Jeremy Maletz

Hi, I'm Kelly Cavaniara, Managing Director, Head of North America, Institutional Distribution. At Janice Henderson Investors, we believe working together is the way to work better, like combining your portfolio plans and our in-depth strategy, your valued assets, and our valuable insights, your mission, and our vision, working in harmony to seek the right investment opportunities. Janice Henderson Investors, investing in a brighter future, together. Some people treat Chatchy PT like some kind of smart search engine, and some use it to get work done. Chatchy PT work is a new way of working in Chatchy PT that can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work. It's designed to help you move from a chaotic starting point to a reviewable first version. So all the source materials, briefs, and scattered information that you have to grind through to turn into something useful, can just become something useful. 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Generally, he goes a little better on business cards, if you say head of prediction markets, but Goat Markets works too. I've been head of macro for a long time at Sussquahana, and in a lot of ways, the prediction markets kind of grew out of that. Because as head of macro, we had quite a bit of focus around elections. During election years, we got involved in trading the election over in Europe, where you could trade their prediction market style contracts on, say, Betfair. And we would also use some of the products that exist, say, like predict it, even if you can't trade on it for informational purposes. So I'd known about it for a long time. The macro kind of transitioned into the prediction markets, which started out as a side project, and now is a very large business for us. My model is that like, there are all these prop trading firms that like had 23-year-old interns who got into crypto as a hobby, and then that became like their crypto business. And it might be natural to assume that many prop trading firms also get into prediction markets as a hobby because of sports betting. Is there any of that, and so it's 200, or is it strictly like, oh, it's election markets? So when we first started the prediction market journey, sports weren't a part of predictions in that way. But that being said, there's quite a lot of overlap. And there are certainly a lot of people that are passionate about sports. Myself included the other head of prediction markets. We really got to know each other through sports as some of the draft king style places became more prevalent to try and understand some of the opportunities and just being passionate about, you know, all forms of prediction. And so I wouldn't say that it was necessarily directly connected, like the genesis of our business came from the macro angle. But it's kind of hard to disconnect them because, you know, people at Susquehanna are just very into predicting anything that we can. And there's certainly very large markets around sports betting in this world. When you think about like your overall book of business in the prediction markets, I'm going to ask you the split of sports and non-sports. There's obviously different ways to kind of measure it. Certainly sports is more than half, you know, it's probably sitting somewhere in the, you know, it's not 90%, but it kind of fluctuates by season two. Like it's probably more now with football going on. It's a little less in the summer. Sons world cup. But certainly sports is, you know, a very large part of the predictions business overall. I think there's some good reasons why that's the case. The biggest ones being that there was already a market for sports through all the regulated operators in certain states that existed. And this huge amount of sort of latent demand that sort of found a product that's a better product in a lot of ways. The other stuff is new. So it's growing. So I can tell you that the other non-sports businesses are actually growing faster than the sports business. But the sports business is obviously coming from a much higher overall level. Okay. So here's how I think about your business and tell me if this is wrong. I think there are being like three components. There's classic like flow market making where you are making markets on sports elections, some third category. And you're, you know, quoting a bit in an ask and trading with let's say retail customers and hoping to earn the spread. There's parlays, which I don't know how big of your business that is. But I think it's really interesting that like traditional sports makes make a lot of money on parlays. And to compete with that, Kalshi has to offer parlays, she called like combos or whatever. And I gather that you're in that business. It just seems like interestingly different from the classic market making business. And then the third thing is what I started talking about, which is the goat hedging, which is like you have a block trade OTC-ish business where you will like do bespoke hedges for corporate and institutional clients on prediction market events. Like are those kind of the three areas? Kind of. I would probably lump the first two together. There are different market structures to market make. But the fundamental principles are the same in that it's still flow based. We're responding in a different mechanism. But we have our model, we have our pricing, it's largely automated. Like they function the same from kind of a principle's perspective, even if the. They're interestingly different to me, but I can see why from your business that they're both slow automated trading. Exactly. The way that we actually handle some, like the details are different. But like philosophically, I think they're kind of the same. And the main thing being that like everything is happening electronically. And you know, it's in sort of this all-to-all screen type ecosystem. The block business, that institutional business is meaningfully different. And I do believe that's the next phase. And it's where a lot of my personal time is going into now. What's this split there? Like I assume the volume is much bigger in the flow business. Much bigger. I assume you are charging wider spreads on the OTC-ish business. Generally, although spread is a little bit of a weirder concept in that OTC business. Because you're not. Because most of those are positions that we're going to hold for the entire day. It's almost a little bit of a hybrid in some ways between sort of financial markets and insurance. And we have to do underwriting. We use the prediction markets to help with that price discovery and that underwriting. So it's a valuable input to help us to price these trades. But the actual market that exists there is not as relevant outside of the price. Like whether there's $1,000 of liquidity or $50,000 of liquidity on the screen, it doesn't make a difference if the customer wants to do $50 million of size. And so what we've really been setting up is to try to take the really big risks on and risks that the current insurance industry doesn't cover to basically sort of bridge those gaps in a lot of the emergent risks. Okay, so that's how about a good edge. I remember it was like in August, there's stories about this guy. Rones are goat hurting business in California. Yes, in California law basically was looks like it was going to require him to pay minimum wage to his goat herders 24 hours a day. And that would have been bad for business. And he like wanted to draw attention to his plate, I guess. And we're actually unsure against it. And so he found an insurance broker who found you or found a sales cojana. And he ended up by a contract where he paid $50,000 for $500,000 of coverage against the risk that California didn't do something to change this law. And that trade printed to Kelsey. Like, you did a, I say, OTC like over the counter, but you did a block trade on cash. You did like a bilateral trade with him that instead of just like signing a contract and putting in a drawer, it printed to Kelsey. Kelsey like published the terms of the bat basically, which is like fairly complicated terms. It's like if like any of these 12 things happened that would relieve his pain, then the bad paid off for you. And if none of them happened, then he got his $500,000. And that printed to Kelsey, then anyone who wanted to could trade it, which I'm very curious who traded it. But we'll play with that later. So you've talked about like when people come to you for $50 million trade or whatever, like you can look at Kelsey prices. And even though you're not like, you know, just laying off the risk immediately, you'd have a sense of what the market implied probability is. But here there was no market implied probability because the contract didn't exist before you did the block trade. How'd you price the go to risk? So we had a guy that got on the phone and talked to a lot of people. And he literally talked to go herders. Sure. He talked to lobbyists. He talked to all the people who would have an opinion plus some of our other internal research. We have our own research. We basically canvassed as much information as we put possibly could, to come up with an opinion on what we thought the fair probability was. - Did you have pre-existing code research? - We did not have pre-existing code research, but we certainly have a lot of kind of political research. In general, we are in the business of trying to price random things, so we have networks that we understand where to go to to figure out how to price random things. - Hi, I'm Kelly Cavaniara, managing director, head of North America, institutional distribution. At Janice Henderson Investors, we believe working together is the way to work better, like combining your portfolio plans and our in-depth strategy, your valued assets, and our valuable insights, your mission and our vision, working in harmony to seek the right investment opportunities. So all the source materials, briefs, and scattered information that you have to grind through to turn into something useful can just become something useful. - This is Jacob Goldstein from What's Your Problem. Running a business is hard enough. Don't make it harder with a dozen apps that don't talk to each other. One for sales, another for inventory, a separate one for accounting. That's software overload. 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Double your emergency food supply at no extra cost. Plus, seniors and veterans get free shipping. Go to fourpatriots.com right now. Remember, when disaster strikes, supermarket shelves empty fast. Don't let your family go hungry. Fourpatriots.com before the steel disappears forever. (upbeat music) What is the history of Susquehanna being in the business of knowing how to price random things? Is that like, because you've got an inter-production markets? Or is that like, from the time Jeff Yass was a baby, he was taking any bets that people came to him with? - I mean, I think that is probably true. - Yeah. - I don't know, baby, but like six maybe? - Right, right. - Yeah, no, I think that it's always been very much in our culture. I mean, I came to Susquehanna through a recruiting poker tournament 20 years ago. He had some questions earlier about kind of like, market-making, I think there's kind of different forms of flow market-making. Some that are kind of trying to strictly balance and they're using all the different flow signals. And some that are trying to say, we're gonna try to come up with a fair price on this and use all the information we have to constantly come up with a fair price and then take the risk wherever it is. And we've always been very much in the latter category. So it's much more about, we're trying to figure out what's fair, as opposed to, we're just trying to balance the order flow, obviously you do both. But I think that mentality has been in our culture of like, try to price a thing, figure out what's fair. And we have a lot of random businesses at Susquehanna that are pricing random things. We're in the insurance business, like, you know, the official non-predictions insurance business as well. And a lot of that has been pricing random risks that people have. The prediction's angle helps us to take that to the next level, but it's always something that we've done. - So like, why went like the good guy shut up? Why couldn't you be like, go see the insurance people? What makes something an insurance product versus a prediction market, it's block trade? - Insurance in general is very good at understanding. We have this large data set of things that have happened in the past. We know how to pool all this risk. We've got great procedures, et cetera. So you're looking for a standard property product. They're going to totally understand how to do that. But on the business of, here's some new thing that just happened and we're going to figure it out. Like, that's just not really how insurance works. - Maybe even at Susquehanna? - Well, at Susquehanna, that's how it works. Well, we are kind of in the insurance business sort of, but I wouldn't say we have like an insurance ethos. - Okay. I'm going to say ethos. I'm saying like the product, right? Like, you have an ethos that is product-regnostic. We'll take a bet, right? And then the question is, is the bet expressed as a calcium block trade or as a contract called insurance? - Oh. - Is it just like, you get better capital treatment for the, like why print this to calcium rather than to the insurer? - Oh, so that is a good question. You know, there are kind of three mechanisms really for which we can take on one of these trades right now. We can do a contract that's printed to a prediction market. We can do a swap and we can do a regulated insurance product. There are kind of advantages and disadvantages to all of them. If you print it on an exchange, you have the most transparency. It's on exchange. It's cleared. All of those things are valuable to certain people. You do it as a swap. Then it has more favorable capital treatment sometimes, but it's not on exchange. We might link it to an exchange. You do it as an insurance product. There are some advantages sometimes to being regulated insurance product. There's also some fees and some other drawbacks. So our goal when we talk to one of these people isn't really to push them in one direction or another. Our goal is to understand what makes the most sense for them. There are some players out there that say, we really want to do this on an exchange. It's important to us. There are some that say we aren't anywhere near close to being able to connect to one of these exchanges. We've got a million things. We got to figure out on our compliance side, but we want you to take this risk. Can you do it in a different way? And our goal is just to figure out how to take the risk. We're kind of execution platform agnostic. I assume if you face a hedge fund, they want to do a swap. And I assume if you face a gold herder, you probably can't do a swap. Like these probably regulatory and not allowed to do. That's oftentimes more true, although there are exceptions in terms of how some of this goes. But yeah, for someone like a gold herder, it's way easier to do this on an exchange, for sure. So talking more about your hedge fund you want to do a bet, you come to you, you're like, hey, I want to do a bet. You write up the bet. You have yourself and good lawyers. The hedge fund has their trader and good lawyers. You can like hash out the payoff mechanism of the bet. The gold herder bet is like a tri-party bet. There's you, there's the gold herder, and you have lawyers, and maybe he has lawyers. And then there's Kalshi. The contract that you enter into is not like you sign it and the gold herder signs it. It's like you buy and sell a product on the exchange that is like listed, approved, run by the CFTC by Kalshi. So how does that work? Like who writes that contract and who has input into that contract? - So ultimately in that scenario, Kalshi writes the contract, but it's a disadvantage. - But you wouldn't trade it with a gold herder unless you were both happy with the terms. - Right, right, so they ultimately are the ones that sign off on it, it's on their exchange. But we have meaningful input, right? We really do all work together. Like we are all discussing what the different terms are. And at the end of the day, everyone's kind of incentivized to get terms that cover the actual risk, right? Like we want to cover the gold herders risk. They want to cover their risk. Kalshi understands that like this market is in many ways being created to help facilitate this specific risk here, this trade. And that's kind of part of the beauty of prediction markets is how easy it is to create these new markets now. Like the fact that it can be done in the form of a day. So we're all kind of incentivized to work together. So I would say it's a collaborative process. Like Kalshi has a lot of experience writing these contracts. We have a lot of experience doing it as well. The gold herder may be a little bit less, but he knows what his risk is. - He knows the risk, brother, better than either here. - Better than anybody. - Intuitively, having worked at a bank. If you're doing like it is through with a hedge fund, you'd probably say to me that everyone wants it to cover all the risks and be fair. But like, you know, you're like, you know, getting a little edge in in the contract, right? And like they're trying to get a little edge in in the contract. Like Kalshi, I think, is like a genuine incentive to like, if something happened where the gold herder was disappointed, where like he didn't get the path, things didn't work out for him politically. And also your contract didn't pay out. Like that would kind of be a windfall for you, right? And vice versa, like if his contract paid out and he got the thing you wanted, that'd be a windfall for him. But like for Kalshi, they want like the right results, in either case, I think, right? Because they're sort of reputationally trying to build this business. So is it like helpful to the business of like a neutral arridor like during the contract and the determination of the payoff? - So I guess what I would say is, first of all, I don't think that that sort of haggling over terms and trying to win that has been my experience in this so far. Like we are also trying to build the business here. And we actually don't want a situation where we win, but the other side, like, didn't get covered on their risk. Have you ever. Like retroactively, have you ever written a contract where like in hindsight the other side didn't get covered and you're like, "Yeah, I would just pay you anyway." That specific scenario hasn't happened. We've been involved in some contracts where we might have realized like there's some form of an edge case where theoretically something could happen. But we've never actually had that edge case happen in a situation that's sort of a relationship type business where, you know, we've had a relationship with the other side. There are edge cases in sort of the general screen markets that happen here and there every once in a while. We're making our money on the price at the end of the day. We're charging a little bit of edge on the price. We don't want to make our money on the contract terms. Like the worst thing that can happen is we don't want the next story about the go-herter to be like, "Oh, he lost his head and he lost his business." That's horrible, ultimately. And you mentioned in your article, which is fair. Like part of the reason we wanted to do this trade was because we think there's some degree of edge in the trade, although to be honest, we priced this one pretty fair. A large reason was because we think this is an important business for predictions overall. And we wanted more proof of concept out there. We want people to see this is what can be done. We'll win in the long run if this business takes off. And our role right now is really to bootstrap the liquidity of this business and to show what's possible. Okay, so when I read about this trade, it's probably, I don't know, a week after you printed it or something like that. And I looked at the screen for this contract, which, you know, you printed a $500,000 national trade with this go-herter. And when I looked at the screen for the Calche contract, it had something like $500,476 of volume. So I think that's your trade. Plus, like somebody or some people who found this trade, after I wrote about it, and like, you know, nine other articles written about it, like I think that picked up. And when I looked this morning, there was a total of $655,000 of volume, like contract resolved. You won, which means he also won. When I wrote about it, it had traded tiny, tiny, tiny size and like the 80s, which means it traded down from the price you paid, which means that you looked a little like you got picked up. But the go-herter, which I think I said to you, but in the long run, you did fine. And it's hard to extrapolate from that little volume. Who traded this thing? You know, there are just a lot of people that are passionate, like some money stuff readers clearly. Some money stuff readers. There's a lot of people in the prediction ecosystem that are passionate, super forecasters that look over every market, and they probably have some tools that help them find ones where there's any form of volume, and they try to make a prediction in a trade. And if they think that they have some kind of an opinion, they do it. My guess is that's where this came from, although I can't know for sure. It's also possible that, you know, I'm sure some people read your article and saw some of the other publications, and we're like, oh, I want to get involved in this. But the overall could buy it at like lower than the price that's going on up here, which is like, feels like a good deal. The theory you could, yeah. Also, like I wrote about this, like in theory, like if instead of, you know, $400 of volume, there was $10 million of volume after you printed the trade, you could have laid off your risk, right? Depending on you might have lost money if it was all been at $85, but also with the amount of volume there actually was ultimately like, if you sort of take the beginning and the end of the trade, the law was changed in some way that helps them add on actually, you know, the details, but like, the bet resolved to the law was changed. So he got $0 on his bat and you got your $500,000 back, you didn't pay out the $500,000, but he could have like sold his position, right? Like he paid $10 for it, he could have sold it at $15. He certainly could have. And, you know, to be clear, like, there were pieces of news that came out in the interim around, you know, the nation was following this and with baited breath, but I was not, I should have been right. I'm probably the most interested person and I did that. There were pieces of certain news that came out around it. And if you were in the weeds of it, the probability might have been moving around a little bit here and there and you can interpret things in certain ways, you're right, the volume wasn't super high, like in theory, you could have a scenario, though, where someone puts on a head trade and then decides after the fact, like, oh, you know what, like, this is now trading at such a level, like, I'm going to take off some of my hedge, all of my hedge, maybe I don't need to hedge anymore, something else happened, like, that's one of the great things about this form. Of hedging, as opposed to say, like, some, this boat was just like an insurance contract is, it feels like a financial product, right? You can get out of your risk. I feel like an exchange trade product, yeah. Like an exchange trade financial product, the ecosystem needs to scale up and we're trying to bootstrap that scaling, but once it gets to that point, like, our hope would be, like, yeah, trades come to us, we put them up, there's an active market in this thing. Other people might have similar risks. And ultimately, people can trade in and out of their position. If they want to come back to us and they say, hey, the market's trading here now, would you let us out? Would you let us add? Like, those are all, like, flexible things that we can do here that wouldn't be possible without the market existing. Okay. And I'd say, like, capital and funding, when you print this trade to Kelsey, like, if I'm a retail trader on Kelsey, if I want to bet on something that has a 90% probability of happening, I put up 90 cents on the dollar, like, that's what you do here, right? Like, you're putting up $450,000. He's putting up $50,000. It all goes to Kelsey. You get, I guess, like, Fed funds on your cash or whatever. And when the contract resolves, either you get the $500,000, which will happen, or he gets the $500,000, I assume that, like, when Susquejana is trading options, it's more capital efficient. Yes, it is. And I think that ultimately, that's one of the areas that we hope to see predictions evolve over time. So the reason that Kelsey is rolling out leverage for at least some institutional. Right. And I think that'll be very helpful, getting forms of margin and leverage realistically, what helps the most here in this form of business is something that looks a little bit like portfolio margin. So say that we have a hundred of these trades on where all of them are us selling five to 10% of something that's not super likely to happen happening, right? That's kind of what the insurance business looks like. And a lot of risks are people wanting to cover some not very likely thing that happens. But if it happens, it's catastrophic. You have a hundred of those things on. They're all uncorrelated. In theory, you shouldn't actually have to fund 95 cents on the dollar on all of those, right? That's not super efficient. For now, we're kind of just saying we're going to figure out how to fund these things. Like is that like Jeff Yassis equity or like is someone giving you that portfolio margin in some form, you know? So in traditional options business, the prime broker is going to give you portfolio margin here. There is no portfolio margin yet, right? So we're just funding the cash, right? And it makes the trades a little bit more expensive. It means that if you have a really long dated trade. So let's say, for example, someone wanted to ensure against the Olympics being canceled in LA, which is, you know, years away. That becomes very challenging to do on an exchange in the current setup, because then your cost of capital for having, you know, your 90 plus percent on for that long is meaningful, right? A shorter dated trade is much easier. This is Jacob Goldstein from What's Your Problem. ODEU is the all-in-one platform that replaces them all. Why not you? Try ODEU for free at ODOO.com. That's ODOO.com. Artificial intelligence is transforming how businesses operate. But what will it take to build a truly autonomous AI enterprise? On October 28th in Boston, join the Bloomberg Tech Briefing, building the Agentec Enterprise to hear business and technology leaders explore the strategies needed to build cyber resiliency and secure trusted AI systems for the Agentec AI era, proudly sponsored by HPE. Learn more and register at BloombergLive.com/BloombergTechBriefing. Okay, so I wanted to talk about your general OOTC book. I say OOTC, I feel like technically, the Goat-Herter trade is not over the counter because you printed it on the exchange, but it's. Institutional risk-heading is kind of the term we use internally to talk about it. How many trades do you have? Is it hundreds? Is it dozens? It sort of fluctuates from time to time, but it's more in the dozens than the hundreds now. We're obviously opening to grow it. What's the split between true OOTC swaps and on exchange? I don't know exactly in terms of the number of trades. In terms of the size, the larger trades tend to be more likely to be a swap. What's the margin on that? Is that unsecured? Is that your posting daily collateral? is generally just it's negotiated. There are certainly some of those trades where we put up zero. If it's in the go herder trade, if we did it on swap, we would probably say like, I think you can probably trust we're good for $450,000. You know, there could be larger trades where we. You take his 50 there. Probably. You know, but when you start talking about larger types of situations, like we might have to put up margin. The other side might be given an ability to do margin. Like when you're on swap, it's all case-by-case basis. It also provides us with quite a lot of flexibility that we may not have with a pure binary prediction market. Like to give you an example, we've had parties that have come to us interested in saying we're really concerned about a moratorium happening on data centers in our state or in our county or whatnot. But if that happens, what we're really exposed to is now the depreciation of the chips. Can we come up with something that has a trigger that's like a prediction market-based trigger? Binary trigger does this thing happen, but then pays out based on the price of an asset. Whether that's the price of chips or the price of compute or sometimes the price of power. And you know, we're pretty flexible of being able to do that because again, if we have like, you know, a reasonable index to settle to, that value is that's kind of our bread and butter, right? Moving into a financial asset and doing that as a firm. So that's the thing that we're good at and it covers their risk more directly all the better, right? So we're very happy to be creative when it comes to that swap and that can go to the margin. Like, we're going to look at all the components of it and try to figure out what's the best way to get this thing done that sort of fits all the parties. And you know, again, we're looking at this business long-term. So we do want them to be ultimately be happy as well with what's happening because we want repeat customers and some of these are small communities. We want one data center to tell another like, oh, we got this thing and it was helpful. So that is our goal. When they always thought about is the couchers will tell you like, we got into this business because people want a hedge election risk and like, there's the story of Jan Street getting the election right and like losing a bucket of money because they got the directional impact of it wrong. And I always thought that was very like illustrative because like, there are people in the world who are like, I think that Trump will be bad for the S&P. And so I want to buy a hedge on Trump getting elected because I have all this S&P expression. But look, it's totally the wrong hedge because like, you know, the S&P is upright. Like, when you have people hedging the election, is that the thing you talked about where it's like, there's an election trigger and that a financial asset pay off or is it like people who are like, I'm in a very regulated industry. I'm going to be shut down if the Democrats win. What is the intuition behind people doing election edges? I think those are more just binary. It's just who's going to win. And I do think that generally, why is that a hedge? Like who's hedging what there? So I think there's a few different angles. One of them is kind of, like you said, like, this would be just very bad for my business, right? And I do think that the things that are sort of the real businesses, like, this is bad for my business, that's probably the thing that's a little bit slower to pick up because it takes longer to get someone like, you know, who has a real money business to start doing this, right? That's a totally new product. It makes sense why the financial players get involved first. Well, but also like, if you have a real money business, like if you sell ball bearings, you're not going to get shut down if, like, one side wins the election. I could name some firms that make it shut down if one side wins the election. Right. So oftentimes it might be a situation where it's not like you're going to get shut down, but there could certainly be economic impact to your business, right? And you could want to sort of hedge out some of the economic impact. I'm just wondering, like, how modelable that is, right? Is this, like, politically committed to CEOs who are like, oh, if the Democrats win, it's going to be a disaster for me. But like, that's not empirically true. I feel like people's intuitions about the effect, or even Jane Street's intuitions about the effect of an election, unlike their business or asset presses, are unreliable. And it's a strange way to hedge. I can give you an example. Oftentimes, I think if it's a real business, it's much more targeted. It's much less like vibes. This party is going to crush me. But more say, again, just to go back to the data centers because it's the thing we're hearing more about than everything else combined. And you know, I wouldn't say it's like the majority of the trade to be done to date, but it's by far the majority of the interest that we're getting. If you're a data center operator in Texas, right? You've got a governor election. Data centers are 100% on the ballot, right? No, they could come and they could say to us something like, we actually want to hedge the moratorium risk. But you could also get a pretty reasonable proxy for just hedging the governor race, right? And those two things wind up being extremely correlated. So I think with the real money business, it comes more down to like, what is the actual policy that's going to impact me? Okay. So like, you're a data center developer. And you're like, if the election goes one way, there'll be a moratorium. The election goes the other way. There won't be a moratorium. There's a moratorium contract that you can buy from Tulsa, and there's an election contract. There is, let's say, no basis risk in the moratorium contract, right? If there's a moratorium, you get paid. If there's no moratorium, you don't get paid. There's somewhat obviously basis risk in the election contract. Like they're highly correlated, but there's like obvious basis risk. Why are you buying the election contract? Is it cheaper? Is it more fun? Is it more holistically a threat than like a moratorium? What's going on there? I think the way that the conversation kind of happens is the party might not actually even know at the beginning that there is a moratorium. Yeah, but you could tell them, right? Which is why I think that realistic. Why are you selling the moratorium? In that scenario, I don't think we've done a lot of sort of like real money business trading the election. Like the people who want to trade the election itself are much more likely to be a firm that's on the financial side where they're looking at the financial impact, like a hedge fund, for example. They're looking at their portfolio risk and all the ways that their portfolio is going to move based on that election. So they're like, my portfolio has some basis risk, the moratorium contract, some basis risk to the election contract, so neither one is a perfect hedge to my overall portfolio. Right. And you know, I think that most hedge funds that have portfolios are used to trying to break down factors. And election is absolutely an important factor. And you've probably seen some of the baskets that like banks put out of here is all these different products that you should put on to bet on Republicans winning or Democrats winning. And those are your portfolio looks like one of those baskets you're calling South Carolina for. Right. And you can hedge that by putting on that basket. But again, that basket is actually oftentimes a dirtier hedge. You have the ability to, you know, you could close some of your risk. But a lot of these firms might like those positions that don't want to close the risk. So what you're worried about is what's the impact going to be on this portfolio, you can hedge the election directly. And I think that's where you're more likely to see the election. Plus to be honest, people have a lot of opinions on the election. I think you have a lot of people who just think one side is going to win or not. It's a market that obviously trades a lot. If you're a real money business, I think in general, you're probably going to go narrower. And that's what we see. Like the real money businesses, they're looking for things that are much narrower. The firms that are closer to financial markets are often looking for things that are broader. And then is that because they're hedging a broader portfolio because they're taking directional risk with you? I think it can be both. Obviously, when we are doing this institutional business, we're really trying to focus on the hedging trades in most markets. Now, in an election market, especially a big one, like, say, Senate control, those are liquid enough markets with things going back and forth that we're happy to take on the other side of someone else having an opinion and it's their alpha trade. If someone wanted a very, very bespoke thing, like if some hedge fund came to us and they were like, you want to put on this go-hurt or hedge, we would be less likely to take it on. I wrote about you did a hedge for a Spanish soccer team getting relegated. That is like a blazing red alarm adverse selection risk possibly, right? And I assume, and I wrote this, that I assume that your due diligence involved, like modeling how much money they would lose by getting relegated and making sure that you were hedging something less than 100% of that. How much adverse selection due diligence is there in these markets? Are people coming to try to pick you off? You know what I mean? Politicians betting on guns themselves are like, is getting adversely selected by people who know more about these markets a big risk on the OTC slide or is it all pretty up and up? To this point, we haven't really experienced that on the OTC side. If we're going to show a big size and a tight price, we want to have an understanding of the story, right? And we actually really want to work with the other side to understand. You see a tight price. I assume you're not trying people to excited markets on these things. There are times that we do. There are times that we will show a two-sided market. A lot of the times it is one-sided, but there are some parties that actually want a two-sided market and we'll make that. And tell me, like, so you said you want to understand the story, but like, what's the story of a two-sided market? It's just that it's from what's the speculator. So sometimes it's coming through a broker, right? As brokers are just used to, we want to get a two-sided market. We want to show the width. That's sort of part of their process. In that case, we're happy to do it. Usually, if it's a broker, they're more likely to ask for a two-sided market, not always, but more likely. You said brokers. Like, a broker comes to you with like, we have an anonymous client who wants to take a position on the Texas election. Can you show us a two-sided market? So you don't really have a sense of the story there. So if it's something that's kind of more general, right? If it's the Texas selection, there's a lot of information out there. You're not likely to be adversely selected, but we're not that worried about adverse selection. And so it's fine to show two-sided market. They could have an opinion, and that's okay, right? If they're like, we want you to build this market from scratch, and it's this very specific thing, go back to the goat trade. We've obviously talked about that one in good amounts today. It's like the best paradigm. No, it is great. You know, obviously, we're going to need to know the story, right? Like, you can't design the trade that you have the opinion on to get your alpha trade against us. We're not going to be able to get out of the risk in some very random specific thing. So it really comes down to the specificity. If it's a really narrow trade, we want to have a conversation. We want to understand why you want to do it. And part of that is so we don't get adversely selected. Part of that is so that we make sure we actually design the right product that actually takes your risks on. If you come in a liquid market, I want to predict who's going to control the Senate. That's fine. We'll make you a two-sided market in size. feel comfortable. There's enough trading in that market that we could probably get out of a position in that and you know We know enough about it that we're not that concerned that someone's going to adverse select us In crude numbers this book of OTC trades like are you laying off like 10% of it or like 90% of it are like probably closer to the 10% That's what I thought this is a business where we are the vast majority of the time holding all of the risk everyone's you know Sometimes I assume with this business is like 100x where it is today you'll be laying off 90% of the risk yeah for if everything gets super liquid You won't be holding absolutely like a giant insurance company right and you know I think there's different ways that we ultimately can wind up laying off that risk some of it can be the markets are liquid enough that we're Just doing it in the markets that'll probably work for some things and the broader you get the easier that is so I can Absolutely envision a scenario that's not so far in the future where there's a liquid enough market and say like well Texas have a moratorium on data centers that we can trade in and out of that when you start talking about things that are Hyper-specific you're a lot further from that our goal is we really do want to work with people in the insurance Industry because they're used to all different forms of syndication and sharing and things like that and so that's Probably one of the better ways that we can ultimately wind up sharing some of that like laying off some of that risk is to Potentially share some of it with someone who's in the insurance space and you know I think that's probably a much easier way to go for the specific things. Let's say data center moratorium trade is Not that liquid. I don't know if that's true, but like Texas governor is really liquid like are you like Taking a hundred million dollars of data center risk and like laying off 50 million dollars in the governor election and taking some Basisers like you know look at factor correlation model of hedging this stuff It's more that it influences how we will act. So we're more likely to try to lean into a position in some of those other markets Most of them if you're taking a massive trade and you're gonna just sort of like put this massive trade on and have the basis risk Like there's still not quite the liquidity and like Texas governor to be like we're gonna put on this massive trade We're probably gonna impact that market in the process and we're probably gonna wind up with some degree of basis risk there too So it's more of a we have this on we understand the important factors that are risk factors that we have We're going to sort of use that both to change what we think is fair because there can be information in some of these trades And also to potentially try to sort of reduce the factors as we can But that being said we understand that a lot of these are we need to basically manage our risk budget effectively Some of it can be done through laying off and all those things a lot more of it has to be done through like Being selective in terms of like what are the trades that we're gonna put on what trades make sense and you know When we look at data centers and just the total size of the risk that's probably uncovered right now You know, it's in the hundreds of billions of dollars So it's definitely something we have to put a lot of thought into of like how do we? Ultimately at scale and you know right now It's not like it's big enough to be like it's eaten up all of anybody's risk budget for us But at scale we need to understand how this looks when you have some massive risk that the world has and they need some one to be the capacity on the other side and you know That's a good part of the reason why we actually do kind of invite competition to a degree in that like we want to other capacity We want to grow the markets and we think we'll ultimately actually win to that even if it means we maybe are competing with more forms of Insurance syndicates that are doing some of these things too We actually welcome that and that would be the idea of it like prediction markets are basically sports gambling now and The vision is they're basically society's hedge against data center risk like You know Things happen I suppose but I always thought when I first wanted to start building a prediction markets desk You know it's four years ago now. I really did envision this institutional hedging case as the reason to do it I've got a story I've told a few times now about a friend who's CFO of musical instrument company that was concerned about tariffs And that was kind of part of the light bulb in my head. That was the original reason we really wanted to do it The path goes through sports because sports is sort of built the space It's created a lot of volume a lot of interest on it But I do firmly believe that if prediction markets is ultimately successful The long-term success will be predicated on this institutional hedging and that's for ecosystem reasons because you need a robust ecosystem that has a combination of different types of market participants And hedging is a really important part, right? If you think about traditional financial markets You people are not looking for alpha exactly the prediction market like great source of people who are not looking for alpha or Retail sports gamblers, but like data center hedgers are much right You're right if your entire market is built from people who they're trying to predict right and there's reason why that's the first people in Predictions it's prediction markets people are trying to predict the outcome, right? You can build something there, but it's not as robust because you don't have the type of variety of reasons people are trading It's a much more robust ecosystem if you have the hedging in there as well And so I think there's some hedging now But I'm not going to go and tell you that that's the majority of the order flow that you see in predictions today But I do honestly think it needs to be if it's going to become what it needs to become Is it the majority of the order flow in your Institutional hedging book slash birdie see book in the institutional hedging book. Yes, the majority is definitely hedging as opposed to Petron alpha trades. Yes, for sure, but again that volume relative to the overall predictions volume is still very small as it today Some people treat Chachi PT like some kind of smart search engine and some use it to get work done Chachi PT work is a new way of working in Chachi PT that can take action across your apps and files Stay with a project for hours if needed and turn a goal into finished work It's designed to help you move from a chaotic starting point to a reviewable first version So all the source materials briefs and scattered information that you have to grind through to turn into something useful Can just become something useful put Chachi PT to work on your most ambitious ideas and projects Get started at chachi PT dot com by selecting work mode available on plus and pro plans This is Jacob Goldstein from what's your problem? Don't make it harder with it doesn't apps that don't talk to each other One for sales another for inventory a separate one for accounting. That's software overload Odoo is the all-in-one platform that replaces them all CRM accounting inventory e-commerce HR Fully integrated easy to use and built to grow with your business thousands have already made the switch Why not you try Odoo for free at od o o dot com That's Odoo dot com the big-tick podcast from Bloomberg news keeps you on top of the biggest stories of the day My fellow Americans this is Liberation day stories that move markets chair Powell opened the door to this first interest rate cut impact politics Change businesses. This is a really stunning development for the AI world and how you think about your bottom line Listen to the big take from Bloomberg news every week day after new on the iHeart radio app Apple podcasts or wherever you get your podcasts Sorry to circle back to sports but like sports hedging so some sports hedging is like You're doing a promotion and you Whatever but like how much sports hedging is sports teams hedging themselves? I don't think that there's a lot of sports You do more than one because there's one publicly reported Spanish soccer relegation trade have you done more than like Three so not all of it is in sort of this current era of predictions either like there's been history of like different types of OTC transactions where sports teams might might hedge some kind of risk or or another whether it's you know How far they advance or whatnot sports teams don't want to put on a trade? That's a direct bet against themself a lot of the time and there are reasons for that Partially just kind of optics But then there's a lot of businesses that are intrinsically tied up with the sports teams, right? You could say you have some Network that has media rights and they need a team to make the playoffs or even just like they've got some high Marking matchup that's gonna happen on Christmas or some key day and like they want the teams to be good You've got the sponsors you've got all these mouths that are tied up with it, right? And we're probably seeing more of them involved in saying like we really want to head some of the risks that we have on there The different players that have intrinsic risks tied up in the performance of a sports team or even just a player And then obviously there's the promotion stuff too, which is real like there's a lot of promotions that are based on sports teams And you know, we're seeing more of them start to say well, we can hedge our promotion risk or even We can launch a promotion if we can hedge the risk, right? We want to do a promotion where if you pick all The NCAA, you know, March madness winners you get a billion dollars or something But you need someone to actually be the one who's gonna take that on and those are things that we're willing to do, right? You need some mechanism to be able to do that My impression is that Sportsmen's first of all are pretty liquid and second there's a lot of correlated trades, right? Like someone who's like I want to hedge my media rights to some sports team Like you can lay that off in a lot of places. Yes. Does that work by like you do a big trade on you try to lay it off Does it work by like you do a big trade than like there's some dashboard of like how much nicks risk you have and you like Adjust the prices on your full market making on the next that it's probably more the second like I would say relative to most firms out there We tend to have you know more real attitudes towards risk, like we really focus on the expectancy of the trade. So we take on some big trade and we have Nick's risk, right? We're going to tune some of our parameters to try to ultimately over a slower period of time, work out of that risk in the market, but we're not going to go and sort of just try to get out of all of it now because that's more expensive, which by the way is exactly what we do in traditional financial markets too. If we get a big risk put to us, we work out of it slowly, you want to pay less to work out of it if you work out of it over a longer period of time. We're willing to take the higher return for the longer period of risk and that's firm policy overall. Yeah, I guess it's like in general in the production market, OTC hedging business, like how much tuning of parameters is there right? Like the goat herder like goes nowhere, right? Like there's no other correlated trade, right? But like when you're thinking of the risk budget for like Texas elections, these things are all correlated, right? You know, if someone comes to you and is like, I want to do a data center moratorium and you're like, I have so much Texas election risks, like we're going to show a bad price on that because like it would, you know, add too much to our risk or like you sell a lot of data center moratoriums and you like adjust the flow market making price on the Texas governor's risk. My thing about the right way is that sort of dashboard of factors and correlations that is kind of like feeding into all of these trades or like are a lot of these trades. So one off that you evaluate them on their own. So that there's definitely a little of both, obviously there's plenty of these things that are one off. And there's plenty of them that are short dated enough that like by the time you would have gone and done anything, the event is over. There are certainly some where we have things for longer periods of time. And I'd like to give you a very clean answer, but it's actually kind of a messy problem. Like part of it is, okay, we got this position. How did we get it? Is there information in that? Should we actually change our fair values? Is sort of the first question based on this position that got put to us? So the question is, is there information in the trade that it should charge its fair values to change or like, are we further from that? Like you don't care if you're further from that. We'd care about both, but the first question when we have a trade is, is there information in this trade? Obviously, if some of these things we've talked about have been, this seems very much like a hedging trade. But not everything that we do is like, this is definitely a hedging trade on the other side. So the first question is, here's a trade. Let's try to understand how this should impact what the probability is of these things happening. Once we've done that, then we're going to look at our risk and we're going to look at it across all the different factors that we have. And you know, generally we're going to say, okay, we would need to be compensated a little bit more to put on more risks now that we've got a lot of risk on. And we would probably be willing to take risk off for zero expectancy right now to reduce our risk. We would do it for the dead fair price, no bit as spread at all. And that's kind of how it works with most of our risk. And it's the same thing here. Now, the factors and parameters are trickier here, right? Because as you pointed out, you can't just throw it into sort of some market model to spit out all your correlations and tell you what your risk factors are. And that's just because it's like, there's not second by second, but I'm serious for 20 years. There's a lot of second order effects that happen like, you can't just throw a quant model at this. You have to think really hard about how are these things correlated. For example, like, how is the, you know, Senate race in New Hampshire correlated with data center moratorium risk in Texas? They're correlated, right? They're less correlated than, you know, with the Texas race and there's no, like, long history of data that's going to just give you the answer. So that's part of our process. They're like 10 years. You'll be able to throw a quant model at it, right? It's sort of. It'll certainly be a lot easier to just throw a model at it in 10 years than it is today. In part because you'll have correlation of like actual events, which is hard because, you know, the Senate elect comes every two years or even like, but then there's correlations of like the price time series on Kelsey, right? Which gives you some maybe information. It gives you some, yeah. And I think, you know, that as you get more history in those markets, become more liquid, you will absolutely be able to use that information more to sort of back out what some of the correlations are. All that being said, there's a lot of markets that are new also. So like many of these markets exist for a period of time and then they go away. And so you still are going to have to figure out how to do the classification of like these are the buckets, right? This is like a legislation contract that's in California. How is that related to when we go back eight years, like another legislation contract in California? There's definitely a lot of expertise that's required. And ultimately it's figuring out how to make the quant models understand some of the things that are our intuition about, you know, knowing that this product is kind of correlated to this other product for these intrinsic reasons. It's really just creating a map of like what is causality in the world look like because you know, that is the thing about predictions. The breadth is phenomenal, right? You know, and Kelsey, there's over a hundred thousand markets as of right now. So obviously we don't trade every one of those markets, but understanding how they're all interconnected. And that is why, you know, why is it useful to have a background as a macro trader? Well, it's because I spent a long time trying to think about how one thing impacts another and like a really broad scale. And that translates well to understanding risk and predictions. So like you clearly do elections through legislation. You do sports like is there like don't do entertainment, do do entertainment, we'll do it if someone asks. We'll do it if someone asks. Someone came to you and was like, I need five million dollars on the Oscar winner, you'd be like, yeah, let's do it for, for the rest of the year. Well, assuming that I understood why they wanted to do the thing, yeah, I think we would be able to do it. You should find some price and we'd find a price. Yeah, like Austria is a good example of like there's a pretty decent amount of liquidity in the market. Like those prices are pretty good. And ultimately, it takes less volume than you'd think to get a good price signal out of a market. It's not millions of dollars necessary. It's probably tens of thousands of dollars at which point I'm not saying it's perfect. But like you've got a pretty good ballpark for what the fair value is after something like tens of thousands and you get more than that on Oscars. So I think like Oscars where there's that much interest, like those prices are probably pretty good. We can make a market around it to a hedger and feel pretty comfortable that like we probably are making a market around a pretty fair value. We'll do it for our own research too, but it's a really good starting place. What's the right intuition for what a market around it is? So like let's say that like someone winning an Oscar is at 25% are you making like a 2426 market for five million dollars or so you're making a kind of depends is probably a little wider than than 2426, but not about 2030. Yeah, it's tighter than that somewhere in between those and it sort of depends a little bit on some of the specific dynamics. Like again, you know, we might think they're hedging, but you're never 100% sure. So we're going to talk about all those things and it's sort of a case-by-case decision. But yeah, two is a little tight, but might be closer to two than 10. All right. What else should we talk about? On the institutional front, I do think that probably one of the biggest things that ultimately we will probably see within the next few months is some of these really large trades around some of these big products, like hundreds of millions of dollars. And we actually do see big products meaning like a data center, like a data center moratorium or some of the things that are like, I think you've written about this like open AI and anthropic have bankruptcy risks that are sort of priced into the market and but like nobody can hedge them. Every data center is exposed. So like one of these maps of labs where if one of those interesting so like, to me that's like open AI CDS, right, but like there's no CDS, there's no bonds, like so yeah, there's effectively an open AI CDS is you and your prediction markets. I mean, yeah, so those markets have existed and like I mentioned the moratoriums, but I think that is the two things we hear the most in the data center space. I don't know, I keep coming back to this, but it's just because it's the biggest financial hurdle ever again. It's that and then it's the tenant bankruptcy. So that's interesting. So like there's trillions of dollars of open AI debt in the world, but it's not debt. It's leases, right? And the people who are long that debt or data center developers and financiers, right? And they want to lay off some of that debt to like back when there was a single name CDS market, like there were people who were like get long debt by buying CDS, like I assume that you are not going to warehouse hundreds of billions of dollars of open AI bankruptcy risk. I assume that if you are getting into that trade and size, you are somehow intermediating it to someone who wants open AI credit exposure and does not own a data center. Is that right? I don't know that it's as simple as like we're going to find the other side of the trade. It's more likely that we would want to find parties that are willing to partner with us. Like this is one of those situations where there's a massive risk that sort of outsizes everything else that the world wants to hedge, and how can you basically help provide capacity? So we would put on the size that ultimately we're comfortable with, which would be large, but it's not going to be hundreds of billions of dollars. Yeah, it's it. You say like that the world wants to hedge, like apparently the world wants to take trillions of dollars of open AI credit risk, right? Like, well, still on bonds, right? Like someone wants that credit risk, and it's not necessarily that the data center developers wanted in the concentration that they have it in, but like, you know, could it be packaged? Could be packaged. Yeah. It's a great thought. And you know, we love those types of like innovative things. I don't know that we've gotten to the idea of like figuring out how to package and syndicate and all that yet. We're probably not quite there yet, but absolutely we need to figure out how do we create the structures where we can actually take down some of this risk. Certainly the data centers don't particularly want this. And also, I think one of the keys is, if you can actually start taking out some of these risks, it makes these projects way more financial, right? Like what we've heard from some of the parties is actually, if we can get the right product, we actually wind up getting paid to take off risk, right? Because they get a better financing rate from the banks. Ultimately, the banks would be willing to do more of these projects. Like, there's a lot of efficiency that ultimately just comes from it. And we've been told, legitimately like, if you can get this to work at this right price, it actually will save us money. It's not just that we're taking our risk off the table. So that's where things really start to change, is if you can start to sort of create this integrated system where like the banks are saying, like, okay, this is what you do, like They do with other forms of information. insurance, like you want this loan, you need to have this product that's part of it. And everyone's trying to figure out data centers right now and an AI because, you know, you went from nothing to this incredible amount of dollars that's in there. And it is, you know, it's all you hear about in financial circles and it moves the markets more than any other vector. I just, I tend to think that like if the need is to bear, you know, $10 trillion of frontier lab and hyper-scaler credit risk, like that's like, look at me, Susquehaw. No, it's not, it is not going to be us that ultimately is going to take all that risk. It's going to be us that bootstraps it, right? But we need to get the proof of concept. So you're going to like run a CDS desk and like, I'm just going to say, "A-I-J," but like, you know, like some insurance company is going to own, that's absolutely what we'd like. We like taking risk. We'd like to take some of the risk. What we'd really like to do is to build that market in an efficient way. So we become this cog that helps that market to function efficiently, which is our role, right? We take on risk. But we help the market to function efficiently. And ultimately, yeah, we're working with an AIG or we're working with whichever insurers and other sort of like mechanisms that there are to distribute that risk, right? Like, it's funny because a lot of the things in prediction, we're just like, yeah, we'll warehouse it because we're big enough that like most of the prediction things like we can just be like, yeah, we'll take it, whatever. It's millions of dollars. Like, it sounds like-- You're big for a goat herder. We're big for a goat herder. You're big for a soccer relegation. Right. But we're small for the entirety of data set. Right. We can take down trades that are large, but like, I suppose gives you a little humility when you're used to feeling like-- You're nothing to a data set. Right. You're the big player until you get involved in data centers like, oh, we're looking for $10 billion. Like, OK, let's think about how to do this. Jeremy Mallads, thank you so much for joining us. Thank you for having me. It's been very fun. It's been very fun. [MUSIC PLAYING] And that was the money stuff. You can find my work by subscribing to the money stuff newsletter on Bloomberg.com. We'd love to hear from you. You can send an email to moneypodoflumber.net. Ask us a question, and we might answer it on the air. You can also subscribe to our show, wherever you're listening right now. And leave us a review. It helps more people find the show. The money stuff podcast is produced by Anna Maserakis and Moses Anton. Our theme music was composed by Blake Maples. Amy Keen is our executive producer. And Cheryl Brumley is Bloomberg's head of podcasts. Thanks for listening to the money stuff podcast. We'll be back next week with more stuff. 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