Trading Tomorrow - Navigating Trends in Capital Markets

The Intersection of Hedge Funds and Cutting-Edge Tech with Serge Houles

• Numerix • Season 3 • Episode 23

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In this episode of Trading Tomorrow, Serge Houles, the CEO of Tidan Capital, explores the evolving landscape of modern finance. Serge and host Jim Jockle of Numerix discuss the challenges and opportunities of rapid technological advancements in the financial sector. 

During this episode, the two also delve into the transformative potential of generative AI. From the initial excitement to the industry's careful approach, Serge provides a thoughtful and unique view of the future of AI in finance. He also shares insights into tokenization, discusses the crucial role of data and expertise, and emphasizes the need for accuracy and reliability as technologies evolve.

Lastly, Serge introduces Nova, a market-neutral volatility strategy, and discusses how technological advancements can help investors navigate volatile markets. Tune in for an in-depth discussion you don't want to miss. 

Speaker 1:

Welcome to Trading Tomorrow navigating trends in capital markets the podcast where we deep dive into technologies reshaping the world of capital markets. I'm your host, jim Jockle, a veteran of the finance industry with a passion for the complexities of financial technologies and market trends. In each episode, we'll explore the cutting-edge trends, tools and strategies driving today's financial landscapes and paving the way for the future. With the finance industry at a pivotal point, influenced by groundbreaking innovations, it's more crucial than ever to understand how these technological advancements interact with market dynamics. Welcome to today's episode where we discuss financial technology's dynamic and ever-changing landscape. Terms like AI, blockchain and cloud computing are not just buzzwords but the driving forces reshaping the financial industry's very core.

Speaker 1:

Over the years, we've heard predictions of how these groundbreaking technologies would revolutionize everything from banking to asset management. But have they lived up to the promise and what do the experts in the space think about their impact? Joining us to help explore these critical questions is Serge Holtz, ceo and partner at Titan Capital. Serge not only oversees the day-to-day management of the firm, but also leads all global marketing and business development efforts. With a wealth of experience from prominent hedge funds and fund-to-funds worldwide, serge's role has spanned quantitative research, risk management, product development, business development and marketing. His strong academic background includes an MSc in econometrics and finance from Paris' Dauphine and post-master's degree in banking and finance from the University of Paris. So, serge, first and foremost, thank you so much for joining us today.

Speaker 2:

Thank you so much, Yma, for having us and having me.

Speaker 1:

On this show, we've explored a range of cutting-edge technologies, from AI to blockchain, virtual reality, cloud computing. As someone who's deeply embedded in the industry, how are these innovations reshaping your day-to-day work, and to what extent have any of these become indispensable?

Speaker 2:

Yeah. So if that's okay, jim, I'll start by sharing my personal journey of using generative AI. Mostly, I'll start with that. In terms of technical innovation that I think would have potentially the most impact in our industry. I think you also might be the same to me and many others. To start, I'm a big fan of machine learning, so I've been using machine learning in my previous role doing some interesting optimization.

Speaker 2:

I was there, you know, before JetGPT was released two years ago. One of my very good friends, tony Gida, is an expert in machine learning. He's read a number of books and he made me aware of that release. I was not aware before that. So I was there, right there, you know, when it was released two years ago, in November, and I started using it and I realized, right there, the potential, right. It was like so the, it was crazy right, the breakthrough, and it's looking at this, getting all these answers and even asking crazy complete, complicated questions and all doing, all doing, all translation or summarizing documents, and so, very quickly, I could see, you know, how this could be, uh, you know, practically implemented in our business, right to uh, to automatize and all the tidy tasks that you know combined together takes several hours. But then you know after using it personally. First I was like I realized you know what everybody is talking about, right, the hallucinations, right, and also that some of the answers were not very accurate for me personally. So I started learning about, you know, few, uh, few, short, prompting and customizing and uh and uh.

Speaker 2:

I think that's where the asset management industry is right, like in terms of uh, you know, adopting, uh, adopting this uh technology. So from what I see, I mean I'm the ceo of titan capital, so I'm I talked to a number of peers you know like ceo and leaders in that industry and and so I've been using chachiPT a lot and I'm super excited about the potential. So I've been sharing that, you know, and I meet other people, other peer, you know CEOs, and they're like ChatGPT, what you know, like how do you use it. So I can see that it's really not been adopted yet, not even you know for people personally. So, and I think you know, it's not because the industry is resistant to change. I think it's a good thing and it's simply because we have that fiduciary responsibility.

Speaker 2:

I think personally there is a lot of expectations about that industry of adopting the new technologies, for many reasons. To start, there is a lot of brain power in the industry, right, in the financial industry. So then you would expect all these people, all these brains combined, you know, to, you know to customize all these algorithms and make it you know and implement in their business and day-to-day work. To start, the second is that the industry has been, you know, compiling all this data, structured data, for years and that make you know and that make the user obviously easier to implement machine learning and all these kind of new technologies. But then, at the same time because this is quite unproven, right Like I lost account of how much data has been created the last three or five years like that we created more data historically the last five years than we have ever before. So one is a lot of data and all these technologies, they, you know.

Speaker 2:

The pace of evolution also is crazy, right Like we see, already two years ago, when you look at the first, or not even two years ago, even you know, a year ago, we had this, you know, video generated by, you know, generative AI. We could see how you know all the, how deficient they were and all the defects in the video, and then the year after, they corrected that. So it's so. The pace of evolution is so quick that I think it's a good thing that the industry has not adopted this technology yet because of this regulatory responsibility. So we have to make sure to start. We want to make sure that we protect our client's capital before, obviously, I mean the end game for us as a major, and especially in the apps, you know, in the hedge fund industry, as we are trading capital.

Speaker 2:

Obviously, at the end of this, you know, to deliver absolute return for our clients, but the first duty is really to protect our client assets and I think nobody expect us to, you know, to adopt all these technologies and hope for the best, and I, if I do, yeah. So, if I could, I would like to draw a parallel here to people to understand. It's like the medical you know. It's a parallel to the medical, you know industry, where it's like you would ask a surgeon you know to use a AI driven scalpel because, you know, improve precision by 0, 0.2%, with the risk of, you know, would see hallucinations and dread, just to test it out on patients. It's the same with the financial industry. So I think the potential is great in terms of automatizing all the tidy tasks and, obviously, processing data. It's obviously great for unstructured data text, video and so forth but we're not there yet, I think, in terms of the pace of adoption. So that's for GNAI.

Speaker 1:

So, you know, one of the key things or trends over the many years is, when it comes to new technologies, it always seems the buy side leads the sell side in many ways. You know why is that ways. You know why is that? And you know, I guess. And the second question is you know, given hallucinations, given fiduciary responsibilities, you know what is the confidence level for things like AI to become even more ingrained? You know, within day-to-day operations, you know where's that confidence level going to be?

Speaker 2:

Yeah, I think that's a very good question, spott. So I'll start on the first one. I think, to start, the vast majority of this industry is great asset managers. They are discretionary, right, they use their own judgment to make investment decisions and I think for many of them, like it's a bit of a leap of faith to start, you know, to allocate out like part of your that process to an algorithm and try, you know and and trust it. I think that's that starts there, right, like, um, they, all these people, they trust their judgment first, and that's also one of the biases that we find in investing right, like overconfidence. Right, and maybe so that could be a resistance to adopting all these technologies.

Speaker 2:

There are many things that obviously could be done without allocating the entire process and that we see already. I guess you heard about JP Morgan right, creating their own. They have their own internal junior analyst based on AI to process all the corporate information, so all the unstructured data. Make use of it for the portfolio managers. Right To have all that information digested. You have a huge amount, a huge gain of productivity and time, and then you can spend value at time in terms of making meaningful investment decisions. So I think maybe the resistance could be there to start. You know like. You know like afraid of the change, afraid of you know losing their. You know you know being competed away. You know also that could be that you know being competed away by the algorithm and recognizing that you know eventually, potentially they won't have these biases right. As human being sorry to jump on this one but as human beings we have so many biases that doesn't make us a good investor in the first place. So obviously using this algorithm that should not be biased could be a way to improve yourself on yourself. So I think that would be great. To combine human judgment with all these tools and this technology, I think would be tremendous. But then again it's slow because you know scale of change.

Speaker 2:

My take, then, the other one is, I think is a catch-22, right, like if you start somewhere, you know first by somehow, you know collecting all this data, the infrastructure. You know all the reports, all the video, the earning, the earning calls. You know for for an analyst. You know like starting processing all that and make meaning of that and getting confidence in. You know in the lower value add, you know in the chain, the value chain of a portfolio manager, then you gain confidence. When you gain confidence, you use it more. If you use it more, you create more data for this algorithm to be trained on and if, if they train on that you know like in terms of being incorporated into your value chain all the way to making actual investment decisions, they get better and then maybe that's when you're starting a broader pace of adoption.

Speaker 2:

But it's again, it's slow and I think also I mean what's important to, I think, to understand my take is that you know technological change are not deterministic. It's not because they are there that they have to dictate. You know the pace of change and when and how you know the industry will adopt all these technologies. I think it's I mean, we've done it before right in terms of using internet, in terms of using mobile phones for our day-to-day work. I think it's a good thing that things take time. You know, for the best of our investors, I would say so.

Speaker 1:

in today's universe, where is the alpha? Is it in the data, is it in the strategy, or is it in the intuition of the individual.

Speaker 2:

That's a very good one. Actually, I was about to get into this one also. At the end of the day, I would think it's the same. Right, like you know, back many, many years ago, decades ago, the alpha was in the information edge and later that became illegal. Right, that became, you know, insider trading and all this kind of thing so like, and that turned into later to be an information edge in terms of how managers could interpret some information in a better way or because they had access to information. You know before, like the macro data or, you know, building model and making sense of a lot of data, that was some form of information edge. But at the end of the day, you know, these data quickly have become available to the vast majority of managers. Right, it became. It became, like, you know, cheaper because many more managers had access to it, so you had the economy of scale, and then it become cheaper and then, you know, everybody had access to it. So today, you know, like, if you exclude the new technologies, today I would argue Any asset manager in the world has access to exactly the same amount of information and the same quality of information.

Speaker 2:

So then back to your point. Yes, it's always how you use it and that's the same as comparing to. You know. I think that industry you know the hedge fund industry and the alpha driven industry is the same, as you know, like competing in sports. Competing in sports, like if you are a golf player or playing soccer, you have access to exactly the same tools and equipment. So when you're a football player, you have the same shoes and you use the same ball, and when you're a golf player, you are exactly the same. You have the same clubs each. Maybe they could be customized, but the same way. But at the end of the day, there are only 10 best players in the world. It's the same in office. It's really how you use information, how you process, how you combine you know data and you combine your own judgment, how you combine financial modeling and your experience, all that to take investment decisions.

Speaker 2:

But my take sorry, that was a long one and you go right there in terms of what I'm passionate about. You know, at the end of the day, it's all about discipline. That's why I'm quant originally. So I really, like you know, quantitative process, well-researched investment models. But what I found at our firm sorry to comment, on our side also is that, like even the people in our firm you know having, you know making discretionary decisions I can see that through, like you know, very challenging times like the one we've seen, you know, the last couple of weeks, you know we could, you know they've been questioning, right With the sale of like nobody understand and the VIX, you know skyrocketing for discipline is and experience, and that I think is hard to At this stage. It's hard to train, I think, mike maybe I'm a bit biased- Well, you know it's funny.

Speaker 1:

I know what my VO2 max is, I just haven't figured out how to optimize it. It's actually easy. So you know, clearly, technology has undoubtedly driven progress, but it also can introduce new challenges. Driven progress, but it also can introduce new challenges. So you know in your experience how is this rapid evolution of tech, you know, almost complicated your professional life, like my VO2 Max yeah yeah, maybe we can compare our VO2 Max.

Speaker 2:

No so like in many different ways. Like so, I think you in your question I think that's part of the answer, right, it's just rapid, right, like so I mean. Like, I mean, I don't know how much you use LinkedIn and, uh, you know, like, there's not one day where you can see a post of okay, these are the new tools to do this and this, and that changes every week. A challenge in itself, because obviously that challenges you of doing your job in the best way and have access to all these tools. But it's not like you learn these tools. You know. Like, even if it's, you know, there's a lot of algorithm and everything, so they process a lot of your task, obviously without any input from your side, but they still require a lot of customization and human oversight. So that has that put, I think, a lot of strain on people of being on top of, okay, what can you use? How can you use it? How can you stay on top of that? So that's one challenge.

Speaker 2:

The other one touch upon your question. You know, in terms of data, information, everything I think we went from you know having an information edge in terms of quantity, like you know, having access to that information and to being now a disadvantage because there's so much information out there, right, there's a lot of noise, there's so much data that is you know in any way in any shape or form, like social media news, you know. And like satellite, you know. You hear about all the story about satellite images, right, Like, that's a typical example about you know how to use machine learning to predict the shift in macroeconomic cycle. I take it doesn't work so well, but anyway. So, yeah, so that creates a challenge in itself. How do you dissociate from you know what kind of information is meaningful for understanding how that can influence that company or that country, or you know, like. So that's a challenge in itself. So obviously sorry on this one. So like the technology is also here to help you, right, like, so sorry on this on this one. So, like, the technology is also here to help you, right, like. So, yes, in the first hand, that creates a lot of data and information everywhere, but that the technology can obviously address that in trying to sort out, using this kind of algorithm to sort out what it's meaningful, especially if you supervise learning. But again, you know, then you need to understand it, like, and you need to test it and you know, in data and information, you need to understand it, like, and you need to test it. And you know data and information, you need to get comfortable, I think the third one is cybersecurity.

Speaker 2:

Yeah, so like, yeah, I mean, we've been, I think, on our side at Thailand, so we've been using Gen AI for, like, marketing and general business, and it's incredible, really, like what you can do in terms of producing marketing material and getting some template for legal document or, you know, summarizing something or get bullet points or something.

Speaker 2:

But then it also oh, now I sorry I go back to being disciplined, because it's very difficult to find, you know, to find like really, uh, you know very different processes in terms, okay, you can use it, or either there's some firms have decided they block it all together.

Speaker 2:

I don't think it's a good idea, because then obviously you lose some form of competitive edge if everybody else is using it, but then of course, you have to use that. You know, um, you know common sense like. Okay, of course, when you use it, you have to take out all the very sensitive information, all the client specific information, all the company specific information. So that requires a lot of, a lot of discipline. So on one hand you know obviously that you increase productivity, because it's sometimes very hard to start from a blank sheet of paper, but on the other hand you have to be very disciplined in how you use it and obviously that human oversight because of the hallucinations you know, if you just use it right there, then you can have anything that comes out of it in terms of making up a story about your company that is, that is not your company.

Speaker 1:

I've seen that many times yeah, and and the there was a us supreme court case, uh, or citing, uh, the attorney was, uh, in front of the united states supreme court, citing a case that never happened. You know, I I'm sure everything will improve, but you know it and it's improving every day. You know you bring. You brought up one interesting point in terms of you know, and it's improving every day.

Speaker 1:

You know you brought up one interesting point in terms of you know, social media and you know I think back, if I go back maybe five years, there was the you know the trend of how can we gain sentiment analysis, how can we, you know, utilize and harness all this data? You know, albeit unstructured. You know in terms of investment decisions, and I think you know, for the most part, the conclusion was it's an input, but it's just an input. You know the edge isn't there. But then you know you have the whole GameStop issue and Reddit and things of that nature. You know to what extent has that GameStop experience perhaps changed the way people are thinking about social, in terms of understanding sentiment?

Speaker 2:

I would say, you know it was funny because obviously you had that episode a couple of weeks ago, months ago, right, when he was back trying to influence GameStop again. So, no, that's a great point, because I was about also to get into, you know, how we could potentially at Titan, at our firm, how we could potentially use, you know, ai, and that's one area where I can see the clear benefits. Like, as you say, at the end of the day, you know, it's very difficult to understand, okay, can that create an edge if everybody can do it, to do this? I don't think so. But then, at the same time, how do you make sense of that noise, right, like of all the uh, the uh, the reddit and and uh, all the kind of form. And obviously now people realize. So there you have a point. People realize all these group of investors, if they gather in a platform somewhere, they can actually influence, uh, you know stock prices and it's's.

Speaker 2:

It's a challenge for us as as active asset managers, because we have our risk management and if that goes against your position, you obviously have to make decisions, right, like so so, yeah, I mean that's one thing of you know, looking at this in terms of risk management.

Speaker 2:

This short, you know the next game. But going beyond that, you know extreme situation, which obviously does not happen too often, right, like it's just been a few cases and I think it will remain this way because that was a combination of heaven that led to that. You know craziness, but I think in general that's really an area where using NLP, so natural language processing, to make sense of unstructured data, so like earnings codes, you know like, you know after the central bank policy and you know all the guidance from central bank policy video, audio and process all that information to make sense of. Not the information itself, because that's that you can do on your own right, but like the change, the nuance of, you know, the change between one call to the other or that could provide some valuable insights into investment processes.

Speaker 1:

But you know you bring up a really good point, especially when it comes down to the Fed. I mean, you know I go back to the 1990s and you know, thinking about Fed calls, it was sometimes what wasn't said or the nuance or a head nod of the way something was said that could move a market. You know, is that just getting lost in transcripts?

Speaker 2:

Yeah, I think I think this one is a difficult one because it's so polished. I think I'm pretty sure I don't know how that works, the process'm pretty sure there are many people checking you know how, how the um we did several times, you know how can that be interpreted outside, right, like so. But maybe that one is a harder one, you know, in terms of trying to make sense of that information. Uh, but then on the, so yeah, I'm not so sure if, if you can actually make conclusions about that, because sometimes you know how it has been the last 10 years. Good news is good news, bad news is bad news, like some, like I'm not so sure if that's significant in terms of making meaningful, you know, decision out of that in terms of the macroeconomic direction, right, but I think for companies that's that's super helpful because I mean the. But I think for companies that's super helpful because I mean management, as and there was an example a couple of days ago to see how management and how people perceive the impact of management on the company. I think it was today or two days ago Starbucks announced that they would, that they had you know that they had the Chipotle CEO and the stock rallied 25%. So that's the kind of thing that you that specific example was obviously announced.

Speaker 2:

But I mean these kind of things in terms of, as you say, getting the nuance, the change of nuance in the narrative for earning scores, any kind of unstructured data that could be very useful to gain insights on companies. But again, sorry, before I Last one, it's still how you use it. You cannot just simply say, okay, it's a two-star deviation from a monotone voice, something away from the average decibel in the voice that, oh, now the CEO is super excited in using that model and I should go long. No, it's just one more indicator of you know gathering, like a modality kind of thing, you know where you gather a different type of information, looking at the financial and deep down fundamental analysis, and then combine that with this kind of indicator to fine tune your, to fine tune your view on a company.

Speaker 1:

So you know, as you, as we in the beginning of our conversation you were saying how you talk to a lot of your peers and share your enthusiasm, you know however those conversations changed? Has it gone from strategies and markets to data lakes and APIs? And you know proprietary vertical AI, or you know what, what, how, what's the? What's the? The, the conversation among your peer group.

Speaker 2:

Actually that's no, that's a good one, jim, but actually not so much, to be honest, like and when I talk to people I know in my you know my network people like, oh, here comes the AI expert again, right, like, because I'm so passionate about it. I don't think it's on top of people's mind, from what I can see, at least to the people I talk to. Of course I can find examples. I mean, I know a few managers like Vaquant having implemented unsupervised learning, and there are hedge funds only doing this, but I think they're just one of the few. There was this article in, I think, yesterday on Bloomberg about Baliasni.

Speaker 2:

You know one of the multi-strategy funds and I think you know the because these funds they have, you know, a very large asset base and they obviously can afford to have larger IT teams and and people doing research and everything. Even then they say you know there's a lot of hallucinations. We have to be careful in how we incorporate. We can have this replace or not replace, but at least, you know, automatize a lot of the work for doing the junior investment analyst work. But there are a lot of safeguards everywhere in terms of you know, okay, how they can use that information in their process. So you know, even people that are supposed to be leading in terms of how much investment they can put into this people that are supposed to be leading in terms of how much investment they can put into this uh, they're still far away from uh adopting. So now the discussions are still around.

Speaker 2:

You know, like uh, obviously, the uh election in the U? S and uh, you know, uh, where equities go from here. And uh, you know like uh, because we have that platform with different teams. We have one team that is really quant and systematic and the other teams are more like discretionary. So there I can see, you know, obviously on both sides, right, like people being more quant, but even there you know like we sit back to understanding what we're doing. Right, like again, because that's why investors trust you know and trust us their money.

Speaker 1:

Well, I do have to add is there an election coming up in the United States? I wasn't sure of that.

Speaker 2:

Yeah, that's why it's so undecided. I'm pretty sure that's what people think that was a good one.

Speaker 1:

So in Q2, you launched Nova. Perhaps you could share with the audience what Nova is and now that we're almost at the end of Q3, how is it performing? Has it met or exceeded expectations? You know? I would love to hear about that.

Speaker 2:

Yeah, thank you for asking. So I mean, we actually launched two strategies last month, not only Nova, and you know, as we discussed that was quite a time to launch a new strategy, right, it was a kind of you say that, baptism of fire or something. We say that in French.

Speaker 1:

Baptism of fire.

Speaker 2:

Yeah, that was kind of that. So Nova, I start on the strategy itself and all the two strategies, what they're trying to achieve and how they've done in this environment. So NOVA stands for Neutral Option Volatility Arbitrage. It's a market neutral option slash volatility arbitrage strategy. I think it's quite unique because if you look with peer funds trading volatility and having volatility based strategies, I think what's unique about the strategy is it's long, most of the Greeks, so for people listening. So it's just to be more precise here what I mean by long the Greeks. So it's long VGA, meaning it has positive sensitivity to increase in volatility. It's long convexity, long gamma. It's also long theta, which is quite hard to achieve. I mean there's a balance between theta and convexity, as you know, and it has a very low delta, so very limited directional exposures. That's quite interesting, quite unique positioning, I think. And also, you know, back to the idea of Thailand, of being really fundamentally driven. That's because the strategy really in itself exploits an anomaly in options market that have exploits in anomaly in options market. Uh, that's a very non-anomaly but it still exists and that's also what I think are the best alpha opportunities if there is a phenomenon in markets, financial markets that are there to stay, because on the other side you have market participants which are not, um, you know, which are uneconomical, which is the case here. People are ready to pay for protection more than they're, you know, they're ready to pay more for protection for input, and that creates the volatility skew and the strategy really exploits that and positions the portfolio in a way to also harvest that data. So it's quite interesting.

Speaker 2:

It's interesting also where it came from. So the genesis of the strategy came from the asset manager way it was managed before. They had balanced funds, like a typical larger asset manager for clients having equities bonds and a balanced portfolio. And that strategy was designed originally to act as a diversifier in that, you know, balanced fund. So that makes it, I think, quite, you know, a very good, you know allocation tool for, you know, equity slash, balance portfolios, but also as an absolute return strategy. So I really, you know, I think it's quite unique. And then about the uniqueness so in that environment where obviously it's been quite volatile, the strategy has done really well, performed in line with, you know it's done historically, it's been positive, you know, in challenging days for equity markets. So, like the whole market, neutral, you know, implementation has played its role in this kind of market.

Speaker 1:

Okay, you know yeah. Talk about getting tested right out of the gate.

Speaker 2:

I mean, I can tell you that the strategies were launched on the 17th of July. That was the peak of the S&P. So, yeah, that's exactly that, and we're quite pleased to see how the world played out and diversification played out.

Speaker 1:

Unfortunately and sadly, we've reached the final question. We call this the trend drop. It's a desert island question. So if you could only watch or track one technology or trend in the capital markets right now, what would it be?

Speaker 2:

That's a good one. So I think maybe it's a personal wish on this one also, it's because I can see there's a lot of gain to a lot of improvement to be made in terms of on the operation side of our business. Right, it still requires so much resources in the whole settlement process and, you know, like trying to reconcile trade breaks, so like for me, like a dream, I think that's whether the next potential you know, substantial innovation in transforming the industry is more tokenization. So just to also to be clear on what I mean so tokenization is really converting assets into digital assets using blockchain, and there I feel like a potential.

Speaker 2:

What I see for me, one of the best implementation of that technology is to use blockchain for all the settlements of transactions, like to make all these transactions instant, less effortless, reduce operational risk. You know that's one advantage I see. So obviously it has a huge impact on the industry, right, you know, because there are, you know, so many asset managers, banks they have hundreds of people trying to reconcile all these transactions every day. The second one is, I think, potentially reduce cybercrime, like if that ledger, the transparency of the ledger, makes all these transactions auditable, then all of a sudden you solve one of the big issues in that industry when it comes to KYC and, obviously, being able to trace all the funds, so that also will reduce a lot of operational risk. You know, and for us you know, at the end of the day, you know, like there've been so many, I'm sure there are some technological issues right, and challenges in doing this.

Speaker 2:

It was years ago where you heard, like about all these banks getting together and trying to find, you know, like you know, solutions to find within themselves. It's a system to settle transactions and everything. There's still no solutions out there. Maybe AI will help us to some degree to accelerate that innovation. I would like to see that. I think that's an area where we definitely need improvement, because obviously there's a lot of money wasted in terms of very limited value to solve issues that could be avoided in the first place. So, yeah, that's the one I would monitor. I'm pretty sure it's going to happen. It has to happen and that's where I see a lot of value in blockchain.

Speaker 1:

Well, serge, I want to thank you so much for your time, your insights and, as I said, I would love to have you back next season. And, you know, let's get past this little election thing that's going on in the United States and we'll see where the markets are then.

Speaker 2:

Okay, I appreciate that. I'd be glad to be back. Thank you so much, Jim, for having me.

Speaker 1:

Thanks so much for listening to today's episode and if you're enjoying Trading Tomorrow, navigating trends and capital markets, be sure to like, subscribe and share, and we'll see you next time.