FactSet Research Systems Inc. (FDS) Earnings Call Transcript & Summary

Hello. Thank you for joining today's webcast on Performing Deeper Analysis with FactSet's Thematic Intelligence & Investment Solutions. My name is Philipp Zerhusen, and I'm your host and moderator today. Before introducing my co-presenters and panelists, a few housekeeping notes for this event. So you may submit text questions in the Q&A box at any time, but they will be addressed at the end of the presentation. You will also receive an e-mail with a link to a recording of the event. And last but not least, if you'd like to schedule a follow-up meeting about anything discussed today, please fill in the post-event survey. And obviously, also from a quality perspective, we'd highly appreciate to learn about how you like the event and what we could do better going forward. With that brief intro on housekeeping, let me introduce my guests on the call today. In an effort to utilize the time for the greatest value for you, I'll keep it rather brief. Our profiles are -- and bios are available on the landing page for this event, and we are happy to share them after the event if you're interested. Let me begin with welcoming and introducing Elysabeth Alfano. Elysabeth is a plant-based industry expert and sought-after speaker and consultant with more than 30 years' experience in the field. With impact investing at her heart, it seems only natural that as of last year, she is the CEO of VegTech Invest, a plant-based innovation and climate ETF. She will tell you all about it in a little while herself, so I won't steal her thunder. She co-founded VegTech together with our second guest, Dr. Sasha Goodman. Sasha is an experienced portfolio manager, statistical software specialist and quant analyst. He's the President of VegTech Invest and runs both the EATV ETF as well the EATVI index. As such, Sasha is a regular user of our Thematic Intelligence & Investment Solution and will tell you about it, about how it helps him being more efficient and effective. We are very proud and happy to have partnered with VegTech for a great use case. And I believe hearing from them directly as thematic impact investors is much more interesting than a 45 [ effected ] product demo. So bear with me on that one. But last but not least, I would like to welcome Alex Stumpfegger. Alex is the Chief Sales Officer and representative from our partner firm CID, with whom we developed Thematic Intelligence & Investments. Without further ado, I would like to hand it over to Alex to give you a brief overview of Thematic Intelligence & Investments before we go into our panel discussion with Elysabeth and Sasha. Alex, over to you.

Great. Thank you, Philipp. And thank you, Elysabeth and Sasha, for joining us today. So what is Thematic Intelligence & Investments about? Basically, it's built to support portfolio managers, fund managers, who run thematic investment strategies, like those who invest in like digital healthcare, the Metaverse, we'll hear more about VegTech obviously, in a minute. Those who want to break free of like industry, sector classification. Those who want to leverage their deep expertise and experience and knowledge about a theme to find the true investment opportunities. And there's something that's quite important that we call the insights gap because the sort of "traditional" fundamental data and quantitative data sources typically do not provide the required level of granularity. And that's why portfolio managers, research analysts typically invest a significant amount of time and manual effort to do research to find those true investment opportunities. And that research means the information is out there, it's available. And that's what we call qualitative data that we can source from sources like news and filings and corporate websites, et cetera. And that's exactly what we apply thematic intelligence to because helping to automate this sort of research or at least to make it more efficient literally screams for artificial intelligence and big data analytics. And again, that's what thematic intelligence is about. This does not only apply to thematic investing. This can also be applied to like risk analysis to assess what themes have -- what sort of impact on portfolio holdings, it has a like an ESG dimension, it can enrich quant models with additional factors. So it's really about leveraging qualitative data in combination with quantitative and fundamental data. Next slide, please. So what exactly does the thematic intelligence AI do here? So again, [ in spite ] of capturing big data insights from unstructured qualitative data sources like news, like filings, like earning call transcripts, like even research to make sure that we capture all this information automatically and on repeat. And it turns this data, it extracts sort of quantified theme exposures for equities from this qualitative data. So it makes the data usable. It makes it actionable in terms of directly adding insights to an investment process. And very important, this is not about pre-canned themes. This is also about enabling a business user, a portfolio manager, a research analyst to train the AI model themselves. So again, to leverage their expertise and experience, train it into the AI model and then have the AI system do all the data crunching at scale and turn this into a more efficient process. And that, again, that's what Thematic Intelligence & Investments is about. Let's see what this can look like. Next slide, please. So this is really a view on [ Themescape ], which is the business user interface that we offer. And it starts as simple as a web search to train a theme in the system. Like here, I chose another theme not to interfere with what Elysabeth and Sasha will tell us in a minute, so I chose plastic recycling, also sustainability heavy theme. And as you can see, I just keyed in like two keywords, plastic recycling. And at first, within milliseconds, I already get a list of companies potentially exposed to this theme. To get a very first idea and test my hypothesis in less than a minute, is it worth to spend more time on this. Then next slide, please. I am guided through a process where the system leverages excerpts from the news to provide me with descriptions of a theme. And I as a like business expert can give a thumbs up, thumbs down feedback, which is ultimately the training of the AI system. And this doesn't require 1,000 or 2,000 labels, as you might be used to from other machine learning exercises, we're happy with a minimum of 90. So this can take like 20 to 30 minutes to get a new AI model up and running. It's a massive reduction in time to market and a massive increase of efficiency, if you want to invest in themes. Next slide, please. Here's an example of what the results look like. This is a list of companies exposed to plastic recycling based on this qualitative analysis. And it's already filtered on public equities. So you can see on the top, like 639 companies, still a lot, we'll get to that in a minute. Next slide, please. The thing is leveraging this AI model, leveraging the qualitative data analysis and the output of companies exposed to the theme, it's not like a list of companies you've got from a web search, it's already fully mapped to all the other fundamental and quantitative data. So you can see the performance. Next slide, please. You can see all the revenue, the market cap, et cetera. You can see what sort of signals and events also the AI has extracted from this incoming flow of information, like here, that's a company from Finland, Neste, they do a lot in plastic recycling. You can see that they are now collaborating with others to -- like to build a stroller leveraging bioplastic and so on. So you can see what happens. Next slide, please. And most importantly, you can also immediately validate why this theme exposure is a real thing. And you can use this for theme validation, but you can also use this like for investor relations and communication purposes to keep telling relevant stories to investors and customers. Next slide, please. And it's so essential to see like how this interferes again with the standard industry sectors because if you would just look at industry sectors, no sector of those would tell us that Neste is actually a significant player in plastic recycling. Next slide, please. Just to show that, too. We also leverage the wealth of supply chain information FactSet has to be able to see who are the customers, where are the suppliers of those companies. So think about a thematic exposure analysis also across value chains to comprehensively understand the market and a theme from a business value chain as a whole. Next slide, please. So it's really about like focusing on narrowing this down to a list of -- a good list of companies. So this is a strategy builder, by the way, where you can apply a lot of additional factors and filter criteria to like in line with your strategic asset allocation, probably like public equities only or geographic filters, filters on industry sectors. Like here, we applied a filter like on market capitalization. Next slide, please. Most essentially, obviously, from this point of view, you could pull in the theme exposure, in this case, plastic recycling. You can add in additional signals and events like mergers, acquisitions, "punish" companies with like legal disputes or corruption events that we detect it from. So you can combine all those aspects, and it can interactively weight them in this way, like define your priority list of investment targets. Next slide, please. And just applying those additional filters got this list down to 151 plastic recycling exposed companies. So that's -- what I've learned is a good list to work with and then to like make the ultimate investment decisions. But that's what Thematic investing intelligence can look like in a daily use. And I will now pass it back to Philipp to see what VegTech has done with it and how they run their strategy.

Perfect. Thanks, Alex. So diving then right into what we dubbed in our little script then the sort of the panel discussion. And before we go, and I'll ask some of those questions that jump to mind. I obviously wanted to give the opportunity to Elysabeth to introduce her firm and her fund and her approach to us, which I believe is very relevant and interesting for the audience before then we dive deeper and deeper into sort of portfolio allocation, teams discovery and so on. So Elysabeth, it's -- the floor is yours. Thank you.

Thank you, Philipp, and thanks, Alex. It's been very rewarding to work with FactSet on the Thematic Intelligence & Investment tool. It's great to have cutting-edge partners. If we can go back to the last slide, I'll just say that I'm the CEO of VegTech Invest, and we put out the world's first and only plant-based innovation ETF. So how did we get here and why did we think that this would be a megatrend that we could capitalize on? Well, let's go to the next slide, and we will see that Sasha and myself both deep in the plant-based world, experts in the sector, venture investors in the sector. So we know all about the sector, but we're not alone here. When we look at the former CEO of -- former co-CEO of Whole Foods, Walter Robb, he says that the move to plant-based foods is the biggest single trend in history of food that he's seen in the 40 years he's been in grocery store work. He'd likens it to a megatrend akin to digitization. So wonderful to get his perspective. Let's get more of a financial perspective. We go to the next slide, we see that Bloomberg anticipates that plant-based foods will be 8% of the industry by 2030 and representing $162 billion. According to the Boston Consulting Group, it will be $290 billion by 2035. That would be 11% of the market. So Sasha and I aren't alone in our predictions that plant-based foods, alternative proteins, plant-based innovation are going to be a megatrend that continue to grow, and it's something that we can capitalize on with an ETF. But why would this be the case? Let's go to the next slide and find out. So it's not just that we think this. If we go to the next slide, we see that plant-based innovation and alternative protein companies are solving some of the world's most pressing issues. And I think after you see these slides, you'll see it's undeniable that we will be shifting our global food supply system. It's undeniable that it will be a complete disruption and that the technologies that we're investing in up and down the supply chain will be the technologies that feed people from here on out. So let's see some of the reasons behind this. We go to the next slide, and we see that, yikes, Animal agriculture puts out 16.5% to 20% of the world's greenhouse gas emissions that comes from a variety of studies, but primarily the United Nations. A whopping 37% of U.S. methane comes from animal agriculture, methane being one of the most nefarious gases, having the most potency while it's here on the planet. 1/3 of the world's methane comes from animal agriculture. Next slide. It is not climate change and greenhouse gases alone. Although this is linked, animal agriculture is responsible for 41% of the world's tropical deforestation. I get the statistic from our world in data. Of course, those trees meant to pull carbon from the air. But when we cut them down, not only do they release carbon, but then we grow all these crops to feed people? They have fiber, they have protein. Why wouldn't we grow -- cut down trees to benefit us if we're not even going to get that carbon capture? We don't. We cut down trees to grow crops that have protein and fiber and when we water these crops and we give them land and we give this food to people. Now, we give them to animals, and also need more time, water, land, et cetera. You see the enormous inefficiency, which is what we'll talk about next. The enormous inefficiency that comes from animal agriculture. So when you have an inefficient system, first of all, the business world does not reward that. But second of all, then you have quantity issues. So according to the United Nations, we have a growing global population going from -- I think we just hit 8 billion actually, going to almost 10 billion by 2050. But we're not getting more land and we're not getting more water. So we have this enormous middle animal, middleman, taking up that cost of goods sold, by the way, so enormous budget item line. And you see if you go to the next slide, an example of just how inefficient our current food supply system is. If you go all the way down -- sorry, I can't zoom in folks, if you go all the way down to the fourth bar, agricultural land and you kind of get your glasses on and you squint, you can see that 77% of our agricultural land goes to grazing and growing crops for animals, 77%. If you look at the next slide, you see it gives us only 18% of our -- so sorry, I'm sorry, back one, I'm so sorry, my fault. If you look at the next line under agriculture, you'll see 77% of our land use gives us 18% of our calories. So that math doesn't work as you start to have more people on the planet. We'll go to the next slide now. As an easy comparison, just because it's there, life cycle analysis from beyond meat, but comparing plant-based innovation to a beef burger, you see using 99% less water, 93% less land, 90% fewer greenhouse gas emissions, 46% less energy. So it's undeniable that as our population grows, we're not going to be able to feed people. And an inefficient use of our resources as climate change even becomes more dramatic, like water, droughts become more important, we won't be able to sustain the system. Next slide, please. And then kind of the last one, you might imagine that switching over away from animal proteins is going to be healthier for you. Even a processed burger, which, hey, it's not a carrot, we all know it's processed, is going to be one step in the right direction towards better health. It's not going to have animal heme, it's not going to have Trimethylamine N-oxide, it's not going to have cholesterol, it's not going to have antibiotics. But it's also going to help us not only in reducing health care costs, but reducing health care risks for people and for corporations. We see the enormous risk tied to animal agriculture for pandemics according to the United Nations. The top 3 reasons for the next pandemic, not the pandemic we're in now, folks, yikes, the next pandemic are all related to eating meat. Top two reasons related to the intensification of animal farming. That's animals in small confinement living basically in their own feces, but [ that's ] now. So that's kind of how our food is produced now, so very inefficient and very risky. And this risk transfers over to balance sheets. And as we see the SEC looking at Scope 1, 2 and 3 emissions, we see carbon pricing coming, we see an educated consumer base that wants to know about that label, and they also want to know about that carbon footprint, you see that companies won't be able to just dump their external costs on to society. Their emissions, their deforestation, their pandemic risk, these things are going to be things they're going to have to pay for. So industry wants to shift as much as the consumer wants to shift. And ultimately, governments want to shift as well for food security reasons. Okay. Next slide, please. So ultimately, we -- what we've discussed, we have to create more food that is more nutritious in a shorter amount of time, using fewer resources, creating less damage. That is a very tall order. That's why our theme is so dynamic, and I'll say complex, and it even has subtleties. It's not like we're going after just semiconductors, we are going after a problem, and we're solving it with plant-based innovation. That's what we do with our ETF. We capitalize on that megatrend as well as create impact. Next slide, please. So to do this, we created EATV Invest, the ETF, the world's only plant-based innovation ETF, our last and final slide before I exit and hand it over to the next question is EATV is focused on the company up and down the supply chain around the world that are innovating to replace animal products for sustainable consumption. This can be plant-based innovation like we see it today with the burger, but really, we're focusing on those novel technologies that are going to, spoiler alert, allow people to eat meat forever and ever. Nobody thinks the world is going vegan. I don't think it, Sasha doesn't think it, no one thinks the world is going vegan because the world is not going vegan. We're not asking consumers to change. We're just changing how we make our food and we're capitalizing on that innovation here in this ETF. So I think I've done my job there. I'll hand it back over to Philipp.

Perfect. Thank you, Elysabeth, for giving us the, how shall I say, a 30,000-foot view and why and how you decided to basically build this ETF. So then I don't know, Sasha or Elysabeth, diving a little bit then into the challenge where you use our service. So what were the challenges you faced when pretty much a good year ago, you started this fund? How did you go about your investment strategy and then identifying, screening and picking the stocks and the individual securities for your fund and for your index?

Yes, I think I can start. Next slide, please. So the first challenge here was this is a global phenomenon. Plant-based movement is not just in the U.S., of course, it's in Canada, Europe, Asia, and we see a lot of traction in fact, in the U.K. and Germany. And so searching the world's markets is quite a challenge. There are tens of thousands of public companies in the world to go through, different languages on different websites. It's a massive undertaking. And so the challenge was dealing with the scope of this and being efficient and then having -- being as efficient with the first step of prescreening companies so that we could then do our in-depth research to match the companies up with all our screens to see which ones fit and didn't fit into our individual screens. So our experience and our networks helped to some extent, but this was a big undertaking. Next slide, please. So I'm not sure if Elysabeth wanted to talk about this, but basically, this is also complex in the sense that we're not just getting the end product consumer packaged goods that people expect. But we're going all the way up to supply chain to some producers that provide the inputs there. So AgTec makes more efficient products than farming. That is an input into the ingredient and flavor companies. And all these different companies are exposed to different kinds of risk and we wanted to diversify our fund. So it's not just a matter of looking in a GICS category and just picking one. We have to find companies up and down the supply chain in this ecosystem that do fit our theme. And so that was an additional challenge, the complexity of it.

So if I may chime in, Sasha, because that sort of fits sort of my next question on the list here is, so how did you go about -- define then once you had your long list or your vision what you want to build, how did you build your overarching theme? And then also those, how we will take it from the slide, the 4 sub themes? How did you do -- I mean when you started, you didn't have the tool, but you went, I would say, the traditional way of finding those or...

Yes. Well, we are -- we were private investors and had some exposure to this sector, and we knew that it wasn't just about food. It was also about other things like plant-based materials and plant-based inputs into precision fermentation, which is a very efficient way to produce various compounds in nature synthetically but without the animal with a more efficient way. And so we are aware of the kind of cutting-edge technologies in this sector, and it wasn't just about the plant-based burger. It was -- and so there was a lot of technological innovation going on here. And as an active fund, we want to adapt to that as time goes on because there's always some evolution of that.

Okay. Cool. Do we have more slides or...

Yes, the next slide, please. And so this is also just showing the breakdown that we -- of course, 37% of it is plant-based foods and beverages, but there's just -- it's much more diverse than that, and we seek different subsegments that are less correlated with plant-based foods so we can really diversify and protect ourselves from any swing in one sector, so it doesn't spread to the others as much. Next slide, please. And one of the key characteristics of our search was not just to find companies that have fit our theme but also really solid companies that had good revenue growth because we are seeing this emerging sector happen, and each company needs to find its market. And so here, we show how compared to the broad-based indices, we actually -- we have higher revenue growth. So in our selection, that means we have to be even more efficient by starting with a wider selection. We're all about the selection. We have to really be selective and start with as many as possible and then pick the cream of the crop, the vegan cream. And so we had higher revenue growth, higher EBITDA growth, but then we also are searching for value and the price, the books of our companies have been showing that these are discounted stocks as well.

Excellent. Thank you. So maybe switching gears. So like a couple of months ago, I think Elysabeth, you ran into our colleague, Jim [ Linder ], I believe at an investment conference.

Yes.

And then you partnered up, tested our tools. So how has that changed or made your life easier since then?

Yes. Well, so Sasha, really fund manager focusing his time on the selection process and we see how much time it has saved us because I don't know if it's possible to go to the next slide. Again, it's a repeat slide, but it just -- again, to show sort of the complexity of what we're doing, again, we're not looking at semiconductors. We're looking at solving a problem. So we're capitalizing on that megatrend while having that impact on society. So when we look at solving a problem, we don't just look for the plant-based burger, which is what everybody asks us, well, what could be possibly in there beyond meat? Oat meat? What's left? Well, all that B2B technology and all the technologies coming, so we look at Synbio and how you're going to have more precision fermentation, cultivated meat. Anyone who saw the news last week, you see that the FDA gave a no questions letter to upside food to go to the next step for their cultivated meat, which means growing meat from cells in a controlled environment. So again, no one's going vegan. They're going to have that meat that they want, but it's just going to be produced so much more efficiently. So we need to be nimble and take advantage of these companies that become IPO. We also need to be nimble to the fallout of what happens when animals leave the supply chain. Again, so inefficient are they. Will you have them falling out of material supply chains as well? And there's going to be a lot of innovation for new materials. So I don't know if I'm actually answering Philipp's question, let me get back to it exactly. So you see how complex and dynamic our theme is which is really solving a problem rather than a tiny sliver of a theme. So you're talking about a monumental search. And before we have a Thematic Intelligence & Investments tool, it was just ours and we're a new fund, I mean we're a year old. But spoiler alert, the markets are tricky right now. So we're not spending on lots of -- hiring new people to do research, et cetera. That's got to be me and Sasha. We don't have that kind of time. So that's why it's been so rewarding to work with cutting-edge partners like FactSet because it does save us an enormous amount of time. But maybe Sasha has more to add there.

The more -- it's all about efficiency because the more efficient we can be in the prescreening, the less time we have to spend on our own in-house research process. And so we have multiple screens. We have to make sure that eventually, we have to make sure that it's plant-based, no animal products, no -- it doesn't support animal agriculture. We have to look at other features like that. And so every -- any efficiency we can grab is useful. And so this tool was a bespoke tool. It was -- so yes, I was so grateful, I mean, that it was saving me time because it presented everything in a list, ranked by relevance. And I could then sort them by the features that I needed, so -- to manage the actual fund. And so like market cap, liquidity, those kinds of features are right there, easily sorted. And so it was really saving us time. And I guess, I'd just leave it at there. I mean, it was just very -- it was very -- it was a good way to tackle the complexity.

Yes, and I'll jump in there, again, if I may just quickly, just because I don't even know if people understand the full complexity of what we're doing. We're not just screening out, we're screening in. So not only do we want -- don't want anyone involved as what we consider a very old and outdated system. When you look in animal agriculture, it hasn't been improved upon really in thousands and thousands of years. Okay, you had some efficiencies with factory farming, but now they've caused so many more problems than the efficiencies. So it served us well. We all grew up on it. It served us well, but it's just not serving us in the future. So we're not only screening out. So like Sasha was saying, you can't be even involved in creating animal feed. That's supporting an old system. We also have to screen in, find those that are innovating and then you can't just have -- or at least for us, because we are financially minded, we don't just want a fund that has small dynamic upstarts. Of course, you want those value-focused funds, those large multinational corporations that are focused on using their expertise to shift the global food supply system like Ingredion and others that are committed along the B2B supply chain of using their expertise to capitalize on what they as well see as a megatrend. So just complexity upon complexity and almost too much really for two people if we didn't have this tool.

Cool. And if I maybe -- sublime, maybe Sasha, since you are the sort of the daily user of the system, from what Alex shared early on in that quick sort of demo, what kind of feature resonated the most? Or can you very bluntly kind of confirm what we, product-wise, envisaged to help, but then can you confirm it works? Or is there any particular feature tool that stands out? I mean, in terms of theme building, is it as easy as we made it to be or...

Yes. Well, the ease at which I could customize the AI screen was a key feature. And just to put it in context compared to the like a traditional kind of system, the traditional way I would use a terminal to search was through keyword searching a short description text or a certain GICS or BICS or categories like that, that were so constraining and then I had to go to the website and it was in a foreign language and things like that. And so the features I really enjoyed were the ease of screening and then the fact that it could read the languages across multiple countries, dozens of languages. And so that was a key feature. And then once we have the screen, there are just some -- there's such customizability of it and ease of sorting that I appreciated because we have to look at liquidity. So there was a great Italian vegan ice cream company that I couldn't include because -- and I found out very quickly through sorting by volume. So that kind of thing, the ease was just very efficient. So I think just -- it was just like a night and day difference.

Cool. Thank you. Maybe over to Alex. I mean, Alex, do you see this from other clients we have engaged? Is this sort of a similar experience or any particular takeaways that you took from our proof of concepts and engagements with other clients as the service is still relatively new, so we are also like in infancy stage here, but yes?

I really appreciate working with like so innovation-focused asset managers like here at VegTech and others too who -- what you said Elysabeth earlier, like you're a small firm, like you have limited resources in terms of like how many research analysts you could hire. But frankly, that applies to other firms too. That even applies to very large asset managers as well because you always have to deal with margin pressures, resource constraints. And that's exactly what AI is made for, gain efficiencies from more efficient data processing to overcome the sort of research analyst scalability limitations and that's what we can see across the board. And that's where I think we have -- we're able to offer a great advantage and help those outperform others who try to do everything manually, not leverage AI as they are supposed to today.

And also, if I could something...

Great, cool. And...

Well, so sorry, Philipp, if I could add something. There's the complexity of our thesis and how we try to have impact and capitalize on that. But there's also the world in which we live in right now, which is kind of a complex scenario itself. Thematic is a little bit under attack, ESG a little bit under attack. And something that we really pride ourselves on because it's so meaningful the work that we do, for Sasha and myself, there are thematic ETFs out there that are basically just tech funds with the cute thematic name. So your fund, everybody knows this, is only as good as the companies in it. So for us to save this time but save money, not having to hire other people so that we can really have as, if I can say, creative approach to having an ETF that is solving a problem is really kind of groundbreaking for us. Like one of the things we can do, you see that companies are starting to switch over and they used to have animal products and now they no longer do and they're innovating for the future. So we can kind of keep tabs on those companies that we think they're not there yet, but they're going to be. You see this very live dynamic tool that we have that keeps us at the top of our game. So its...

Just piggybacking on that real quick. There was a company that had pivoted away from where it was into plant-based -- bio-based plastics that we put in the fund that we -- I discovered through this tool that had incredible growth as well as profitability, that had a good earnings season, and it actually added Alpha to the fund which was amazing, it's one of the better performers. And then there was another one, too, from Austria that I realized it also is selling plant-based products at price points that are appropriate for the economic times we're in. So -- and then that had good earnings, too, and it's been one of the top performers since we added it so it has helped.

Thank you, Sasha. That preempted my question on if the tool helps you to find the all-important Alpha. Even though you are ethical investors and impact investing and maybe Alpha takes a slight backseat, but ultimately, we all want to generate superior returns with our assets. And if we can do well and do good with it, even better. But yes, so that would have been my question. I'm just conscious of time. Normally, we set it to 40 minutes. So let's extend it to 45. I also want to give one sort of the chance again to the audience to ask questions and at least address one or two or three. While maybe the audience thinks of submitting a question, if you have any, let me ask maybe one final question before we turn it over to Q&A. So to you guys, again, Sasha, is there anything you are missing what we can improve, and I was also wanting to ask Alex on sort of our road map, but maybe we can combine this and Alex can pick up on anything you missed or where you think certain screener tools or others would help you better?

Well, the beauty of this tool has been that I can create multiple screens within half an hour, an hour, even milliseconds sometimes. So right now, we have a screen that has been very helpful to cut across industries. But we can also create additional screens that deal with each of the fund portfolio kind of negative and positive screens. And it may be possible already, but it seems like it would be very easy just to kind of have multiple -- the relevance meter for the multiple screens available on one list. It seems like it's just a slam dunk that can happen. And then I could just see, oh, it passes that screen and that passes that screen, it passes that screen, all lined up. And right now, I just -- it's so -- I can do it. I just have to put the screens side-by-side. It's like -- but it would be a convenience to have it in one place.

Absolutely. That's a very good point. Thank you, Sasha because that's in fact something we're working on to provide a sort of cockpit or a dashboard that would allow you to analyze multiple screens or portfolios, at the same time, compare and exactly run those benchmarks or comparisons, but also like overall analyses to keep track of portfolios and their performance, but also their theme exposure. So this is exactly something we're currently working on, plus like expanding also the coverage, adding more languages, adding more company website coverage for instance, to keep growing the database that we work with to extract theme exposures and additional relevant data points for or to form the investment process.

Excellent. Thank you. Again, in the interest of time, I'd close the interesting discussion. I could go on forever. Maybe we will do a second one sometime soon on further learnings and experience. Just the chance for some Q&A. I at least see two. One quite interesting, Sasha and probably also for the quant audience here, how do you administer -- you not only have the EATV Fund, you also have the EATV Index. So how do you calculate that one? What's the sort of index composition rules? How often do you review the composition or is that tightly aligned to the fund?

Sure. We have a separate product. It's not driving our fund, but it's meant to be a benchmark of our industry and we administer it through Morningstar now with liquidity rules so that it could drive a fund in the Europe or somewhere. But it's sitting there. So the answer is, we have a process where we accumulate candidates for the index and update it biannually. And so what this tool has been allowing me to do is as I work on the fund, I've been adding stocks to the index list to be added at the next rebalance. And so it's kind of complements our process and that there's a very long list of candidates. And when it comes -- and I have those ready and I'm constantly evaluating which ones are -- could go in the fund and then periodically, we add them to the index.

Perfect. And then maybe the very last one, and I hope the operator allows me to add two more minutes. For Alex, on the signals that you showed and the signals on the companies, are they only qualitative, i.e., the signal itself? Or can you turn them also into quant scores?

Yes, you can. And not just the signals, but also the theme exposures. That's important. All those data points are quantified by the system. They come with scores, which are also like comparable across the system and between themes and between signals. So that this is really a reliable, usable data set which can be combined with the other fundamental quantitative criteria as they now have also become quantitative data inputs.

Okay. I see one more popping up that I would like to take. So there's a question here. Does incorporating this tool into index methodology involve any additional licensing fees or perhaps usage requirements or restrictions? I mean, I can try to answer it. I mean, I'm aware if you use underlying -- so we built a common data lake that is fed by FactSet proprietary content sets and sort of thousands of public crawler that crawl the public web. So normally, the signals and intelligence and the, how should I say, the correlation scores of thematic exposure of firms actually comes as -- from this tool. And there are, I mean, various sort of revenue models associated with it. They can be a flat fee to basically use the tool and use it for your purposes that includes basically building the baskets that your investors follow or how you build your fund. We can also go by any sort of start, small and charge basis points for assets under management that relate to those thematically created baskets. So that's open. But typically, we don't have any sort of third-party implications or license fees from using the tool because you really generate this from the AI routine underneath, which is our proprietary technology. Unfortunately, we have to end it here. So if there's any other questions, please reach out to us, and we'll try to answer any of those. We'll be sure to follow up. So in sort of conclusion of the event, thank you very much for listening. Thank you to my guests and panelists, Elysabeth, Sasha, Alex. It was a pleasure. I hope we'll do it soon again. Again, to all the participants here, you will receive a recording of the presentation, and please do us a favor and fill out the post-event survey. We always strive for getting better, so that will help us greatly. And again, any question, let us know and we'd be happy to be in touch. With that, it concludes today's event. Thank you for joining, and enjoy the rest of your day. Goodbye.

Thank you.

Thanks.