In this discourse, we are privileged to engage with Marco, the esteemed CEO of Etna Research, as he elucidates the transformative intersection of artificial intelligence and finance.

Central to our discussion is Marco's insightful perspective on the imperative need for adaptability within financial institutions, particularly in response to the burgeoning demands for technological integration and customized solutions.

As he recounts his illustrious journey from academia in Milan to the forefront of financial innovation, we explore the challenges faced by asset managers in establishing efficient IT and data infrastructures. Marco articulates the mission of Etna Research, which seeks to alleviate the burdens associated with these complexities by providing scalable and adaptive frameworks that cater to diverse client needs.

Furthermore, we delve into the role of agentic AI in enhancing human decision-making processes, underscoring the significance of merging technological advancements with profound industry expertise to foster sustainable growth and resilience in the financial landscape.

Takeaways:

• Marco, the CEO of Etna Research, emphasizes the importance of adaptability in financial technology.
• The demand for customized investment solutions is escalating due to technological advancements.
• Etna Research aims to streamline the infrastructure necessary for effective asset management.
• The integration of AI into financial services must prioritize human understanding and engagement.
• Successful investment strategies require a balance between innovation and traditional practices.
• Maintaining mental and physical well-being is crucial for long-term success in the finance industry.

Welcome to Connect With Purpose. Today we are joined by Marco, who's the CEO of Etna Research, a firm which is doing something a little different.

I'm really looking forward to the conversation, Marco, to talk about your work, your vision with Etna and your view on AI automation and the future of finance.

So I think let's just get started and perhaps you'd like to give our listeners a introduction on, you know, your journey so far, how you've gone from, you know, studying in Milan to trading on both the buy and sell side to starting, starting this firm and what you're doing now.

Well, Augusta, thanks first of all for inviting me. We have met each other many years ago and you know, we still talk a lot about business and finance.

So, you know, I'm very glad to, to join this podcast.

So moving next to, you know, a little bit my background, yes, I'm Italian, I started, I did my PhD in Milan, I actually Alphabet in London at Cass Business School. So I've been exposed to what everybody know about, you know, the London financial service life or lifestyle in a way, solo.

The reality is long hours and, you know, passion for what you do. And it's been always my, I would say my clear pattern over the years in London.

So I've been through different shops, big and small, incumbent and radical, innovative. I've been, you know, big bank. So I started with Citi, I joined Horizon, which at the time was a multipod hedge fund business.

I had the pleasure to push the innovation in a robo advisory business called Money Farm for a couple of years. And then I had also the chance to launch an ML Macro hedge fund at Lumen Capital.

So I've been again to sum up across the buy side, sell side, very hardcore innovation and let's say much more a mix of digital and traditional tools. And after this, you know, this few shops, I realized that there is a massive demand for customization with the right technology these days.

And that's why I launched Etna Research years ago.

Interesting.

So in terms of, in terms of obviously what you've launched with Etna Research, can you just maybe explain a little bit about, you know, what you're doing exactly, for the listeners before we get into it?

Absolutely. So ethnic research solves a very specific problem that any business leader in asset world management is facing these days.

So we know that it's very hard to put together a big investment, an IT team, a data science team as well. We know building IT infrastructure for high performance activities, hard. And we know as well that the data zoo is hard to crack and put together.

So these are, I would say clusters of problems that when you see the entire picture is very complicated. And I face it myself as well. So when we started the firm was really about can we crack all this problem at the same time.

And so it means bringing an infrastructure that allows the clients to really build solutions, strategies, no matter if your use case is ultra sophisticated like for a high performance hedge fund.

But it could also be for a more vanilla use case where you know, a wealth manager is trying to build certain type of capabilities and it might have some partial capabilities or it doesn't want to spend the capex to build that capabilities or it doesn't want to spend high opex to run this capabilities. So that's kind of where we play nicely in the news in the use cases.

And how come you think that obviously you can get this research done in a better way or faster way than these firms or clients?

Yeah, I mean first of all everybody knows the pain and it takes a lot of time to really build a proper institutional infrastructure. And it is a process, it never ends.

So starting from this clear need, the reality is that then the second key KPI is really about how much do I get from my investments.

And most of the people out there are facing the struggle to measure this return in terms of infrastructure development, data infrastructure execution and so on. So that's where we play a kind of partner role where we don't want to replace anyone.

It's really about enhance or being relevant for what you don't want to build internally or if you want to have kind of a second opinion type of thing or another angle that is kind of a lateral thinking angle here. So we're trying to be orthogonal or uncorrelated to what you have internally. That's the key aspect too to really go and get the trust of the client.

Do you find that you work in a specific type of research or a specific type of asset class or market or have you managed to essentially create this kind of research for all areas of the industry?

So it goes back to our background.

So myself and David Koh is the chairman, but also was my boss in the past we've been exposed to pretty much all the asset classes style, not H50, obviously this is not a business for HFT but everything that some people call it me frequency, lower frequency.

So that's kind of the space we play in and, and the reality, you know, we obviously stronger on the macro asset class menu, although we are actually now recently onboarding as well on the equity side because again, it's really about the pain to build a proper institutional infrastructure first.

And then, you know, you actually, you know, it's something like I did my capex so now I can add crypto or Vol, you know, analytics to build again solutions for the clients.

So it's really about this kind of first investments and now we're now branching out from our core initial use cases where mainly in the foreign exchange space.

Interesting. And do you find that the reason that you're obviously it's as you said, these things take a lot of time.

Do you think that obviously your firm has this advantage because you've already got the team in place, you've got the people who can do the work and it's already set up. Is that the advantage? It's a time advantage.

I would say is really about trying to have an infrastructure that could scale massively compared to trying to build an infrastructure that fits only one use case. Typically in a business environment you want to get the job done in a certain time window. But typically it's not designed to be strategic.

It tends to be designed to be tactical.

Although they claim it's strategic, but the reality is very hard to do it now you need to have a technology angle and investments which is a multi year horizon investments. And I haven't met so many firms that plan three years ahead or two, three years ahead in financial services.

So that's kind of where we, where we play our role.

So we want to be useful at that stage that you realize, hey, it's going to take ages to put together a great team is going to take ages to actually build something to put strategy solutions on top. So that's kind of the role we play in this environment.

Yeah, it sounds, it's very interesting.

Is it a situation where a client comes to you and they have and you create this infrastructure of this research and then it's a evolving process where you stay working with them for a long period of time and you're tweaking things for them? Or was it a process where you do one piece of work and you pass it on to them to kind of implement.

So obviously at the beginning there was a lot of client, you know, discovery about what are, I call it the primitives or what clients care.

So we figure out exactly all the, you know, the, the style you want to cover, the, the hard release constraints you want to cover all these things that are relevant for the clients. And it took time to map them, all of them. And but our experience so far has been that the, the needs are always evolving.

So you start with A and you end up with all the letter of the Alphabet pretty soon because again, the key aspect is how can I be fast, how can I avoid capex, how can I keep the OPEX low?

These are the, I would say the business KPIs, it doesn't matter if it's super quantum AI, whatever and all like basic quantitative research is really about, you know, investments, OPEX outcome and these things are evolving over time.

Yeah. And on your website and on LinkedIn you mentioned that you're involved in Frontier AI.

Can you maybe explain for explain to me what that exactly means to you?

So there are two pieces of our vision.

The first is based really on our expertise to be in the trenches, understanding the friction of the markets and trying to build an infrastructure that could handle adaptivity.

Most of the last 10, 20 years, great successes were, you know, based on, I would say most static approaches markets in the last 10 years given, you know, democratization, access speed, tools. Computing is changing very fast now.

Maybe a value investor might say that, you know, the, the key aspect are still the same over the last 10, 20, 30 years. The reality is in order to produce, you know, returns or help producing better returns is really like the key aspect for us is the adaptability.

And that means a universe of techniques that are computationally heavy and require again the infrastructure, big push and big build. And that's the first key aspect. So AI in capital markets, you know, you will never use, you know, a generative AI model to predict the markets.

It's not designed to handle that kind of use case. It's great for producing great test or kind of sensible doc. But it's impossible to use the LLM or anything generative related to forecast data.

So you need to go back to how to again bring the adaptability piece, which is the key, the first component. And the second one, which is a little bit more, I would say not in the radars yet, is really about how we interact with research.

So historically it's been a lot of work on people running code and so on. We think this evolving into something which is much more human, which means voice, which means text.

And that is the key missing piece in the industry, how to make research human again. Because the big push after 08 has been all about algorithmization, complexity and so on.

But the average user, the senior leader wants to have something which is much more straightforward and he talks the same language how to bring the business language into quantitative research. That's the second thing we Want to crack.

Interesting. So that, so the AI is playing, AI is playing a huge part in your work already.

I guess again in, in different ways one is more evident, the other one is still in a way in stealth.

It's much more productivity tools but the gear is also to how to create again use cases on the fly and how to be fast, how to be, you know, plagued fast with clients. How to be, how to you know, essentially destroy the capex cycle for the clients. How can I reduce the paying for the client?

That, that's the way, that's why you see easier if you understand what the other angle is looking for.

Yeah and I think your point on the human element is really interesting because of course to be able to, you know, to be able to raise funds, you know, your clients are going to need to be able to explain that to people who don't necessarily understand these strategies in that level of detail.

Yeah, the key is, I mean obviously now we work a lot with, with funds but the reality is that there is a big push also in you know, family offices, you know, big, you know, trading groups or treasuries from big corporations and they realize that a lot of knowledge can be used as well there with the same cutting edge approaches, stimulant technology of you know, the best of the world.

The key aspect is really to make all of these very high IQ activities explainable and digestible and usable by people that don't need to know the full menu of the quant word to use it. So this is for us the key aspect. Even if you're a sophisticated guy, once you get senior you really go back to business.

KPIs and simple and straightforward ways to take decision is not, it shouldn't be more complex, it's just that you understand what is the world around you. But that's the key aspect for us.

Interesting. I know you talk, I've seen on some of your LinkedIn you've talked about agentic AI.

Can you, can you talk a bit about that and what it is and how, how, how far away you think that we are from that?

Yeah, I mean this can relate to our second, you know, the second point of our vision.

Obviously I'm, I'm a deep, you know, passionate about technology and we have seen this massive trend over the last few years and the capabilities you can actually, you know, when practical use cases that you can implement with this technology.

Now the problem in finance and in other high accuracy businesses is that if you need a very high accuracy use case these technologies are very tricky which again they work for certain type of basic, you know, information extraction and you know, tax summarization. All these kind of, you know, I would say basic use cases which of course they, they boost a bit to the productivity but they're not radical.

So you still have this co pilot that is in and out, in and out with you, in and out with you, which I'm not sure we humans are going to be happy to be constantly, you know, back and forth bombarded by a copilot. So I think this is kind of where we are today in terms of use cases. But it's mainly, you know, tax analytics on steroids.

And recently there was, you know, this kind of claim that now perplexity anthropic, you know, big tech firms, they can go into the, in the trenches of the financial vertical but again they are not adding. The biggest problem with these tools is reasoning. They don't think like humans. Right.

They look like, they think like humans and they produce something that might look like good.

But then again coming back to our point about you know, high performance teams, people that are super strong on the deep knowledge of what they do, they easily spot problems and that's what was our experience. So the next step was really like what can we get from the agentic AI and how can we bring to the next level in finance?

Which again goes back to my point about agentic AI is really for us like a front end. You know, it's the distribution, it's the human attach.

But don't let these machines doing, you know, the, the, the hard work, certain hard work that you require where you require very high precision which is again the, the, the key aspect about bringing your own heuristics and experience and merging them with generative AI instead of relying on the generative AI, you know, industry heuristics which are typically bad outcome.

So you are saying that obviously your expertise over the last, you know, many years has been the human experience is still just as important, you know, and then blending it with AI.

Yes.

So in essence, you know, what we do at Epine Research is to get, you know, decades of how to build solutions, strategies, indices and how we can merge this with Agent Ki on steroids.

So the typical use case we see is people, you know, they have an idea, we talk with a client and they have an idea A and then after two weeks they say, but actually I'm not, maybe, you know, I was not clear on that requirements and maybe this is what is important for me, which is B or C. And that is the part where you struggle to, if you just have a, you know, a quantitative approach and back and forth with the clients, you really want to provide the human engagement that the agentic could, you know, facilitate the, the sales engineering kind of way.

The sales engineering part of what in Silicon Valley they call it the forward deployment engineer, which is a very high demand job these days, but in essence is a sales engineer on steroids. And that's why the friction, that's why people don't close deals and so on.

Because you come up with a client wants to B, you're not fast enough to, or you have to reinvent the wheel to get B and you don't close the deal. So that's for us where the agentic side and becomes our frontman in a way to simplify the client's friction.

Yeah, it's very interesting because I've seen a lot of people using it for, you know, software development and things like that, but you're, you're, you're, you're using it in a very different way on as you said, the front end.

Yeah, I mean, just to conclude on that, I'm not saying there is wrong use cases for software development. There are certain things that are relevant but typically are not.

Again, in the high precision, low latency, certain type of clients, you know, they're not going to use massively these technologies or they rely a bit, but it's useful for, you know, I would say most of the, you know, 80, 20, right. 80 could be covered by these things. But a 20, which is where actually people make money and requires high precision.

And then you need very, very, very senior people involved to make sure that everything is aligned. And so that's kind of the angle. I think in general you can cover the average use case, but not necessarily the super extreme high precision problem.

Interesting. And I know you mentioned to me that you view AI as the biggest boost in productivity in the financial industry and in financial history.

How do you think funds involved in capital markets are dealing with the future of work and automation? And you know, how are they going to be, how are they going to deal with this?

Now?

It's important to say that, to say that, you know, this, you know, looks like a recent trend has been the, the trend over the last 20, 15 years because it's very related to how you can handle your computational advantage. So everything is becoming much more computational.

Again, coming back to my point about adaptability is the key aspect for us to be agnostic about what happens, but you need to be adaptive about what happens around. So it's not new. It's always been a constant trend. Obviously with generative AI certain things could be boosted.

But the key aspect again the, the things we see among in our networks really is really about you know from the most sophisticated to you know, the long only guys and the wealth managers is really there is an appetite to accelerate and there is an appetite to realize that you know in the past was really about I build it myself. I try to do as much as I can internally with all the risk of you know, ages to build together.

Pushing risk is always, always there and then you know, out of control budgets. So people are much more. There is, we see much more appetite in the last years.

The reality is also they I think there is much more also appetite to work strategically within external partners. Which that's why we saw as well. We have seen this kind of acceleration of requests and demands because people say hey, I have to solve this problem.

And that's my you know, investment horizon, capex Horizon. And I have to figure out, you know what is what happened if I do or if I work in external firms in t mamp.

It's super interesting.

And I know that you, you mentioned to me, you know before the interview that Sam Altman said that it'd be possible to build a one person billion, billion dollar company. And you think that might actually come from finance? Like how, how far away do you think we are from that?

You know, do you think that's something in the near horizon or how far away do you think we are?

So obviously you know that's maybe it's not one, but it's three, something like that. So the reality is also because you want to share the pain of running a company with someone else. It's part of the journey.

But the reality is obviously here is much broader than certain use cases on what we do with capital market. But finance has two great advantages. First is global is a very historically a high margin business and it's global and it's big.

So you need to start with a very big total addressable market. And the incumbents, it's very hard to change the DNA of incumbents. So you can be faster, they recognize what you're doing.

But potentially maybe the option is to buy you. But it's very hard to compete at least for in some, some situations.

But the all these tools that are out there these days and technologies allows you to do it.

So now you can do it because from even like from a business management perspective, you know, hr, accounting, finance, marketing, they have so many activities that these Days can be structured and more automated than just three, four, five years ago. So when you run a company, that is part of the thing, right? Where to find new client, new leads.

How can I simplify the conversion of leads in clients and optimizing the business? So that's kind of the running the business part, which is obviously easier. And so that's definitely the angle where we see this is possible.

Then of course, it's the product, right? What you do, you know, we do, let's say solutions for assets and wealth managers.

Someone is doing something else, compliance and so on credit and so on.

The reality, again, if you know your own heuristics and you understand certain type of mechanism and certain type of inefficiencies, then you can really combine your heuristic and AI and ML with the generative AI piece, which becomes a little bit your distribution advantage.

You don't need 300 people in New York, London and Tokyo, but you really have something out there that is able to actually solve the problem of the clients 24 7, always there, always ready to talk.

This is the angle that Sam Altman, I guess behind the scene was really referring to, which is like running a business is much easier and can be automating and building the distribution of your products. You need still to build the product and be good at that, but you have to distribute the products somehow.

So that's kind of where I see certain functions can be massively scaled with these new technologies.

And, and you talked about pain there obviously of, you know, I know you've been in this industry a while and leading your own firm. How, how have you survived in this industry and you know, doing so well in this industry for so long?

Like what kind of, maybe what kind of rituals do you do or are you into health or into fitness or how, how do you keep yourself, you know, mentally, mentally focused and performing?

This is a fantastic question because I always say, even to young folks, I mean, it's, it's an insane marathon. So first you need really this kind of strong determination to achieve your goals.

And you know you're going to have, you know, like a good strategy, we always say, has a lot of drawdowns. So you are successful because of the pains, you are not successful because of the wins.

So this is the number one rule to realize that if you don't have pains, you have no wins. And then of course, again, determination. So you have to recover your drawdown, your life drawdown every quarter, every year.

But yeah, your point is very important because I think especially in the Entrepreneurial side, you need to think like an athlete and you need to think that you need to be mentally fit, physically fit. You know, having a life that is very, we know that it's easy to make it unbalanced, but you have to find the balance.

Which means, you know, focus on the family, focus on your private life, finding the time to dedicate your brain to something which is outside work. And you know, when I was in London again, the pharmacy London, we are spread out in central Europe for on the team side.

But it's really to, to balance this, this the stress right that you might have from, from, from work and you know, delivery and deadlines. So I think that's for us. That's what I learned over time. That's what I didn't learn at the beginning, was all about pushing the boundaries.

And then you realize that if you have to play this game for a longer time, you cannot play the game of oh, I work 15 hours per day, Saturday included, because it doesn't work. It's great for some Y Combinator, you know, tech, tech pros mentality I've seen in us. I'm not saying we don't work.

I work mentally, you know, a lot of hours when I'm outside the office. But it's really about finding the time to actually take a break and focusing also on the things that are important in life.

And yes, I think in my case, you know, I, I don't have so many hobbies.

Yeah, I play tennis, I love, I love to go to the gym and all these things are important to have in your routine and, and of course, you know, great, great, great relationships. So I always, I'm, I'm lucky that over time as we, I, we, I develop, you know, a fantastic network of professionals that generally like what they do.

This is the key aspect and you know, we help each others and you know, we learn from each others and that's kind of the, I guess the never ending growth process that you should have. Instead of trying to settle down on certain type of, you know, business goal, it's really about growing together and helping even competitors.

I mean I've friends in, you know, they're competitors and we help each other and you know, the reality is it's never a game of, you know, me against you, but it's really guy me against myself. And I always stress this, it's always trying to be better than yourself and sometimes we forget this.

So having, you know, these elements in your mindset are very important to again to play the drawdown. They Always. We're all we're always going to face in this business.

Yeah, I like that and I think it's right. It's the marathon idea, not the sprint idea. So yeah, I think that's great.

And we always ask this, but what do you think your broader purpose is within work.

For me, for the firm and for my colleagues is really about can we build something that has a massive global impact? Can we build something that's simplifying life of our clients but at the same time can we be remembered to do something iconic?

So this is something that to me is like you need to have this vision of building something iconic to really then manage the quarter of a quarter because otherwise it's like if you don't have this kind of mission every day, every day written somewhere, it's very hard to really do the tactical thing that you have to do. So you need to have this kind of things.

And the second again point and this is something more recent because I had now the chance, I work, I have my firm so I can move freely on certain activities. The give back.

So the importance of we have an association in the south of Italy of technologies and entrepreneurs giving back, building new ventures, helping other people like joining this kind of journeys. It gives me personally a lot of fulfillment as well. So of course the time limitation that doesn't allow me to do a proper massive impact.

But we try to do it properly anyway and we are a very good group of people. It's called pulatech. So it's kind of a.

The an association in the south in the boot of it and the south of Italy connected to the major apps, you know San Francisco, London, Singapore, Riyadh and Tokyo and bringing new businesses, new operational operative companies and here technology company.

And it's fun but it gives us, it gives me, you know, a lot of, you know, joy to see other people trying to do it and trying to have an impact in other industry. You know from prop tag to clear tech, travel tech. You know, we have seen, we have seen it all.

But that is to me is kind of, I would say the, the, the key aspect to, to give back to people that are, that deserve to, to go to find their, their way. This is actually one of the key aspect in everything we do even internally pushing to the limit people that actually have potential and everybody.

The beauty of here as well is that everybody could really build. It's like a scientist, you want to be remembered to do something meaningful to society. And that's a very typical scientist point of view.

And because again, we are all. I mean, David is an astrophysicist. I'm an engineer as well. We spend our life in industrial and academic research.

We like this idea of providing an impact and something that people see that has impact. I think that for us is very important.

I think it's amazing. I've really enjoyed the conversation. Marco, I think the. Yeah, the topic around. The topic around AI and automation is amazing. And thank you.

Thank you so much for your time. I think we're running out of time, so. Yeah, I'll say. Thank you. Thank you there.

Thank you very much. Augusta. Talk soon.