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Ep 38: Learning from DeFi. Would Automated Market Makers Improve Equity Trading?
91 Plays1 month ago
Andreas Park (University of Toronto) and Katya Malinova (McMaster University) discuss their research on how automated market makers (AMMs) from DeFi could revolutionize equity trading. We explore the potential savings, challenges, and future of finance in a digital age.
Check out their paper below:
Learning from DeFi: Would Automated Market Makers Improve Equity Trading?
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Transcript
Introduction to the Podcast and Speakers
00:00:06
Speaker
Hello and welcome to this OWL Explains Hootenanny, our podcast series where you can wise up on blockchain and web3 as we talk to the people seeking to build a better internet. OWL Explains is powered by Avalabs, a blockchain software company and participant in the avalanche ecosystem. My name is Silvia Sanchez, project manager of OWL Explains and with that I'll hand it over to today's amazing speakers.
00:00:33
Speaker
Hi, everybody. It's great to be back for another one of our Owl Explains Hootenannies. Today's episode is a knowledge bomb, because I think that even the title starts off strong. Learning from DeFi, would automated market makers improve equity trading? And well, the title and the episode is based on a paper written by Andreas Park and Katya Malinova. So hi, Andreas. Hi, Katya. It's great to have you guys with us. I'll let you both introduce yourselves.
00:01:01
Speaker
Okay, so hi, i'm I'm Andreas Park, I'm a professor of finance at the University of Toronto, and I'm an economist by training. Hi, I'm Katya Maninova, I'm an associate professor of finance at the Green School of Business at McMaster University, and I hold the returns investments research chair and evidence based investment management there.
00:01:21
Speaker
Interesting combination. No, that's amazing. And also, just to give ah more context to our listeners, Andreas is one of the co-hosts for our CBER series, which is a more in-depth series featuring more leading researchers. And season one dropped last year, and season two is in the works, so stay tuned for that. But back to this particular paper, to this specific episode.
00:01:45
Speaker
This paper dives into stuff like what different types of digital tokens mean, how governments are trying to keep up with this new tech, and pretty much how money works in this digital age. I think it's a really good combination. So I wanted to get started asking you both, what inspired you to to look into this? Why this big focus on automated market makers? like What was the the starting point for for this research project?
Automated Market Makers in Equity Trading
00:02:09
Speaker
So that's actually a really good question. um i'm goingnna I'm going to take a really broad arc on this one because um so just just so you understand a little bit of where we're coming from. so ah Both Katja and I work in a field called market structure or market microstructure. We're asking questions such as how do trading institutions affect market outcomes, price efficiency, trading costs, and so on.
00:02:33
Speaker
and um So we we are part essentially of of two communities, this microstructure community and also of the blockchain research community. And um so one of the key things that happened in late 2022 was that the SEC in the US ah introduced an overhaul of what they call the national market system. That's a set of regulations which kind of determine how trading is organized and arranged in the US.
00:03:00
Speaker
And kind of this is the kind of thing that falls smack on into the research interests of a large number of people in the microstructure community. And among the many suggestions, I made basically four major suggestions, and and one of them really was a bit of a bomb for um most people in the in the space. and and You know, it was also something where people were just literally shaking their heads. So we we run, for instance, a webinar series on microstructure. And when these suggestions came out, we had sort of like open open discussions after the session and people just go, what what are they doing here? And so specifically what the ACC essentially did was is they came up with a new way of how retail order for should be traded.
00:03:44
Speaker
ah So they essentially created or invented a market structure for forcing a particular way how retail orders should be treated. And, you know, so so we're looking at this and say, well, how, how ah but why would a regulator if actually come up with such specific prescriptions of what has to be done?
00:04:06
Speaker
And then in the broader context then, because Katja and I have been working on on on work in microstructure and on market automated market makers and come to the details there, um we thought, well, if you really wanted to push it and change market structure in such a significant way, and the way our trading is organized is, what would actually happen if we would apply this new invention from the blockchain space automated market makers broadly to equity markets?
00:04:34
Speaker
So it's kind of a bit of a bold question to say, let's you know just throw everything out and let's restart in you and just allow this one form of trading. Would this be better or worse? So and that's kind of how we set up and set out. And we then try to write a paper about this, try to figure try to think it through. What would this actually mean? How would this work? And what would be the outcome, at least based on the data that we that we have available?
00:04:59
Speaker
I love that. That sounds amazing. That's, I think, a really good explanation into and to how all of this came to be. And also something that we like to do, just to give some context to to our listeners, because some people, we have listeners from different a levels and industries. So just to make sure that we all have clarity on these terms that we're going to be referring to a several times throughout the podcast.
Understanding Automated Market Makers
00:05:24
Speaker
Can you explain in simple terms what an automated market maker, AMM, is and how it differs from traditional equity trading methods? Let me see. that um So generally speaking, um there's many different ways how and equity or trading generally of assets has been organized additionally and over over time, right? So there's been, you know, people sell assets in auctions,
00:05:49
Speaker
ah There are, you know, one-on-one negotiations. There are public outcry markets, you know, the ones that we sometimes see in older movies like Trading Places. um There is the model of that was prevalent in the New York Stock Exchange ah for for, you know, over a century essentially where all the trades would go through a specialist, so a single entity that would ah post ah post prices and people would trade with that specialist.
00:06:16
Speaker
And then, of course, has been the recent development of electronic markets which are organized in what's formerly called a limit order book. So a limit order book is you send orders to a trading system with something called a matching engine. This matching engine It takes all orders where the a buyer is willing to pay more than a seller is willing to sell it for, and then all orders that can't be traded right away are put into an order book and then stacked by price, right? And so the difference between the best price at which you can ah sell an asset for and the the best price at which you can buy an asset, that gap is referred to as the bid aspect.
00:06:55
Speaker
um And that's kind of the the standard way how most equities are traded these days, but it's not the way how stocks are traded all over the world. There's not stocks, securities are traded all over. There's many different ways. um And now in comes the blockchain world. And in the blockchain world, um there are some difficulties if you want to actually organize trading. So if you take a step back, and you know when you think about a blockchain, a blockchain allows the transfer of of digital value, ah in particular of assets, and therefore the trading of these assets in some organized fashion would be a first order application that you need. but So you know if you want to actually use these value management systems, you kind of need the trading system.
00:07:39
Speaker
And most when the blockchain world started, most of the trading actually didn't happen on the blockchain, they happened in separate systems like on Coinbase or on Binance and the like. And that's kind of weird, right? So you use, you have these digital assets that live on this special infrastructure where you can exchange them one-to-one, so from person to person. And yet when you want to trade them, they all have to happen on, you know, essentially on an on an outside market. and So this sounds weird. um But in came the DeFi space, um developments in the Ethereum space where you can, ah where people came up with an idea of how you can trade directly on a blockchain.
00:08:20
Speaker
And so that's actually a phenomenal idea. So you can use the ability of blockchains to um process code or to ah to you know do value changes and you do the trading on a blockchain. So now that sounds ah you know something you can do. um Now let's let's think about traditional trading again, you know a limit orders. A limit order effectively is just a set of instructions. And in principle, the set of instructions could also be executed on a blockchain.
00:08:51
Speaker
And there were actually trading systems early on, so one for instance called Ether Delta, where you would register a ah trade or an an order as a smart contract and the system would sort of fetch these existing orders from the blockchain, build an order book and allow you to do the trading. But that's terribly inefficient, right? Because, you know, like the Ethereum blockchain has like 100,000 different nodes and they would all have to process each of these orders, store all of these orders and have them available for for processing.
00:09:20
Speaker
um So it's not efficient, it's is's really a terrible way how to organize trading.
Liquidity Pools and Economic Model Analysis
00:09:24
Speaker
And so then ah came the invention, essentially I think the first one was really Uniswap to do it, where you organize trading by liquidity pools.
00:09:35
Speaker
and so Maybe, sorry, I'm monologuing here for a very long time, but let's let let me just explain basically how an automated market maker works per se. So you take two assets, right, as ah as ah as somebody wants to provide liquidity, you make two assets available for trading. Let's say um ah a cash token, a stable coin, and let's say the Ethereum token, formerly would be a wrapped version of it, but let's not get into the details. And you put them into a liquidity pool.
00:10:06
Speaker
So think of this as ah is ah as a big pot, right? And so you make a contribution to this pot. Everybody throws it into a big pot. So multiple people make their contribution to this pool. um And then somebody who wants to trade against this liquidity pool, who wants to make a trade, sends one type of token into this pot, and then is allowed to withdraw another type of to and another amount of token of the other token based on a particular set of rules.
00:10:32
Speaker
And that's fundamentally what a liquidity pool is and that's what an automated market maker is. Somehow the term automated market is a bit of a misnomer because it sort of suggests that there are market makers or persons doing something. It really is a liquidity pool that you trade against.
00:10:49
Speaker
I'd like to add to the misnomer part that when you hear, when your background is the market structure and you hear another market maker, you the mind comes to a trader, maybe a high-frequency trader. And so in this case, it's really a trading mechanism and other market maker. It's not a participant. It has a slightly different meaning to what people in traditional markets come to think about when they think about market makers.
00:11:18
Speaker
but yeah, it's a critical trading mechanism. which I felt coming from my market structure was very fascinating to me too. Why we got into this, that it's a new trading mechanism that was developed ah for a particular set of reasons, is something as I've explained on the blockchain, because it's costly to send messages around. But it's brand new that was never used before in equity markets, and it's just interesting to see what happens if you put it in equity markets. Yeah, and and so you know to add to this,
00:11:52
Speaker
So the really interesting feature of this, if if you think of any other market where people trade with one another, um you know, an order, so like a limit order market, you sent an order to buy or you sent an order to sell. um So you on one side of the market with with your actions, you can choose to be on both sides of the market, but that requires separate interactions to do. Now in an automated market maker,
00:12:20
Speaker
You dump your assets in the pool and you are on both sides of the market. So you're buying and selling um as as a member of the pool for as long as you choose to stay there. Then it really is, you know, conceptually a very different way how we think about how people would would trade and how people would provide liquidity means that make the assets available for others to trade.
00:12:43
Speaker
Got it. Thank you both for that for that great explanation. I think that's a really good foundation for what these concepts mean. And also you've covered the concept of efficiency and you you know the the role and the significance of liquidity providers and demanders in the context of AMMMs. And now I'd like to switch gears a little bit because your findings in the paper suggest that AMMs could lead to significant aggregate savings. So Could you elaborate on how these savings are calculated as well as their potential implications?
00:13:15
Speaker
so it's kind of um so it's So first, let me just say this. right So a paper like this is not very standard. right So normally in economics, what we do is we take ah a system, an invention of what people are doing, and then we make an efficiency analysis based on it. um We don't really usually invent some new stuff. That's kind of not our prerogative. um And so in this sense, this this this paper was a little different. um But then if you if you think about it, so if you want to design a new market,
00:13:45
Speaker
meaning that you want to create a world in which people can trade a particular asset in a particular manner. So how do you actually think about this? Well, so the first step is that you actually have to build a model. So what Katja and I did was is we we thought, you know and this and when you think economic model, you always think if you if you have listened ever to economists, we have all kinds of behavioral assumptions and auto, you know and and there's utility maximization and all that.
00:14:11
Speaker
And in many ways, we actually don't really do that so much, right? So it's it's kind of very bare bones, which is powerful, by the way. So this is a good thing. It's not a bad thing. And so we basically set out to say, first off, okay, so you're a liquidity provider. ah So it means somebody who's willing to make their assets available for trading.
00:14:28
Speaker
So what are you going to get for that? what what What are the costs and what are the benefits of that explanation, of of that of that ah setup? And then we took the perspective of the liquidity demander who can trade against the assets of the other side and say, okay, so what are their incentives? How would they interact? And so kind of we we put this together in what we refer to as an equilibrium model. And you know and and then we we determined what the behavior was.
00:14:56
Speaker
And now there's one variable which is kind of key to this whole process, which is ah fees that have to be paid by the liquidity demander for trading or using the liquidity provided by the other side. right So there's sort of there has to be some form of transfer, but is kind of which is common, of course, in in all markets where liquidity provider always has to be paid a certain fee.
00:15:18
Speaker
And so what we did is we took this little screw and we set it in such a way that it is set in a manner which would be optimal for the liquidity ah for the liquidity demand.
00:15:30
Speaker
um And this is something which would be neutral. ah So this is a little difficult to explain you know without going into very wonky details. But the the key idea is that this fee is set in such a way that It is optimal for the liquidity monitor and it's neutral for the liquidity provider. and So they are indifferent between different fees in the sense of that the reaction is always, they would always provide liquidity in such a manner that they act equivalently.
00:16:00
Speaker
And so based on that analysis, so based on a theoretical model, we then ah calibrated this model to the data. Data meaning we looked at, um what did we do? I think we started 2014 to 2022. This is the data that we had access to. We took the high frequency data for US equities. So this is for all stocks that trade on US equity markets. And then we determined, based on the observational features of this data, like the trading costs, ah the returns, the trading volume, we determined what the optimal fee would be.
00:16:35
Speaker
um And then we determined, based on the trading patterns that were there, what would this mean for the fee for the costs that liquidity demanders face in a market which would be optimized, which has some optimal fee and has, you know, behavioral assumptions from liquidity providers and liquidity demanders. So, in other words, to make a very long story short, we simply compared to in this market that we,
00:17:05
Speaker
you know, basically made up ourselves, what would be the cost that liquidity demanders have and how do they compare to the costs that they actually have to pay in real markets. And when we compare the two, and now I'm leaving this to Katja to say, what is the what is the big reveal here?
Potential Savings and Liquidity Enhancements with AMMs
00:17:21
Speaker
Well, when we compare the two, we find that it is indeed cheaper for the liquidity demanders to trade in the system that there would be automated market system in equity markets. And if the fees that divide optimally, the savings are actually quite large. ah We find that on average, it's cheaper on 94% of days. It's cheaper to trade in an automated market maker than in a limited book for equity demanders. And the average savings are on the order of 16 basis points i per day, which um if you translate it into a dollar terms, it's about $9,500.
00:18:11
Speaker
$9,500 per day on average per per stock, about $2.4 million per year. And I guess if you convert basis points to an annual savings, it it accounts to about over 30%, 20% per year. So it's not a small amount.
00:18:36
Speaker
Yeah, and and so ah you know if you if you really want to be wonky in the paper, we actually tried various different approaches. So Katja just cited the ah most conservative approach um in terms of the estimation. We actually, we actually you know if you if you would do this in real life, you probably would use a different approach, which is a little more generous, and then the savings could be even higher. It could be on the order of 30% of transactions cost basically could be saved.
00:19:03
Speaker
And that adds up to, I don't know, something like $15 billion for US equity investors a year. So pretty significant numbers, obviously in a science fiction environment. right but um so But what we're trying to say here is is there's actually a lot of there's a lot of room for improvement, if you want, in in regular markets um for you know for trading and trading costs.
00:19:27
Speaker
This sense fiction, on the one hand, on the other hand, this sense fiction works very well in crypto markets, so there's no reason to think that it won't work in equity markets. So it's not a sense fiction, and it's not a mechanism that doesn't work elsewhere. It's actually a mechanism that is but has been working for quite a while. so Yeah, and and and so you know in a may in a way also, so when we started with this, we kind of thought,
00:19:56
Speaker
and Maybe this is actually something which would be useful for for small stocks. and I'll explain in a second why, but but the but the savings are really across the board. It's more large. it's just It doesn't really matter where it is. it's just it's just universally ah You can describe it as a universally good thing. the In basis points, the savings would be larger for smaller stocks um simply because trading costs are already quite low for like Tesla and the like.
00:20:23
Speaker
right um But in relative terms, it's it's it's actually still pretty much the same across the board. And maybe Sylvia, it's useful if we give a little bit also of an idea of where really the difference is, ah because it may be actually something useful in particular to understand for for small issues, would that be okay? Sure, go ahead. I think that's perfect.
00:20:47
Speaker
ah so I mean, again, bigger picture, thinking here is this. is So, I mean, we're from Canada, right? And in Canada, we have something like 3,000 stocks listed on on the different exchanges in Canada, which is a huge number, has to do often with, we have a lot of mining, small mining companies, right? um but ah But the issue here is this, it's like, of these 3,000 securities, or 3,000 stocks,
00:21:13
Speaker
only about ah several hundred like two three hundred or so trade regularly the rest doesn't really ever trade and that creates a lot of frustration among the issuer so the firms that issue the stocks because you know being listed is quite costly you have listing requirements with the exchanges you have regulatory requirements and You know, then you do all of this, you try to raise cash and you try try to raise funding you via stock exchanges and then you're public and you kind of don't really benefit from all of the purported benefits of a public market and all the activity concentrates in just a few issues. And so why is that? Well, there's multiple explanations, but one of the key ones is for institutional investors to to invest in a small issue is is quite difficult because
00:22:02
Speaker
You kind of need, ah you know, you need to have the ability to to trade out of a position when the time comes. And, you know, to trade out of the position, you need liquidity in the markets. But if there's no trading in the markets, then you can't trade out and therefore you can't invest in the first place. And so sort of like it's a chicken and egg problem for many issuers, but also for institutions to invest in smaller issues. And so an AMM,
00:22:27
Speaker
may actually be um you know a way to to improve the situation. And here's how. So in an AMM, you as an owner of a stock can make your assets directly available for trading. but So you you put them in the liquidity pool, and sure, there is there is some risk involved, but you also receive possibly an income stream from trading fees that you collect from other parties right that trade against you.
00:22:52
Speaker
And in doing so, you kind of create the opposite of the tragedy of the commons. So tragedy of common means basically everybody wants to use a public space, but nobody wants to contribute to it. And here it's kind of like everybody wants to have liquidity, but nobody wants to contribute to it because in a normal market, this is quite difficult. But with an AMM, it's quite trivial to do. And so you kind of can solve this conundrum of liquidity provision by having an AMM because it's such a simple system to be used.
00:23:22
Speaker
And so this is also kind of why how we think an AMM is so fundamentally different to an existing market because existing markets rely often on intermediaries that are able to facilitate trades. And um and they do this so because you know at any given point in time, there may not be a buyer for somebody wants to sell a security or vice versa. right You need this coincidence of wants all the time.
00:23:49
Speaker
And, you know, so you need people to come together and intermediaries basically bridge the the bridge or solve that problem. And then in an AMM, this can be solved by existing investors, which is kind of a really cool feature. And it's something which to an economist is very, very appealing.
00:24:06
Speaker
and This is where one of the sources of savings we believe is coming from, because an intermediary is there to provide liquidity and to bridge this gap, but they charge something for the liquidity provision or they extract rents for the liquidity provision.
00:24:24
Speaker
And if you're a national investor that just deposits assets in the pool, yeah and so one reason for the rents comes that an intermediary doesn't want to hold a position in an asset. So anytime they have a position in an asset, it's costly to them and they would like to be compensated for the cost of having that position in an asset. Whereas as an investor, you desire the exposure to the asset, particularly um overnight when most of the risks come. And so you don't aim to end the day with no efforts whatsoever, and um you don't try to charge investors for the so-called inventory costs.
00:25:06
Speaker
And because of that, it's possibly cheaper to trade in in the near moment. One thing to add to for for the investors that Antennares has dumped their shares to the liquidity pool, one question is why why would you do that? And and there is already evidence in in the existing markets where similar things happen, for instance, for securities lending for short-term.
00:25:30
Speaker
So this would be kind of a similar concept where you let your securities to be traded in an EMA, and then at the end of the day, you you get them back with some fees, hopefully. Yeah, I know. So that just the last thing to add maybe to this is, so one of the things, so if we take just a very, if I may get a little wonky again, right? So um if you think of a normal trading mechanism, when when somebody posts an order, um so they're exposing themselves to a particular risk, right? So the the risk is that somebody may trade against their order and and take advantage of a mispricing that the you know there there could be an arbitrage opportunity or the
00:26:08
Speaker
The fundamental value of the stock has moved and then the person who has put the order out there is at risk. But the person is at risk by themselves, right? So they are the one that taking the risks. Now in an AMM, if you if you want to be really broad here is a lot of people share this risk together, right? So it is not an individual who is hit by a particular movement. It's it's actually shared among a large community of liquidity providers.
00:26:36
Speaker
And so kind of for an economist, the ability to share risks in an economy is really quite critical. This is why financial markets are so useful is we we collectively, as individuals, share risk. And an AMM is, again, is a mechanism that actually facilitates that among a large number of and of of providers of liquidity. and And that is usually a good thing. um And that is one of the reasons also why I think why trading could be cheaper.
00:27:02
Speaker
Of course. and And those are great points. And back to to what you were both mentioning earlier, everything definitely adds up. The savings are are noticeable and you've covered several things, different approaches. And I'm curious about what challenges or limitations you encountered while conducting this research. How did you address some of those things that that inevitably came up?
Challenges in Designing and Implementing AMMs
00:27:24
Speaker
Well, so you have to be... So when you think about the design of this, you have to be um rather careful. um so um So let me let me take say one problem that um that would could arise or that that you have to think through, which is that of 24-hour trading. and So normally we think of blockchains are available 24 hours a day, 365 days a year and so on and so forth.
00:27:49
Speaker
And when you talk to people and in computer computer science and engineering, they say, this is great, right? So you know you can trade anytime you want. That's awesome. But um if you think of this as ah from the perspective of somebody who's worried about risk, um about announcements that could occur. So like a firm, if you take a tokenized asset like a stock, there's usually have to be announcements made ah that are material to the value of the company. like you know, earnings or a takeover or the like. And we have a mechanism in place to to to take care of these. um Why do we have this mechanism in place? Well, because you want to make sure that, you know, nobody is taken off guard by an announcement that is being made. That that there is a certain level of fairness of how how you can react um when when an earnings announcement happens. Everybody is sort of like operates on the level playing field.
00:28:46
Speaker
And for that reason, when there is great uncertainty about an asset is either the announcements are made in after hours when markets are closed or there is a trading halt. But when you have a system which is open,
00:28:58
Speaker
24 hours, seven days a week, 365 days, that's actually quite difficult. And so in designing this market, one of the things that we then assume is, well, you know, yeah we only have it operating during trading hours. um Then a second thing that one has to be a little cautious about, and that's also, that's not a small thing, is um AMMs are actually, and this is known, not very capital efficient.
00:29:26
Speaker
and So what do I mean with this is then if you put your your, you make assets and cash available for trading, these assets and trade cash can be traded over an arbitrary price rise or fall. So the price can go anywhere from zero to infinity. um And that kind of is ah so you need to make the liquidity available for that. And And what that in turn means is you also face a very large risk of of prices moving. ah In reality, that's not the case. In reality, um and we have something called single stock circuit breaker. So in equity markets, if the price of a stock goes up or down by more than 10% in a given day or over a certain time horizon, a circuit breaker is hit. um And then there is sort of like a reset after the fact.
00:30:17
Speaker
and One of the things that it does, it protects liquidity providers by tape by being taken advantage of, so that orders that are sort of in the book that and the people got could be caught off from, ah could be off guard from, are not hit. And so if you put something like a circuit breaker into these markets, you could actually use capital much more efficiently. So for the same level of ah capital available you can have actually much better liquidity or for the prices that you want to support you actually need less capital one way or another how you want to put it. So we had to think about those problems. Now it gets very wonky though very quickly.
00:30:56
Speaker
Well, so the single-stock circuit breakers, a they ah provide some protection from extreme price movements, but it solves the problem with addressed talks about capital, because in an AMM you'd have to put – so in traditional stock market trading, and we already think about contributing securities to trading, but here you would have to contribute both securities and cash on the other side, so so that people can both buy and sell in an AMM.
00:31:25
Speaker
um that so and ah cash for security assistance. sit ugly at brokerages and they could could be contributed. Whereas cash is very costly because if you don't have it, you'll have to borrow it and with the high interest rates, the cost is quite substantial. But with the single circle breakers, you only need to contribute enough to to satisfy the plus minus 10% or particularly minus. And that's about 5% of the assets contributed. So so that's that
00:31:58
Speaker
something that would resolve the supply of cash problem as well, not just they not just the risk. But that's something that I've had in cash on on to to match the efforts. It's potentially a problem for equity markets, so we're looking at other designs as well, so the same using the same cash, of course, for for multiple assets as we started in the blockchain environment. um Now, um I want to say one thing, though, maybe that's quite useful.
00:32:26
Speaker
so This is really science fiction and there is actually a lot of problems that we can immediately see that would actually arise from a legal implementation of all of this. Do you want me to talk about that a little bit, Silvia? Yes, go ahead Andres.
00:32:41
Speaker
Yeah, so because, so I mean, what has to be realistic, right? So we can come up with something which could work really quite well, and which is sort of like a cool idea, but um in practice, and and by the way, it's actually ah good good to know that you you don't actually need a blockchain to implement this. um I mean, know there there would be certain operational advantages to be doing it. It's not trivial to do because and we actually tried, right? So i've I've been working with engineers about how to implement this.
00:33:07
Speaker
if not yet done it. um but um But there's also legal problems quite easily. right So for instance, if you're a mutual fund manager, and you know I basically just brushed over this a little bit, um you know um saying, well yeah, you can deposit your assets and institutional investors would be a useful way for them to do, but it's actually not a trivial problem because you know you make an investment and you're kind of responsible for your for your for your assets um and for your investors' assets. um And so if you put them into an AMM, theres there may be questions around this and there's lots of legal questions that would have to be answered on that if you can actually do it, for instance. That tax reasons, um you know, if you, if you' and again, if you're an institutional investor and you make the assets available for trading, there's tax implications for every single trade that happens and they could add up and that could be a problem possibly
00:34:02
Speaker
which has to be included in the fees and which has to be included in in other considerations. So um what I'm trying to say it with this is that this is sort of like a starting point to think about a problem, but if you really wanted to do this, you kind of need to do a lot more work than what we came up with here.
00:34:19
Speaker
Oh, wow. That that definitely sounds sounds like a bigger task. And I think that this is like trying to fit a novel into a short movie, into you know like a big saga into just two hours or something. Because I feel like as you just cover one part, then there is another part of it. It's like the spider web effect. and Obviously, it's not 100% possible to to fit it into the
Conclusion and Further Resources
00:34:43
Speaker
podcast. So we're running out of time, and there's still a lot of details that are covered more thoroughly in the paper, which is why I'd like to invite our listeners to to check it out. You can find the link to it in the episode description, as well as in the Owl Explains website. We will be publicizing this paper for you to just get a deeper dive into it, even though I think that
00:35:03
Speaker
These 30-35 minutes were a great yeah great starting point, a great and great summary into this. so Once again, thank you Andreas, thank you Katya for for being with us today, and also thank you to our listeners for tuning in. Until next time.
00:35:21
Speaker
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