A conversation on materiality
Tuesday, June 11, 2019
Tuesday, June 11, 2019
A deep dive of sustainability topics with Ethic's lead data scientist
A conversation on materiality
Tuesdays With Travis is a collection of monthly interviews with our data science lead, Travis Korte, that explores the complexities of expressing values through data.

The ability to prioritize information has always been an essential skill for investors. But today, with the growth of data on companies’ environmental, social, and governance practices, investors now must weigh information that was historically considered non-financial, such as environmental impact or gender diversity.

Understanding how much those factors matter is difficult to say the least, and can vary across time, sector, and market. A discussion of materiality is therefore an important consideration for every market participant.

In this Tuesdays With Travis, we break down what it means for information to be material, how materiality is assessed, and what that information can tell you—or can’t.  

Comments have been edited and condensed for clarity.

Let’s start with a definition. What does it mean for information to be “material?”

Material just means, ‘does it matter?’

Most often we’re thinking of it from a financial perspective—is this data point reasonably likely to have a bearing on financial factors like performance? It’s a helpful way of narrowing down all the zillions of data points out there and finding the ones that might actually make a difference.

But financial materiality definitely isn’t the end-all for deciding what data is useful. We also want to look at non-financial impacts. For example, is this data point likely to have a bearing on, say, carbon emissions? Or waste volumes? Or your company’s wage gap?

So our models will normally include some data that’s assessed for financial materiality and some that’s not, and that balance depends a lot on client preferences.

How do we determine what matters—what’s material—and what isn’t?

It’s tougher than you might think.

The traditional way of thinking about it is in pretty strict technical terms: there’s an accounting standard that essentially says information is material if omitting it would make a reasonable person change their decision—about investing in a company, for example. The world is complex and we don’t have perfect information about exactly what information is going to have a financial impact, but what do we reasonably think might matter? And the way they figure that out is, a group of experts get together and go through industry by industry and say “we think this information is material to companies in this industry.” Water usage is material to food and beverage companies but not software companies, that sort of thing.

Now, you might notice that this doesn’t quite line up with what I said earlier, how materiality is just about asking “does this data matter?” Because, as you can see, the question these experts are asking is really more like: “is it reasonable to assume that this data matters?” The assumption is that it’s too hard to tell exactly what information is going to make a financial difference for some company over some period of time, but you can come up with some pretty good rules of thumb.

These kinds of frameworks are great as first approximations. I don’t want to knock this approach to materiality, but what I’d love to see in the future is a more granular interpretation of materiality: let’s see if we can estimate what data will matter to what company at what time, and how much. That feels like the natural next step, especially now that we have so much data at our disposal to do these kinds of analyses.

You just mentioned materiality changing over time. Can you elaborate on that?

Sure, this is something we think about a lot. I find that people often want to think that certain bad behaviors will be equally important to company performance over time and that there’s some invariant truth to what matters financially. But in practice, we’re subject to the market and markets are made out of people and people’s preferences change over time, so what is material can change as culture and norms change. Think of the way people have become increasingly aware of racial justice and gender equity issues, for example, over the past couple of decades. Controversies in these areas are probably more financially material for companies now than they were in the past.

Given that materiality can change across time and sector, is it hard to pin down? Have you ever thought of something as material, only to find out it isn’t?

Not all data providers agree on what’s material. That can be confusing for clients, and it’s our job to understand the range of definitions and get a sense of where each is most reliable. We want to create a balanced, composite view so that we can really use materiality as an aid for decision-making. We want to be able to select the variables that are going to be most impactful, and certain data points matter materially more than others.  

In an earlier version of our sustainability model, we were looking at a variable that was tallying fees paid to auditors. It’s a corporate governance data point that’s supposed to be a kind of proxy for accounting fraud. But we did a bunch of research and that was one of the factors that we couldn’t find materiality in, meaning that companies with high and low values were indistinguishable from a sustainability perspective. Because of that, we removed it from consideration. The goal is to use materiality as a guide for what issues to take into account, because otherwise you’re rewarding or punishing companies for things that are irrelevant.

If a data point doesn’t qualify as material, does that mean it doesn’t matter?

No way, there’s a lot of reasons it might still matter. It might just be that we’re not measuring it very well, or that we don’t have enough data, or that it’s only material under certain conditions that we haven’t quite figured out. I’m hesitant to refer to any data as “not material,” in fact—it might just be “not material yet.” We’re trying to identify these signals from an overwhelming amount of noise, so there are going to be insights that remain buried for a long time.

And even if we feel confident that some or other bit of information is probably immaterial, there are still good reasons you might want to include it. This comes up a lot in sustainability issues with a strong values component: if you have zero tolerance for any company tied to tobacco, you may exclude companies that have a single dollar of tobacco revenue. It may not be financially material—or central to the company’s business model—but it still might be important to you.

Key Takeaways

  • Materiality is a way to cut through a large universe of information to determine what matters for an investment. For investors, typically “what matters” is considered in a financial context, but there are ways to evaluate traditionally non-financial impacts as well, such as carbon emissions.
  • Given that it is subject to changing market beliefs, materiality can vary over time, sector, and even across data providers. Ethic constantly assesses these discrepancies to determine which data is most relevant and personalized to the issues clients care about.
  • Just because a data point doesn’t show up as material doesn’t mean it doesn’t matter. It may matter to an investor’s personal tolerance or could be material in ways we have yet to discover.

Sources and footnotes
Contributors

Travis Korte is the Data Science Lead at Ethic. Previously, Travis organized civic-minded technologists at Hack for LA and advised a wide range of clients on data science, data policy, and quantitative methods. You can follow him on Twitter at @traviskorte.

Jay Lipman, a co-founder of Ethic, is driven by the need to address climate and environmental risks with the resources to which we each have unique access. He has been ranked among the Forbes 30 Under 30: Social Entrepreneurs.

Melissa Mittelman creates content at Ethic and is an alumna of Bloomberg News, where she covered private equity & deals. Melissa previously worked at Deutsche Bank, providing institutional, cross-asset sales coverage for ultra-high-net-worth investors.