Speaker 0:
Joining me now on this exclusive interview where we will be discussing resilience as an analytical filter for E S G data. I'm joined by Ashby Monk, head of research at a welcome Ashby.

Speaker 1:
Hi, good to be here.

Speaker 0:
So Ashby, because of the increased focus globally on sustainable investing E S G intermediaries and data providers have proliferated globally. How do capital providers and allocators build faith in E S G data providers when most of them fail to provide a comprehensive offering of E S G implementation data?

Speaker 1:
Good question. Tough question. There's a lot in there. So we're talking about all of the different intermediaries that are basically collecting the E S G data, environment, social and governance data and then packaging it up and delivering it off to the capital allocators. So in our in our research, we suggest that is problematic. Um Ultimately, the the goal of E S G is to change the flow of capital in capitalism.

Speaker 1:
So to help the biggest investors on earth understand something about their portfolio that they didn't otherwise know some environmental risk, some social risks, some governance risk. That is truly like a an epiphany moment. A light bulb going off and almost obligate them in that realization to make a change.

Speaker 1:
Um Unfortunately, the, the critique we have of the current um E S G industrial complex for lack of a better word um is that it, this analysis takes place um away from the capital. Um allocators, the decision makers too often. Uh The the data is collected in a format that is often unknown. It's a bit of a black box, what the data points going into this analysis.

Speaker 1:
Um Bar and uh and then the analysis itself is difficult to kind of unravel. And so when you get a score, you're told that you have a 77 out of 100 or you have an A out of, you know, an ABC D rating. It's difficult for the investors themselves to actually make sense of that. So

Speaker 1:
what we want is ultimately the ultimate investor, we, we want the investors that actually control the capital to begin to um become more fluent in the underlying data, the signals so that they can actually have those epiphanies

Speaker 1:
and they can change that flow of capital, change the cost of capital for those um good actors make it worth their while and change the cost of capital for those bad actors um in the form of, you know, making it more difficult to secure equity capital or making their loans more expensive. Um That's really the, the goal in all this is to make the capital markets more efficient as it pertains to those longer horizon risks,

Speaker 1:
sorry for the long answer. But there was a lot in that question. No, that's

Speaker 0:
perfectly fine. So Ashby, you've highlighted capital market efficiency as a supporting argument for E S G dis dis intermediation. What further supports your view?

Speaker 1:
I mean, ultimately, the, the fact that we see E S G um E S G scores are very different from, you know, rating agency to rating agency. And I think this is where a little bit of this current backlash,

Speaker 1:
you know, like I think when we first agreed to do something on E S G, it was ages ago, I don't even think we appreciated how um how wild this E S G backlash was gonna be in the U si mean, just I think it was this week Florida signed a, you know, a ban on including E S G in the in the pension fund. So, so E S G is seeing this backlash and I think ultimately, it is due to the fact that

Speaker 1:
um the ratings that are being developed are often very different from one agency to the next. Um the scores which are aggregated, you know, we're gonna pull in the E S N G into a single rating in certain certain context. Um That's very difficult for investors to use. Remember our last comment, which is we want investors to use E S G to change something about their portfolio.

Speaker 1:
If I give you a very generic rating, it's hard to kind of unravel that rating and build it into my fundamental analysis. Ok? We're not, we're not taking, for example, a credit rating and using it to make an equity investment decision.

Speaker 1:
Um, but that's kind of what we're doing with E S G ratings or we're, we're, we're making the implication that we're gonna do that. Um, we've borrowed that credit rating logic and brought it into the, the, the land of E S G. But ultimately, credit ratings are different. Credit ratings are about assigning a probability of default on a credit instrument. Um I don't know what the probability that we're signaling are, the probabilities are for E S G ratings. And so ultimately, thi this is why we think

Speaker 1:
some disintermediation at the very least in the analysis phase. Um I think it's gonna be very hard to remove the, the intermediaries that collect the data because a lot of this data is niche data. It's around the world. It requires, you know,

Speaker 1:
poring over documents that's gonna be hard for your standard capital allocator to go and figure out. But when it comes to analyzing those, those facts that are being collected on the ground, I think we need to find better ways, better tools that allow you to maintain the integrity of those facts and not smooth them out across these ratings.

Speaker 0:
So, Ashby, you've mentioned these cap capital allocators often they outsource their E S G data analysis to third party data providers. Why don't they just then insource or cultivate an in-house set of data metrics to analyze?

Speaker 1:
It's a great question. I think there's a few organizational um factors at play here. First off is the governance of these organizations for a very long time, we pushed these funds to consider only commercial things in their decision making. Remember that the sponsors of most capital allocators, most pension funds, sovereign funds are often governments.

Speaker 1:
And so we pushed those organizations to define their governance in a way that um avoided interference by government on issues that were important to government but were not aligned with the kind of financial and commercial objectives of the plan. So it's important to realize that for like 50 years, we said, look, you have to maximize risk adjusted return

Speaker 1:
so that governance became a culture and the culture was we can't look at society's problems. We have to look at our challenges to meet our rate of return target. OK. With that, we built a culture of commercial and financial excellence.

Speaker 1:
Um And when we were bringing in, hey, can you invest in housing for students or can you invest in infrastructure? Often, the chief investment officers and chief executive officers had to kind of block and tackle as ways to avoid being dragged into bridges to nowhere type investments, economically targeted investing. Unfortunately, the world has really shifted where we've started to realize that, you know, some of these extra financial signals are material to performance.

Speaker 1:
And so we're having to innovate and change the way these organizations actually make their decisions.

Speaker 1:
So inside these organizations are many people whose gut reaction to many of the topics um raised by E S G, their gut reaction would be, look, my job is just to pay pensions. Let me get on with the job of paying pensions. If you want to go solve society's problems, you do it through different means. The problem I'm solving is retirement security. OK? But now we're realizing that retirement security and climate change are connected.

Speaker 1:
And so in order to reintroduce this kind of climate signal into the investment decision making logic, we need new capabilities, OK? We need to bring the E S G inside the organization. We need to sit these people next to the investment decision makers, the private equity managers,

Speaker 1:
um the chief investment officers and we need to demonstrate to those folks exactly how E S G impacts um dollars and cents and financial return. It, it's fine if that's over a 10 year 20 year horizon, it just has to be demonstrable and the links have to be obvious. So all of that is to say it requires a lot of innovation,

Speaker 1:
it requires change in terms of culture in terms of the way we think of um constructing portfolios in terms of technology, in terms of internal team coherence and all of that innovation needs to take place inside organizations that we designed not to be innovative.

Speaker 1:
We told them right in the beginning, be conservative, be prudent. Don't do anything your peers wouldn't do. OK. And so with all of that, you know, the challenge is extra hard to get this kind of E S G signal really brought inside these organizations. That's not to say they don't have E S G teams. they do, many of them have set up these little teams, but too often they're kind of separate teams off on their own

Speaker 0:
Ashby. So what do you think is then perpetuating the avoidance of E S G analysis by typical investors such as these large capital allocators, assets owners and smaller investment firms.

Speaker 1:
There's a lot of data that's, you know, you, you just think about how much data is getting collected today as opposed to even 10 years ago. You know, like sitting here right now this watch is gonna say, oh your heart rate is raised is your own asset TV. You know, it it's gonna be, it's just fascinating to think about all the, all the data that's being collected.

Speaker 1:
And so this proliferation of data means that it is very difficult to understand which data sets have value, which data sets are material to my portfolio, to my goals. Because what we learn in this space is like different E S G signals actually are material to different events, not just different companies but different types of events in certain events, it will help you in certain events, it doesn't matter.

Speaker 1:
So understanding all of that and the proliferation um makes it incredibly difficult to keep up with it all. Um The second piece, I think that avoids the typical investor from really bringing it in is integration with the traditional models of risk management. So the traditional models of risk man management often think in terms of scenarios,

Speaker 1:
OK. We we've got this type of uh you know, let's call it a G F C shock that we need to run against our portfolio. Well, that's a scenario based assessment that tries to understand would we survive? Could we meet our capital calls? Could we meet our liabilities and this type of a shock? Unfortunately, the E S G tool kits are often not designed as scenarios, they're designed as issue based kind of um signals, you know, this

Speaker 1:
issue of environmental footprint or diversity on the board, they are kind of presented as points in time. And so part of what needs to happen in this space is we need to start to design these scenarios where we talk about how the E S G issues affect portfolios over time because that's when it becomes really useful to the investment decision makers

Speaker 0:
Ashby now to close up the session. What is the resilience approach to E S G analysis and how it will become more scalable efficient and insightful for investors.

Speaker 1:
Yeah, resilience is in effect our attempt to start to integrate a lot of the E S G world into more um more mainstream processes of decision making. So first off Dan Ruck and I wrote a paper called the resilience Manifesto. Then we followed it up with a paper called resilience as an analytical filter

Speaker 1:
for E S G data. And more recently, we wrote a paper on submergence which is about explaining how portfolios become more resilient. So those three papers come together to try to describe portfolio resilience as kind of a coherent theory of investment management. Ultimately, the simple way of saying it is we are finding that resilience is a positive um driver of value in portfolios.

Speaker 1:
Resilience as a concept is very common in engineering and biology. And even human psychology, resilience is about the ability to recover from a show.

Speaker 1:
So you get hit with a shock, you get hit with a drawdown, you get hit with an earthquake and you're shaking, but ultimately you recover and in fact, sometimes you recover stronger than you did ahead of times

Speaker 1:
in the investment business. We don't have a whole lot of um theoretical frameworks for understanding recoveries. We do a lot of work on drawdowns and variants and volatility and value at risk. So we really do think deeply about the shocks, but we don't have as much math to think about the recoveries. And so the work that we're doing is about trying to say, look, the recoveries are very important.

Speaker 1:
You know, if you're a surfer, I often say if you're a surfer and you get, you wipe out and you go underwater, it's actually not how deep you go underwater. It's how long it takes you to get back to the surface. Ok. And it's that concept of getting back to the surface that we want people to understand

Speaker 1:
and it's that specific recovery and pathway back to the surface that we believe E S G is material.

Speaker 1:
So it's obviously helping you avoid a deep shock. That's there's a lot of work already done as academic researchers on the ability of E S G to minimize the drawdowns to absorb shocks because you're managing risk. There's more work that we are leading, that describes how sustainability, how an in a smaller environmental footprint, for example, might help a company recover faster from some

Speaker 1:
hurricane or how a a workforce that is diverse and satisfied might stick with a company through difficult times and help it recover faster from a shock. These are the types of components of portfolio resilience that we believe will become very important and allow us to begin to integrate E S G more thoughtfully into um traditional portfolio um tools

Speaker 0:
Ashley. Thank you very much for sharing your insights into the resilience as an analytical filter for E S G data. We appreciate your time.

Speaker 1:
My pleasure. Thanks