Unleashing the Power of Technology and Data Analytics in ESG Investing

All right. Uh, God. It's funny lighting. Uh, good, uh, good morning everybody. Welcome. Uh, my name is Mark Zurich. I'm a professor, uh, at CBS. Have been here, uh, a little more than 20 years. Uh, but a few years ago I started, uh, a course on ESG investing. And, uh, my role, I think, is to facilitate discussion. So, uh,

I won't waste any of anyone's time, uh, talking about me. So, uh, I'm gonna pass it to, uh, Richard, Rachel, and Manish and let them, uh, introduce themselves and talk about kind of how they hit, how they relate to the field we're gonna cover today. Sure. Uh, you guys can hear me. So, I'm Manishh. I'm in oh three, uh, graduated in oh three from Columbia. Uh, for those of you who are students, I'm also a career coach. So, uh, I coach on entrepreneurship,

a lot of intersectionality, interdisciplinary kind of work. So right now I'm coaching on ESG and climate and physical risk. Uh, my background is startups. So before Columbia, I'd done startups, we'd sold them. They were usually tech companies that we'd sell to software, uh, software companies. I got involved in ESG when we invested in a geospatial company, uh, RS Metrics, which track the world's largest retailers, factories, and we would track economic activity using satellite imagery, and we would sell the data primarily to hedge funds who would pay a lot of money. And the markets, obviously,

you guys know how capital markets work and trade on that. So in 2018, we had the opportunity to buy the company out, and we did a management buyout, and I joined as the CEO, and we sat together with our board of advisors. A lot of our board, by the way, is from Columbia Business School, Bob Hertz, Malcolm Harris. And we said, what's the biggest problem that we can address? And a lot of our customers had started coming to us and said, you're tracking metals. Can you also track the water usage of the metal companies? You're tracking factories? Can you track how they're encroaching on biodiversity, what their, uh, uh, footprint is for land usage? And we started putting our heads together and we said, this is probably the biggest opportunity we'll have and the biggest market we'll have to address. So in 2019, after we bought the company out, we pivoted to ESG, and we started co-developing two platforms.

So we've developed these two platforms with Google, and we've built on that. And what we do is we track the world's largest companies at an asset level. So we track every company. Let's take any company here, Chevron. We track all their refineries, and we have about a hundred different metrics that we provide. So it's a direct measurement, and we sell those metrics to the companies, and we also sell them to the capital markets. So, uh, been been a fascinating journey. And, uh, you know, I finally feel in 23, we've been beating the drum on ESG and what we call ECP for the past three, four years. But I think finally in 23,

people are realizing it's not just something you have to do to get a tick mark, but it's actually a competitive business advantage to know what your environmental climate and physical footprint is and where the opportunities are. And thanks for having us. I also, uh, talk sometimes on Mark's class. And Mark, when I was in school, had the probably hardest class to get into derivatives, which I never took.

Hi, I, I'm, I'm Rachel. I am, um, not a Columbia Business School grad, but really happy to be here and had the pleasure of, of taking a class with Mark up in, if I was at Cornell. I'm Citizens Financial Group Head of Sustainability. I've been with Citizens for about two and a half years, and I was the first dedicated hire to focus on ESG and sustainability. My role has evolved a bit over time, but, um, but at a headline level, I'm responsible for developing the company's ESG sustainability strategy and, and driving it forward. I've got reporting on my team, um,

and in that capacity and, and also in the ways in which we interact with the business functions, both on the, the commercial side and on the retail side. I'm, I'm really interested and, and spend quite a bit of time engaging across the industry on this topic of, of ESG data, which, which I think is incredibly important. Before Citizens, I was at JP Morgan Chase for about a decade in a couple different roles.

Most recently focused on headline sustainability commitments, the sustainable financing target, the inaugural sustainable bond issuances. And I built out the, the platform for sustainable investing within Asset and wealth management. Um, which again, is, is so dependent upon ESG data, which, which has evolved quite a bit over the past several years, but I'd say still has a lot of room for growth, which I hope we'll dig into in this conversation.

Wonderful. Um, see if this microphone works. Yes. Okay. Hi, everybody. Um, my name is Richard Rottenberg, um, CEO of Global AI Corporation. I'm a graduate of, uh, Columbia Business School from 17. And, uh, my journey started actually in Wall Street, um, working as a high frequency algorithmic, uh, trader quantitative, doing quantitative research on, um, several investment banks, internal hedge funds, uh, Goldman Sachs, Deutch Bank, and others. And from there, looking at large, uh, data sets, um, that impact financial markets, at the time it was climate only, uh, but then, you know, expanded to other, what we call ESNG, um, to impact, uh, uh, valuations and and such. And in parallel,

I was a researcher at the Lawrence Berkeley National Laboratory. It's one of the world's largest super computing labs where we look at, uh, systemic issues related to, um, financial stability, global finance, and, uh, climate stability, climate, uh, issues, climate, uh, uh, change. Uh, so it relates to financial markets. And in addition, um,

also part of various task forces at United Nations, uh, specifically on indicators such as 12.6 0.1, which is the only sustainable development goal that addresses, uh, corporate, um, reporting. And we applied, um, large scale artificial intelligence and big data, uh, specifically natural language processing across, um, several, uh, uh, thousands of companies, uh, globally to inform this indicator for, for the United Nation official, SDG and such informed policy for various governments. In addition, we, uh, wrote a paper with the, in collaboration with the United Nation Pension Fund on generated generating alpha, uh, using, um, AI driven, uh, ESG factors, and in which we prove that you don't have to sacrifice returns to be sustainable. And in addition, um, I'm also part of a, um, editorial board of a journal, machine learning and ai. And, um, uh, I would like to address, uh,

today some of these topics, uh, that we found at our company. We also, uh, I'm also president of the global, um, algorithmic Institute, which in which, uh, we also is a think tank, which we have AMOU winner with the Ted's an agency of United Nations, uh, to perform, uh, various, uh, researches, uh, or using big data in the area of ESG and specifically the sustainable development goals. And, um, I think AI has a big promise, uh, to deliver on this area and look forward to discussing this today. And, uh,

thank you very much. Uh, professor Zurich, one of the best professors I had at Columbia Business School. Highly recommend his classes. That's

Right. Now I remember. I remember actually. You're not getting a better grade, though. Yeah. Uh, well, thank you, the three of you guys for coming and joining us, and hopefully, um, all of you have questions you want answered, so we'll make sure you've got enough time to do that. I guess I'll, I'll start, uh, with, uh, Manish, um, can you talk about, talk about one of two topics, either the, um, interest and focus on, um, environmental climate and physical risk me metrics, um, metrics, or, uh, what do you see as current priorities when it comes to data needed for decision making for corporate clients? Sure, sure. So, uh, usually when, when we think of ESG,

the lens that we look at it through is always capital markets, right? In capital markets, we've been used to looking at through a ratings lens, right? So you're familiar with the N SROs, S and p Moody's, they're usually rating agencies, and they're using the same methodology that they've always used, which is to rate more complicated instruments, debt, different kinds of instruments, sovereign bonds, et cetera. And they're trying to apply the same business model to coming up with ratings for environmental, social, and governance, right? Uh, when we started looking at this space, uh, mark, I'm gonna go, I'm gonna deviate a little, but I'll bring it back. We, there were three ways that we saw people were doing it. One way was the old way from the 19, uh, from the nineties or two thousands before the UN came up with the SDGs, which were CSRs corporate sustainability reports. They were really long PDFs, but they were feel good reports, right? The second part in the two thousands and 2000 tens was these bigger companies like S and PMSC, I started doing ratings, but the ratings they did were based on company reported numbers, which didn't really mean anything.

The company didn't really know how to measure what their water impact was. How much methane were they releasing? Did you have impact on marine life? They, nobody collected the data, especially at their company level, right? I don't care what's happening in India. I wanna know what your plant doing in a particular city, in MDA bot or Bombay, right? So this is, this is what the market was when we were, when we were looking at it, and what we found was the ratings and the capital markets needed more actionable data, and they needed data which was actually provided to them, and it was auditable, it was verified.

So that's one way we look at it on the corporate side. Uh, on the capital market side, what we've seen is, uh, there's just more focus on opportunities. Now. People are thinking less about, do I need to tick a box to go say that I've qualified and I've done my reporting. The capital market players have really started looking at opportunities.

I have a portfolio of real estate assets. Which of my real estate assets are exposed to alluvial flooding? Which of them are exposed to fluvial flooding? Which of them are exposed to coastal flooding? Do I have, uh, uh, assets in Mexico, which I never thought might be exposed to flooding, but we saw what happened with the hurricane couple of days ago. I think it was Otis, it changed from a storm to a category five hurricane in couple of hours, right? So a lot of the models that we've been using in the capital markets for insurance, for investing are just wrong, and they're not really set up to report actual data at a company level. So what we've started doing, and what we've started seeing is it started off in the capital markets, the asset managers, the hedge funds, everybody else in the capital market saying, I want to go invest, and I think I might make some money. You know,

it's double bottom line focus to really going to the corporations who are using this, like they use any business intelligence tools, why wouldn't the board of the largest oil company in the world be interested in, say, that's Saudi Aramco. What's the water stress for one of my plants? What's the heat stress? Uh, what's going on on the soil part of it? Right? So the way we are seeing it, you know, I'd love to hear what you guys are seeing is corporations have started using it as business intelligence, and it's a competitive tool. The better you are with business intelligence, the better CapEx you'll do, and the better you'll be positioned to actually have the transition if you're focused on transition risk. Ra, Rachel, uh, Manish just mentioned some of the challenges, um, in, in measuring ESG, uh, data. Can, can you kind of, from a practical standpoint, as a user, talk about it from, uh, your perspective? Yeah, AB absolutely. Um, and, and may maybe, um, before I dig into that question in particular, I'll, I'll spend just a minute on, on terminology. We,

we've spent at Citizens a lot of time talking about these different terms and, and what they mean. And I, I think it's worth mentioning that ESG investing or sustainable investing can actually mean a lot of different things to, to different people. I think of there being a, a spectrum of, of ESG or sustainable investing where you could say on, on one end of the spectrum, mm-hmm. Investors are, are screening out and, and just looking for, for particular investments to, to take out of the universe for consideration. Um, there's a lot in the middle. And then on the other hand, you have what, what could be called impact investing, where you're looking for something like a double bottom line. Um, and so when, when folks make reference to categorically you, you can't get outsized returns or you can get outsized returns in, in ESG investing, I think it's important to acknowledge the nuances and the fact that ESG or sustainable investing can mean different things to, to different people and to different companies. That this question on,

on the challenges is, is such a good one. I, I'd categorize the challenges that I see related to ESG data in, in two categories. The, the first is around availability. Um, and so, um, you know, folks are looking for, for ESG data related to the environmental, the social, and the governance. Um, and in certain cases, the data just, just doesn't exist. It, it doesn't exist for, for banks, for investors, for other stakeholders like regulators to, to consume. Um,

but we know that we need better availability in order to make better decisions within, within the business world. And so, one of the things that at Citizens that we're doing in order to increase the availability of data is to engage with our clients. We know that, that the, the best data is gonna come not from estimates, but from the operators themselves. And so we're looking for different ways, and we know that across the industry, there's, there are efforts taking place to, um, to look for how we can develop more data so that it can be consumed and, and made available. The, the second big issue is, is around the accuracy. And, and that, I, I'd say, exists across the board,

across the e, s and G. It, it's certainly an issue when we think about environmental data and, and climate in particular. As, as a bank, having worked in the financial industry for, for many years, there's a huge amount of focus on our Scope three category 15 finance emissions. Mm-Hmm. Um, and we can talk about that later in, in relation to the SEC proposed climate rule. Um,

but that's a great example of where there, there's so much room to grow on, on the accuracy and, um, and while estimates exist, and we shouldn't let perfect be the enemy of good, there, there are, there are many flaws in making business decisions based on estimates in particular, because you can't, in many cases, differentiate between the players that are best in class and those that are lagging. Um, that's, that's a, a big area of opportunity. And, and again, I think an, uh, a place where we need leadership that, that can, um, that can include participation from business, from, from academics, from nonprofits, from consultants. I don't think that anyone's really, really figured these problems out yet, but it's really important in order to, to drive progress to a more sustainable future and to a place where we can make better business decisions around it. Uh, well going, uh, feeding on Bo both what Manishh and, uh, Rachel have said, I'm just gonna pass, pass it to Richard, but, um, Richard mentions an, an area he's worked on, um, which is obviously probably the two most important words of 2023 of, of artificial intelligence. But can you, uh, talk about how that can be used to address some of the, um, difficulties, um, in, in producing, um, usable, as usable as possible, uh, ESG data? Yes. Yeah, absolutely. Um, so one of the interesting developments in terms of ai, big data and ESG, is the fact that historically, as manage mentioned, uh, companies have been reporting data.

With CSR, you have a sustainability report, and however, all these approaches lead to many cases greenwashing, because companies tend to selectively report only on issues where they have data and they look good, and they omit many others. And in, in addition, uh, data is aggregated at the global level. So it's hard to desegregate, um, something that's going very well in one location and very bad in other locations, especially emerging markets and the global south.

So for this purpose, AI has been, uh, as we saw in our paper with the UN Pension Fund, where we benchmark against M-S-S-I-E-S-G scores, um, a lot of the traditional data providers have used, have based their scores based on self-reported data from companies. But again, as we say, this tends to be greenwashed. So what does AI can help with? Well, it can compliment the self-reported data to scan global news, um, blogs, social media across doses of languages and fragment.

Highly fragmented sources use technologies such as natural language processing, which can scan millions of documents and organize this era, categorize it, um, link it to specific SESG indicators, and basically help quantify both positive and negative issues that impact, um, the company. And another advantage of this that we saw in regards to generating alpha is the timeliness of the news. Because when you see scores that are based on self-reported data, such as M-S-S-C-I, they tend to move very slow throughout the year. You may see a straight line six months, a year, and then a change. You know, it's very slow moving. But when you have, say, AI driven data that scans a much higher number of sources in multiple languages, you see a much more dynamic movement to which you can react much faster.

And the changes actually are the key to generating alpha, because if a company as, um, you know, you guys were saying like, uh, the, there's a negative factor impacting, uh, a company like in, um, like you said, floating risk, for example. Mm-Hmm. An event, a negative event that materially impacts a company. Um, by the time this report, this shows on the self-reported data is gonna take a month or a year, but this should, the algorithm can capture this, uh, instantly in either, uh, intraday or in, in a daily basis. And therefore, this can impact the scores, which can lead to an adjustment on the portfolio, either weight less the company, um, on the portfolio, or short it, you know, uh, make an alpha a strategy to generate higher returns. And just to, uh, another point is that, uh, on the multi-language aspect is a major development because we saw not just the ability to, uh, some cases we did with the United Nations, with UNDP for corporations in Latin America, how operations in Latin America, we saw that, well, I'm not gonna mention names, but this com particular energy company had a very positive footprint in Europe, but very negative in, in Latin America. Hmm. So,

with ai, you have the ability to geotag and desegregate this data, uh, because you, you looking at local sources, non-English sources, out of the big universe of data coming out, only a subset comes in English to what you see in the Wall Street Journal, Bloomberg News. But there's a big set that's not shown, and it, if it ever shows, it comes late. And looking at it in the original language, uh, it's, it's very significant. We found sometimes 70% or more of the data available is in the local language, which is very significant. And we, we see very few players, even today just starting to catch up to this new trend because you have new features of data, you know, language, uh, location, uh, timeliness, and all these factors that we haven't seen traditionally with, um, more, um, old school, uh, ESG type of self-reported data. And this obviously generates many opportunities for investments and alpha and risk management and so on and so on.

Good. Yeah, I, I'm going to kind of change paths a little bit, but try and draw on something you said, and I'm gonna ask a question for the three of you that were not in your question. So see, don't answer it unless you, you don't wanna, in my, um, ESG investing course, and we're gonna cover this next Wednesday, um, we talk about something that Richard raised, which is whether there's, um, alpha in a, some sort of analysis of ESG risks and opportunities.

Uh, and related to that is whether you can create a portfolio that outperform the market and become sustainable. And, and, and kind of the point I make to the class is that if you believe that, then you kind of believe that the market is not as good at pricing ESG risk as it is at pricing other risks. Because a lot of people would argue that markets have become more efficient, especially public markets, and it's kind of, it's hard to beat the market Mm-Hmm. And that, that's another debate not within the confines of this, of this, uh, panel, but, uh, but I think what our speakers are saying is maybe it's less efficient in this part of the market. Yep.

And I'm just curious if I can you, uh, want to attack that question? Yeah, I mean, I think, I think that's right on. It's, it's the democratization of data, right? So what Richard is talking about, what we've talked about here previously only, and I can tell you in the asset management space, it's about who spends the most money to get the most real time data, and who acts on that data the fastest, right? Which is why hedge funds pay a lot for alt data. In this case, after having sold environmental climate physical risk data. One thing that's very surprising for us is the insurance segment has really started using our type of data, which is, and you know, we asked, why are you, I was on an insurance panel, and it's like, why are you doing it? Because the reinsurers on the insurance, uh, cap table, if you will, the Swiss re, you know, gen res, a lot of the reinsurance companies are on hook for individual assets, right? You have a flood in Germany who's insured X amount of a plant, ABMW plant, or a VW plant or what have you. So we've seen insurance companies, if you think of a reinsurance company having a portfolio of properties that they're reinsuring, let's say a thousand properties, they've got really good data for maybe 500, maybe five, 200.

They have decent data, that 300, they have no idea. And that 300 properties will pretty much kill their p and l. And there's a number which tracks that, which is the amount of underinsured, uh, capital loss that they have. I think in 21 it was 150 billion. In 22, it was 300 some billion. You know, just think of all of these assets that companies are insuring in a portfolio that are underinsured, right? So we see Mark, we actually see insurance companies pricing very particular risk. So you say,

what does earthquake have to do with climate change? Well, it doesn't, but it's a physical risk. And now using different types of ai, we leverage off a lot of our partners. We can give you asset level earthquake risk. Maybe that's relevant. Next time you're thinking of your supply chain, you're a very big company and saying, where am I gonna put a factory? Is it gonna be in this area or that area? And then tying in some of the other physical risks. So we definitely, you know, we, we see companies and industries actually using it and outperforming, but there's always, with every new tech, there's always the adoption curve, right? There's the early adopters, sometimes they're smaller, more focused companies. And then there's the later stage companies. One panel I'm going to be on, um, uh, it's a space conference.

And some of the really forward looking people are saying, listen, if we really manage to mess the planet up, you know, which I think we're doing a good job of, you know, where do they want to invest? So they've started investing in space, which is low earth orbit, right? Stay in space or what have you. That's one aspect of capital markets saying, I need a plan B. Yeah. And Either, Rachel, go ahead. Yeah, I, I'll, I'll be brief. I, I'd, I'd underscore the, the insurance industry is, is leading in this space, right? And, and within the financial industry, we're, we're getting a lot of insights, intelligence from the way in which the insurance companies are thinking about climate risk in particular. And it's certainly increasingly top of mind for them on, on this question of the, the efficiency of the markets. I, I make the argument that, that it is still less efficient.

The markets are still less efficient on, on topics related to, to ESG and sustainability, but they've become increasingly efficient. So, so there's good research that shows that ESG funds had a greater opportunity to outperform years ago, I think, because there were fewer people thinking about this body of work. There were fewer conferences like this and, and people teaching in, in business schools and elsewhere about what it means to invest within ESG or, or a sustainability lens. But I think there's still, still plenty of opportunity. And, and one of the reasons for that, I'd say, is because there's not great data that shows this company is outperforming on the E, the S or the G mm-Hmm. Um, the metrics exist.

We know that there's a growing industry of ESG raters, the credit rating agencies are increasingly interested in incorporating in some way ESG factors into the analysis that they provide to, um, to investors. But, but there's not consistency, right? There's much more consistency from credit raters than there is from ESG raters and, and that provides opportunities to outperform. Yes. Yeah.

I would like to add that there's a very interesting research by MIT paper that compares a lot of the, um, ESG scores from, and it's very interesting that, you know, mobile ExxonMobil can be the, the, the small, the lowest on one rating and the, the highest in another one, huge discrepancies. So you try to apply all these top portfolio is basically noise is, is, doesn't generate any alpha. But what we've seen is that when we start to capture, um, a large nu larger number of data points through AI and using unstructured fragmented data, and you accumulate that, we found there's a statistically significant, uh, relationship between the, so-called ESG score, AI driven ESG scores and evaluation, typical evaluation measure, like iteration and so on. And what impacts is not so much the level of the company, but the changes, how fast you can capture these changes is well determine your ability to, to profit from it. And from the hedge fund lens, sometimes ESG data is one type of just one type of non-financial Mm-Hmm, uh, risk. So they don't necessarily make care about the environment or people or anything else. They're,

they're addressing risks that are material to companies, uh, and, you know, whatever they wanna call it. And this is interesting because, um, you start to see ESG as a type, a very important type of alternative data. And essentially the more data you have in a more timely manner, the more you can profit from it. And, um, so from that perspective, I, I would like to add also that in many companies, especially tech, we see a lot of the valuation being intangibles and byproduct of also perceptions of companies in which any, um, crisis or any, uh, event can massively impact valuations, um, either short term and long term. And these algorithms can help capture social media sentiment and other things that are not in the balance statements. Yep. And this is a major game changer in the,

in the space of investments for, not just for hedges, but also for traditional asset managers on how portfolios are constructed from a risk return impact perspective. Right. Okay. Um, I guess another broad question for any of the three of you. Um, can you offer your views on what you think, if you are in a regulator's seat, whether it was a government regulator or even you are a trustee at one of the, um, sustainable accounting organizations, what, what should they be doing? What, what should they be thinking about? So, You know, we, we actually partner with different types of regulators, right? So on the financial side in North America, we'll partner with the SEC.

So the SEC has to come up with reporting standards for publicly traded companies, which then the publicly traded companies have to report, and then the big four or the audit and insurance companies have to go and audit, right? So we've partnered with them, they've come to us and said, what can you do? What is technologically feasible in 21, 22, 23? And that's on the financial regulator side. On the environmental regulator side, there's other environmental regulators, which are usually, uh, national. Some of them might be continental, who are actually creating standards, right? So IFRS, I think you have, you have a speaker. So we share a board member with them. And if f so there's a lot of consolidation going on on the regulation side, where the accounting organizations like fasb, there used to be one called SS B, is now creating accounting standards for global capital markets, which are then going to be implemented globally.

So if you're investing in a company in Europe, or you're investing in a company in India or in the us, you will have similar standards that the companies are reporting on. So we see a lot of collaboration, and I, you know, I've seen a lot of this collaboration at Columbia when previously we would do our meetings and there'd be somebody from, uh, uh, you know, capital markets, let's say a large asset manager, JP Morgan, there'd be somebody. Now you have different people from different disciplines putting their heads together from accounting, from regulation, from legal, obviously where we come in, which is on the data side and the tech side, and coming together. So we, uh, we see, uh, regulation as a two-way street, where, where, you know, a lot of companies are sharing, and it's the companies themselves, not just the investor saying, we can't report this because we don't know what's there. We don't know what our footprint is. We don't know all this stuff.

And now with tech, and a lot of it is what, what Richard said, AI makes a lot of this stuff easily discoverable. So you can probably have a time, maybe in 24 when companies have a really good baseline. Before you do anything, you have to baseline what your risk is. How much did I emit? How much did I use that? Once there's baseline, then you'll be able to benchmark. Once you can benchmark,

you can track and reward and, you know, plate the capital market. Uh, you know, the, the whole system and the capital markets, This collaboration, which is so important, yep. Just can't happen quickly enough. We often talk within the financial industry about the financial soup, sorry, the alphabet soup of, um, of, of these standards that exist. And,

and it's, it's really tremendously inefficient that there are so many different standards. Um, at JP Morgan and also at Citizens. I spend a lot of time engaging with our, our investors and other stakeholders to figure out which are the standards that, that you care about, which are the ESG raters that you care about, because these sustainability teams don't have infinite resources, right? So we need to think about which are the ESG raters that do we need to engage with, which, which are the reporting standards that we need to, to provide an index against, um, because it's important to our stakeholders. And we know that ESG data is increasingly important, but there are dozens, hundreds of these standards of these raters that exist. And it's certainly not efficient for, for shareholders, for, for sustainability teams to, to be investing in all of them. Um, certainly there are conversations taking place across the industry to try to align. Um, regulators are, are, are listening,

are engaging to think about the standards, the raiders that that matter most. But, but right now there's not great organization. Um, and there continue to be a lot of different players out there, particularly for small companies that don't even have a dedicated sustainability or ESG person. How can you get smart on these topics to figure out where, where really the investment should take place to be sure that the ESG data that's important to stakeholders is made available in a, in a somewhat efficient and, and easy to access way. Richard? Good? Yes. So we have, uh,

some good experience working with the regulators. Um, actually we have, we were working with two former, uh, CEOs of ASB and several UN task forces and multi-stakeholder efforts to that congregate a lot of these players. And, uh, as managed say, we've been seeing a massive consolidation happening today. As you can see, uh, all this soup is consolidated into two different groups. We have the group that's BA focuses on financial materiality, which is basically what matters to investors.

A lot of these based on SB and as we see the IFRS, I-S-S-S-B, which is, you know, basically consolidated many of these. And on the other hand, we have players that address, so-called stakeholder materiality or double materiality, which means factors that have impact beyond investors. And just a quick, very interesting note that I found very interesting is when covid happened, um, if you look at the sasb, the only factor that addresses anything happening with humans is basically employee health. And, and that's how you track anything happening with covid.

Whereas in the un you have a, a number of, um, metrics and sub metrics that are associated with, um, you know, wellbeing and health and others across nations, and that you can see massive discrepancy, but obviously one is more broader and much harder to get data from. As, as you mentioned already, data is already very, um, sparse. So, but again, AI can help gather everything that's available in across multiple languages and make this process, um, uh, more robust.

And ano, another major trend that I see on this, uh, um, regulation is, um, uh, as, as, as we drive towards consolidation, um, we see many companies, um, um, selectively, uh, addressing factors with, as mentioned before, greenwashing is a major fact, major, uh, risk. So as this become, uh, regulation becomes mandatory across European Union and other places, and SEC, um, we hope to see companies becoming more transparent in, in terms of the reporting and this, uh, contributing to, to, um, uh, clarify a lot of these, um, uh, major risks, uh, that, that we see in companies. And, uh, just to finalize, uh, on the un, we see a very interesting unique approach, specifically the Unad isar, which addresses specifically the balance sheet of companies and tries to map sustainable factors on, on the balance sheet data, basically hard data, and then aggregate this at the country level in, in the, so-called voluntary national reports that countries report to the un and which will eventually become part of the credit risk ratings. Uh, so from this perspective, we have a micro approach that aggregates company's data into a macro factor that ultimately may impact even probably potentially fixed income assets and sovereign, uh, debt. So this is a very significant, uh, factor that we're very excited about. Thanks, Richard. Uh, we have some time for questions from the audience,

and I think someone's going around with, uh, microphones. We have any question? Could we sue someone? Great. You, um, I have a couple of questions just maybe to touch on that last point. With regards to sovereigns, I'd be curious as to what metrics are now available that would be helpful to investors that are looking particularly at emerging markets. And then my second question is, in terms of, um, smaller asset management shops that don't have very large ESG teams, um, what are your thoughts in terms of some of the opportunities to maybe outsource and leverage other, um, other, uh, efforts in this space? Um, other than just, for instance, utilizing MSCI data? I can, you know, I can, I can go on the second one. I didn't, I didn't, uh, uh,

hear the first question is the way data plays out, it's the same exact way, right? I remember when we sold to hedge funds, the first time we would charge, Hey, we can track a Walmart, right? And we can track the traffic at a Walmart, or we can track a factory and tell you how many, uh, tractors John Deere has produced. And it was very expensive data. It was only x amount of people could produce it, but as the company involved, as the data, all the data became regular data. It was available on Bloomberg, all the smaller asset managers started using it. Where we think the market is going with all data is the companies themselves have to start using the data.

So our largest user for our old legacy products ended up being companies. Uh, Walmart or a Home Depot would come to us and say, I want to track my own traffic to figure out why I am losing customers to Lowe's. Or Lowe's would come to us and say, what's the impact if I shut my, uh, uh, southern stores on a Sunday, right? So we think the way it's going right now is companies are going to use embedded enterprise software companies like NSAP, like a Salesforce, and those are the kind of companies we are collaborating with to produce data that the companies themselves will report in a year or two. The companies will report it the same exact way that they report the financial data. And if everybody has similar data, right?

Even if you're a small hedge fund or you're a small asset manager, a hundred million, you don't have a big budget, you will at least know that the data that the company is reporting is audited, right? It's been audited by the big four, it's real time. It's not data from 21 that you're reporting in 24, and it's verifiable. So, you know, the way we think it's gonna play out in a couple of years is companies using existing systems or record, everybody here who works for a big company either runs on SAP, or, you know, one of the Oracle ERP apps or PeopleSoft or Workday are one of those. We think those are the companies who will actually start incorporating it into their systems and obviously reporting it to the markets through regular distribution channels, which add, in my opinion, you know, they might add value or they might not, but it's in the interpretation of the data where the asset managers might make money. One guy will say, listen,

I believe water stress is a really big issue for mining companies, right? Case in point, water stress and Chile. So different areas, different interpretations. I, I'll just quickly comment on the, on the second question as well, and I, I'd say it, it's really inefficient and, and it's difficult for small companies. What, what I hear from large institutional investors is that they're consuming the ESG raters, but, but by and large, they're not using those ratings as determinant factors. Rather,

it's an input into the decision making process. So you can imagine what, what all of the resources invested across those teams that institutional investors look like, and then what does a small investor or, or some other stakeholder interested in this ESG data, what, what, what can that, what can that individual or group do? Um, and, and that's where I see a huge opportunity. So it's a, it's a challenge, but it's also a huge opportunity. I, I'm, I'm really intrigued by the movement of the credit rating agencies into this space. Um, some of them are incorporating ESG data. Some of them are developing standalone ESG ratings. Um,

I, I generally would take the position that we should be thinking about ESG topics as woven into all of the other things that a company is doing. And so we should, we should think about it in that way, which, which is why I think looking at ESG from a credit rating perspective is so interesting. But unfortunately there, there's not a great solution. I'd say there's not a great solution today, and it's one of the places where, where we need this, this industry collaboration, we need new ideas, uh, because a, a great solution that's really efficient for the market doesn't exist today. Yes. Just to address your first question, uh, there are major gaps on sovereign, uh, you know, sustainable reporting. I would say the major resource available now is from the, uh, high level political forum for the United Nations.

That happens every year where countries submit their voluntary national report, which is the equivalent of a sustainability report of a company. If you think of a country as a entity, uh, corporate with a balance sheet and traditional credit risk, and then use sustainability risk being the BNR, that's very interesting. And another development that we see is that ESG has typically been a conversation around equities. And, but now more and more we start to see it on the, across multiple asset classes, particularly fixed income looks very interesting. And also, uh, private assets. So private equity companies,

uh, you know, in the absence of public company data and specifically emerging markets where there's massive, uh, sparsity of data, AI has becoming increasingly important to uncover these hidden risks, um, where there's no data available from traditional sources, and alternative data becomes the only source, uh, so infrastructure investments as well. So across all these spectrum of asset classes in these ESG conversation is expanding and becoming more relevant. So, um, I would say, uh, the time is now to, to embrace, um, you know, new technologies and, and, and, you know, get ahead of the curve and across, you know, company level sovereign and all different asset classes. I think we have time For one more question Quickly.

Is there a case study that you shared in the public domain, or you can't mention, um, where client was digesting property, say, because they anticipated coastal flooding, meet Climate Central. See, what percentage of us will be underwater in 2050, where, where the hedge funds or asset managers have put pressure or internally and have actually done that divestiture plan divestiture in climate migration? Um, is that happening or are people just talking about it? No, I think, I, I think it's happening and I think it happens when it hits your bottom line, right? Which is your own home. Our own home is the largest asset that majority of us will have. So I can tell you an anecdote.

I was in Martha's Vineyard over the summer and everybody's talking about climate change, and I'm, we're at some party, uh, one of the CEOs of a big bank, and it's like, how do I make money on this? It was like, well, maybe short where your home is, it's right on the water. And he's like, yeah, we already moved the home up like 80 years ago because it got flooded, right? So you can establish all kinds of products, asset classes. I mean, I always tell people a really good place to invest is upstate New York. It's not gonna get flooded. It has a lot of water.

There are companies who are investing just around water. You know, where's the largest source of clean water in the world? It's probably the Great Lakes. So in some of those cities, states are monetizing that, you know, when is it gonna play out? We have that data available, but people will have to come in, put a portfolio, and then they can go buy the metrics, which is also, I think one of the questions they asked is, it's becoming so easy with SaaS now that if somebody goes in, we'll probably release a product in Q one, which will allow people to go in query whatever company they have, pay us using Google, you know, and get the data. Mm-Hmm. Yeah, but we can, we can send you something if you, if you reach Out. I, I just like to add there it is a very interesting case of a hedge fund. It's called Caps Capital from Bob Litman. Uh,

they're profit tremendously from this concept of stranded assets. And, you know, going short, this is a long, short portfolio. A lot of that may not be public domain, but you can probably find indirect hints from the research published. And that's one of the most interesting applied cases to generate Alpha on a multi-billion portfolio real life. And we thank you, uh, Manish, uh, Rachel and Richard, and, uh, hope you guys enjoy this session. Take care. Thank.

2023-11-28 03:08

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