Episode Summary
In the latest episode of Signals, Nick Mazing sits down with Chris Semenuk, Portfolio Manager at Tema Global Royalties ETF (ROYA). Together, they unravel the multifaceted world of royalties. While many associate royalties primarily with music, they extend far beyond, touching areas like pharmaceuticals and natural resources such as oil and gold.
Chris delves into the unique appeal of royalty companies. Shareholders not only gain access to distinct assets, be it a high-potential oil property or a promising oncology drug, but also benefit from the expertise of seasoned management teams. These companies offer a blend of unique asset exposure and experienced leadership.
Rounding off the discussion, Chris introduces the Tema Global Royalties ETF. This fund aims to provide investors with diversified exposure to the best royalties across various sectors. It’s a deep dive into the potential and intricacies of an often-overlooked asset class.
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Guest-at-a-Glance
💡 Name: Chris Semenuk
💡What they do: Portfolio Manager
💡Company: Tema Global Royalties ETF
💡Noteworthy: Worked as a global fund manager for 20 years at a large US pension fund, specializing in active stock picking.
💡 Where to find them: LinkedIn
Key Insights
The Broad Spectrum of Royalties
Royalties aren’t just about music. In the episode, Chris Semenuk and Nick Mazing explore the landscape of royalties, highlighting that they encompass a wide range of intellectual properties. From the familiar music royalties that artists like Taylor Swift earn from streaming platforms to more complex areas like pharmaceuticals and natural resources such as oil and gold. These royalties can be paid to individuals, companies, or even governments. The discussion underscores the multifaceted nature of royalties and their significance in various industries.
Unique Appeal of Royalty Companies
Chris Semenuk delves deep into what makes royalty companies stand out. Shareholders in these companies gain a unique exposure to assets, whether it’s a promising drug in the oncology sector or a high-potential oil property in the Permian Basin. Beyond the assets, there’s the added advantage of experienced management. Many executives in royalty companies come from industries related to the assets they oversee, bringing a wealth of expertise. This combination of unique assets and seasoned leadership makes royalty companies an attractive proposition for investors.
Tema Global Royalties ETF: Diversified Royalty Exposure
Chris introduces the Tema Global Royalties ETF, aiming to offer investors a diversified exposure to top-tier royalties across various sectors. The fund’s objective is to give shareholders access to the best royalties available, spanning different asset categories. Currently, 70% of the fund is invested in commodities like precious metals, base metals, and oil and gas. However, the ETF also ventures into non-commodity sectors, such as the pharmaceutical industry. This diversified approach ensures that investors get a broad spectrum of the royalty landscape, maximizing potential returns while spreading risk.
Episode Highlights
The Basics of Royalties
Timestamp: [00:02:00]
Chris Semenuk breaks down the fundamental concept of royalties, explaining them as an alternative source of financing. Companies, especially in the commodities sector, can approach royalty companies for financing in exchange for a percentage of their revenue or even a portion of the actual product they produce. This arrangement offers a predictable income stream for investors.
“So, when companies need financing, they have essentially 3 main opportunities. You can either use the equity market. You can go to a bank and get a loan […] A miner can go to a royalty company and a royalty company will provide cash in exchange for a right to collect a certain percentage of the miner’s, or in some cases, collect actually a percentage of the metal that comes out of the mine or comes out of the ground.”
Shielding from Cost Overruns
Timestamp: [00:04:00]
Chris highlights the benefits of investing in royalty companies, emphasizing how they shield investors from the typical cost overruns associated with industries like mining. He discusses the challenges mining companies face, such as operational and capital expenditure overruns, and how royalty investors are protected from these risks.
“So as a royalty holder, you are shielded from not only CapEx overruns, OpEx overruns, but you’re also shielded from dilution and therefore you end up isolating that predictable income, which you get from the top line of the royalty or from, as I said, metal that comes out of the ground.”
Royalty as a Financing Source
Timestamp: [00:11:00]
The conversation shifts to the role of royalties in the current low-interest-rate environment. Chris touches upon the attractiveness of royalties as a financing source, especially given the prolonged period of low interest rates. This context provides a backdrop for understanding the growing appeal of royalty companies.
“And when we spoke before the recording, you made a very interesting point regarding royalty as a financing source. So for a long time, you know, 10 plus years, we’ve been in this very low interest rate environment.”
Tema’s Unique Approach and Background
Timestamp: [00:02:00]
Chris delves into the background of Tema and its team. He shares his experience as a global fund manager and emphasizes the active stock-picking background of the team at Tema. The team’s mission is to offer investors access to unique themes propelled by long-term growth drivers.
“So, I worked before Tema as a global fund manager for 20 years for a large US pension fund, as an active stock picker […] we all come from the active, stock-picking background, not only on the long only side, but we have team members that also work on the long short side.”
Full Transcript
Michael Maloof: [00:00:00] But since it went live in August of 2023, we have even been impressed internally by its accuracy.
For example, Wall Street bet that a large national wholesaler would Report positive sales growth in the second quarter of 2023. Not only did earnest AI correctly predict a contradictory negative year on year sales print, but came within one one hundredths of the actual result.
Nick Mazing: Hello and welcome. You’re listening to Signals by AlphaSense and I’m your host, Nick Mazing. Today we have a returning guest, Michael Maloof from Ernest Analytics, a leading alternative data company. When we spoke very early this year, we covered the corporate use case for alternative data, but a lot has happened in the world since then with AI.
So this episode will focus specifically on AI. IA applications in alternative data. And Ernest just released a new product. We’ll have the links in the show notes. So [00:01:00] Michael, welcome back. And can you tell us a bit more about you and about Ernest?
Michael Maloof: Yeah, thanks for having me, Nick. It’s great to be back on the show. I’m the head of marketing at Earnest Analytics. At Earnest, we go find data sets that companies generate as part of their business. We call that data exhaust and turn them into highly predictive measures of the consumer economy. That includes tens of millions of anonymous U.
- consumers, actual credit card spending, itemized consumer package, good sales, online product pricing. foot traffic using mobile locations and healthcare treatment through insurance claims data. I wasn’t, in the data business originally. I actually started my career as an equity analyst at Goldman Sachs covering tech and telecom.
That’s where I really gained an appreciation of the role that data plays in decision making. You hear the phrase garbage in garbage out. I think I said that on our last episode too, all the time, but it really was difficult in those days to discern [00:02:00] accurate signals from the very noisy data that we had.
And what originally drew me to Ernest was the founder, Kevin Carson, and his approach to data. He essentially believed that the noisy data that investors were using back in the early 20 teens would be more predictive and hence more commercial. If it were paired with analysts who understood the business models of the companies that the data covered.
So almost seven years ago, I joined Ernest when it was just about 20 folks in a pretty cramped office in the Flatiron. And it’s been a great run. data is an interesting space because very few people at the time and still today had all the skills necessary off the bat to succeed. I had the business understanding, but not the understanding of how our products were made or how we actually read that data.
So early on I learned how to pull reports in SQL. I managed AWS. I built Tableau books. Now I’m our chief storyteller. I go into [00:03:00] the data daily to look for interesting stories that apply to investors, companies, and consultants. What I love most about the role and the space are the interesting thought leaders I get to meet, like you, Nick.
In an average week, I may sit down with a professor of marketing, a portfolio manager, a blogger, a CMO., I’m based here in New York City and I love that confluence of tech, finance, and corporate leadership here.
Nick Mazing: So when we spoke earlier this year, we covered the, what I would call emerging. corporate use case for alternative data, things like which promotions are working real time and things like through market share and so on. But the market for alternative data, like you said, originally, the market is served by alternative that originally is investors specifically the most sophisticated,, you know, long, short hedge funds.
So can you walk us through the types of alternative data that is used by investors?
Michael Maloof: Yeah, that’s right. We use the term gold standard at Earnest to apply to our consumer [00:04:00] dataset, specifically our two separate transaction datasets, because that’s really what they are. No dataset on the market has proven more predictive for a wider range of names than anonymized credit card transaction data.
We originally built our solution suite around consumer facing brands, not B2B or professional services, because that’s where the best data was. Now, to your point, of course, that data has missing pieces. So we talk about investors that includes both private and public equity investors, by the way, building a data mosaic after getting a baseline of company performance, using transaction data.
Maybe the investor also uses foot traffic from mobile location to see how an individual store is faring or product level sales by CPG brand to see if consumers are trading down to cheaper products. Maybe the investor also uses online product pricing to see if there is margin pressure on the business or they’re just [00:05:00] discounting heavily.
Investors build a case for an investment over time through this data mosaic, which they can easily do with. Lots of information, but not easily with publicly available information, or even data obtained while doing the due diligence from the companies themselves. So whether they’re building a position in a public stock or taking a company private, investors are ultimately looking for the same thing.
That’s confidence. They want to eliminate surprises in their investment process. With earnest, they have more data set options than ever to build that data mosaic, all the ones I just mentioned, and ultimately build confidence in their investment decision. We’re up to about nine highly predictive data sets so far in counting
Nick Mazing: Let’s talk about AI. I mean, here at Alpha Sense, we already released our first generative AI product earlier this year, earning smart summaries on transcripts, and I don’t think it’s an over exaggeration to say that [00:06:00] everyone quote unquote knows that AI applications are going to be very widely adopted, so can you tell us That was more about how you’re now using AI in the context of alternative data at earnest for things like revenue forecasts.
Michael Maloof: short of all the tech buzzwords we’ve lived through in the last decade. You and I, I think AI has the one with the most staying power, given its potential to scale existing processes and unlock. The value hidden within our massive data sets, Ernest has been using some version of machine learning, the basis for AI for years to ensure the quality of our data.
There’s would simply not have been a scalable way for analysts to comb through the billions of rows that we get and create reliable signals. Machine learning helped us quiet the noise, if you will. Then in August,we launched Ernest AI, which built on our machine learning foundation. Ernest AI represents the next [00:07:00] step in using generative artificial intelligence to actually derive signals from the data.
The generative tool can predict reported metrics for hundreds of U. S. companies, nearly doubling our coverage in the single day that it was launched. Previously, it would have taken analysts Days, if not weeks, to deliver predictive modeling for a ticker. Now analysts can spend more time covering companies and less time launching them.
So it was a huge development for us.
Nick Mazing: So how did you build a model? Like, how does it work? How is it used?
Michael Maloof: That’s a great question. So I can’t get into too much detail. It is proprietary, but at a high level, reported metric predictions from Ernest AI does two things. First, it improves the historic data using publicly available sources of truth to make sure that the data’s sample is as accurate a reflection of reality as possible.
Second, it looks at historical relationships across the company’s reported metrics. So think reported US [00:08:00] sales or same store sales in our data to predict how the future quarters earnings will print. We can now predict earnings with a lot greater accuracy and much earlier in the quarter thanks to earnest AI.
So that allows investors to act faster and with more confidence.
Nick Mazing: Do you have any good examples of your forecast for whether it’s for, you know, metric, like revenue or a KPI has been more accurate than the Wall Street estimates for that specific
Michael Maloof: Yeah, quite a few examples. Actually, Ernest AI’s reported metrics predictions were trained on years of, examples on hundreds of tickers. So we were fairly confident in its ability to generate accurate results at launch. But since it went live in August of 2023, we have even been impressed internally by its accuracy.
For example, Wall Street bet that a large national wholesaler would Report positive sales growth in the second quarter of 2023. Not only did earnest AI correctly predict a contradictory negative year [00:09:00] on year sales print, but came within one one hundredths of the actual result. If an investor had been using the Earnest AI read, they would have seen the subsequent negative stock reaction coming.
and there are more AI, not just correctly predicting. The directionality of an earning surprise, but also generating predictions that came very close to the actual results. The basic reason is that Earnest AI is able to build that data mosaic for these stocks faster and with more accuracy than wall street.
Nick Mazing: KPI. We’re obviously very early in AI adoption. And I mean, everybody has started using some applications every day, whether it’s. consumer tools or in our cases, the specialized tools, but certainly there is a long ways to go. So how do you see AI playing a role, in alternative data we’ve earnest in the future?
Michael Maloof: Right. So Earnest AI launched with just a single feature reported metric predictions based on our VLA transaction data. [00:10:00] it’s one of our two transaction data sets. Ernest AI is already available in our direct feed. in delivered tables. Next, it’s coming to Dash, which is our proprietary online platform.
But this is really just the beginning. In the future, you can expect Earnest. ai to power predictive metrics across data sets to create composite earnings. This tool will also eventually underpin other metrics for non public names. So a lot of applications, if you take a step back, it makes sense for the whole data industry to embrace AI as part of our process, you know, data platforms like Earnest, we’re onboarding billions of rows of data a day, there’s simply no way we can keep up as an industry using analysts alone to derive signals from exponentially growing data sets.
Ultimately, those signals are what our clients are paying for. So Ernest AI. Is, moving us into a new chapter as a company where our product is no longer just high quality data on the consumer [00:11:00] economy that our clients interpret on their own, but rather our predictions themselves. I invite folks to read the press release and sign up for earnest dash tool to see for themselves and take a tour.
Nick Mazing: Yeah, I’ve, I’ve, I played with the dash when we first spoke and I should go try it again. Michael, thank you for joining us today.
Michael Maloof: Yeah. Thanks so much for having me back, Nick. A pleasure as always.
Nick Mazing: Today we spoke with Michael Maloof from Ernest Analytics about the intersection of AI and alternative data. We’re going to have all the links in the show notes. This was another episode of Signals by AlphaSense. My name is Nick Mazing. Thank you for watching or listening.
