprietary, and we encourage anyone interested to inquire, as to our services and licences.

CG: Is it correct that your index is currently performing 22% above the market comparison? How does INNORBIS incorporate a sustainable predictive model without any trade-offs?

ALDC: We have created an index for a partner working with large clients, with a performance 22% above the markets during the current pandemic crisis. The model is predictive, as described above. The "trade-off" refers to the still commonly believed idea that sustainable and impact investments inherently earn lower returns. That is an obsolete idea and not adding to efficient solutions to scale. The desire for sustainability is not anything new. Businesses have always wanted long-term, stable, predictable and reliable outcomes.

CG: GIIN’s 2020 survey found that 73% of impact investors use United Nations (UN) SDGs for at least one from of impact measurement. Are investments made by INNORBIS aligned with the SDGs and do you consider them a useful tool to measure your impact achieved?

WPF: Yes and no. Yes, they are aligned, and the SDGs are highly useful in being a product of an intensive global effort aimed at bringing sustainable development to the notice of people, executives, governments and others everywhere. They are not, however, anything like the tools we need to actually measure impacts. A series of three papers we presented in 2019 at a global measurement meeting, give the details.

ALDC: Thematic investments are as common as individual preferences. In a common communication, the SDGs represent a tool to address these investment preferences, and we are supposed to be able to serve the impact investing community with scientific measures, results and monitoring for those preferences.

However, INNORBIS is a neutral tool, in the sense of providing visualisations and comparisons of the sustainability positioning of the different players and products. As INNORBIS’ mission is to accelerate and contribute to progress in sustainable development, it focuses on the business and investments to be made towards SDG targets and solutions. The SDGs as official indicators are a reference and therefore are in the systems, so we can generate reports and analysis towards the relevant effects and results, i.e., the impact of investments made towards the SDGs targets. But this is secondary to our primary purpose, as we aim to close the gap between business efforts and investments, public and private, towards sustainable development.

Shared, quality assured standard units will help promote sustainable solutions

CG: Do you use the ESG ratings and scores provided by agencies such as MSCI ESG or Refinitiv? If not, why not?

WPF: Ratings and scores are not measures. They are ordinal rankings (ie. 1^st, 2^nd 3^rd, etc) useful in comparisons of less and more, but they are inherently unable to say anything useful about how much less or more. The same numeric difference means different substantive amounts of difference in the real world. This is true not only in comparing across indicators, but within them.

As people become more aware of the shortcomings of the statistics they mistakenly think are measures, they will see how and why their efforts at promoting sustainable solutions have failed so catastrophically. This situation will only change when we think and act together on the basis of shared, quality assured unit standards, such as those that INNORBIS is developing.

Establishing a standard method of evaluations

CG: You mentioned there’s no reward for ESG rating agencies to leave a negative score. Could you elaborate?

ALDC: Unlike financial analysis, ESG evaluations do not have a long history. The standard method of evaluation is yet to be established, and that's what we are working on. ESG or non-financial aspects matter a great deal for financial analysis and financial risk assessment.

Global disclosure of information necessary for evaluation is also under current development. The correlation of ESG evaluations by, for example, FTSE and MSCI, is very low. And still, it is only for dialogue. Morningstar Rating has, for example, a split between financial and ESG ratings, operating with older models and primarily with the listed larger players, with no reward if you leave out the negative choice. By this I mean the investor in this kind of situation has no opportunity to impact, or new growth opportinities with its choices. With ESG, these kinds of opportunities have yet to be opened up.

Another example is a credit agency rating, with grading systems dating from 1909, over a century ago, that are focused mainly on the assessment of the creditworthiness of an obligor as an entity, or a security or a money market instrument. It is an impartial credit analysis focused on the long-term ability to meet debt payments, nothing more. With only integral quantification of cash flow in different economic scenarios forecasted, accounting for some management strategy and regulatory trends, the committees' analytical discussions and rating actions are by default covering sovereign ceilings, with directors' and industry specialists' intervening discretionary actions not prioritising Green Deal and Paris agreement targets.

This kind of model does not assist in any way the great challenges and what we now have ahead of us with the debt structures at unimaginable levels and what I call a social “prime” crisis. There are financial ratios today that are almost ridiculous after the pandemic crisis, since the basic mechanisms are so disconnected from ongoing events that they cannot support real-time assessments and pricings in the way day-to-day market fluctuations do. ESG or SDG target considerations are not integrated in these financial mechanisms and rarely the main driver of these agencies’ rating outcomes in their current business models. This plainly needs to change, and we have already experienced many times that this action is not coming from them, but will come from disruptive technology, business model and other players.

CG: How does INNORBIS price the value of human and environmental capital so it can be monetised integrated into investments?

WPF: Measures of human capital, such as literacy and physical health, are routinely associated with variation in earning potential. Existing economics research documents are based on group-level statistical associations between educational achievement and income. We are making use of the same associations but at the individual level, where market valuations can be used to price individual literacy capital stock values. Unless you do that consistently within a capable system that end users can trust, you cannot achieve the kind of integration that you need, which is objective and neutral.

ALDC: Our current financial mechanisms’ legacy are the reverse of what we need. We have still not priced ESG in the market. Nor have we priced the impact of operational and internal processes and policies that business decisions have on our society, natural living systems and the planet. There is no systemic understanding or visibility of the effects of these impacts for better decisions to take place, and that is what INNORBIS builds its systems for. The business works in the dark if it does not take into account these “non-financial” aspects, the ESG or impact analysis, in the decisions being made.

INNORBIS is an independent system that does not jeopardise decision risks with any weighted equation. We do not believe one can operate with fixed weights. For example, we do not think it is effective to choose or weight companies in a measurement system emphasising any of the SDG targets. The end results of that kind of weighting are incoherent, but that is what many statistical and financial models do today. As it is necessary to admit that no kind of weighting systems of this kind can operationalise all targets at once, some then choose to exclude the entire sectors. But with a transparent comparison of all companies and investments together on a common scale, you can choose your own thematic preference of investment without the loss of calculating with human capital and environmental aspects.