Odd Lots - Why Susquehanna Is Building a Prediction Markets Business

Episode Date: June 6, 2026

Prediction markets that enable you to bet on pretty much everything are everywhere nowadays. But there's still a big question over whether they can expand to include larger institutional investors like hedge funds. Part of the problem is that a lot of prediction market contracts are illiquid and trading volumes can sometimes be shallow. That's where trading firm Susquehanna International Group comes in. In this episode, recorded live at New York's City Winery, we talk to Jeremy Maletz, Susquehanna's head of macro trading and prediction markets, about the firm's market-making business with Kalshi. We talk about how big investors could use prediction markets, what Susquehanna is seeing in terms of flows, how a market-maker hedges risk on these contracts, and how it makes money from them.See omnystudio.com/listener for privacy information.

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Bloomberg Audio Studios. Podcasts Radio News. Hello and welcome to another episode of the Odd Lots podcast. I'm Jill Wisenthall. And I'm Tracy Allaway. So Tracy, we're still rolling out shows from our live show on May 28th in New York City at City Winery. As we discussed in our last episode from the show, it had a sort of future of markets,

future of trading theme to the night's conversation. Right. And if you're talking about future of markets and trading, we have to talk prediction markets, right? Yeah, that's right. So in addition to the fact that there is the, quote, AI trade, unquote, the other big thing going on in markets is the sheer explosion of instruments with which people can trade, right? So it's like you used to have stocks and bonds and options. And then the options started getting more exotic, like zero-day options, which you love and so forth. But now it's like if you could think of something that would resolve in some way whether it is snowfall in New York City, Tesla deliveries, or how long a halftime show is at the Super Bowl, there probably is a way to bet on it. Right. And so one of the big questions is whether or not these markets are going to take off from an institutional perspective, whether or not you're going to see more professional investors get into the business of betting on not just snowfall in New York, but maybe halftime shows and that sort of. of thing. But that said, we do have some institutional participation in the market already because

you have market makers that are starting to come in to try to make these markets more liquid. Yeah, but you really said the key thing here, which is we know there's like a ton of liquidity for like the sports betting, et cetera. Like that's not the problem that needs to be solved. It's these other things where in theory these might be useful instruments for hedging some sort of economic risk. Maybe not the Taylor Swift one or the half-tive show ones, but so, of these other ones, but like in theory, some of these contracts could be useful for hedging. And so the question is, yeah, but will anyone use them? And so on this discussion, we really had the pleasure of someone who has on very little media in general and who is right in the heart

of trying to essentially solve this chicken and egg problem. Listen to our conversation with Jeremy Mallets, the head of prediction markets at Susquehanna International Group. All right, why don't you tell us, just let's start really simply. What does the prediction markets desk at Susquehanna do? So essentially, the main function that we do is market making. So we're the main liquidity provider, one of the main liquidity providers on quite a lot of platforms. And that means we're providing liquidity and everything from sports to economics to

politics. But I'd say also a big part of what we do is we were the first institution that got involved in prediction markets in the first place. So we're really trying to have a role of kind of being a shepherd for other institutions as they get involved. So I'd say it's a two-pronged goal really of providing the liquidity for the ecosystem to help it to grow and then bringing others into the ecosystem, which I think has been one of possibly even the more important role of what we've done to this point in time.

Wait, can I ask an even simpler question, which is I kind of feel like Suscohanna is like the sales force of the finance world. where I have this vague idea of what you do, but also not really. What does Susquehanna do? Well, my wife asked me the same thing. It's her birthday, by the way, so happy birthday to her. It's incredible that you're here. I know.

I owe her big time. So we're primarily a market-making firm, and our bread and butter has always been options. So we make markets in pretty much every option, equity options. But obviously, we trade a lot of instruments across a lot of things. And we just, we have a culture overall that's very much looking for new opportunities and thinking about things in probabilities and kind of, you know, it all starts from Jeff Yoss, our founder. He loves things like prediction markets.

So, you know, we have a lot of random businesses at Susquehanna all over the place. You wouldn't believe the number of things that we do randomly. And it all comes back to the same culture we have of thinking in Bayesian probabilities and trying to, to think of everything in terms of break it down into what the odds are. But the bread and butter remains market making. And we try to use that to kind of make a market or bring a market to any asset class we're looking at. Let's talk about, okay, there's some contract out there. Who is going to win the Texas primary election?

Let's just say. Why, you know, and so there's an exchange and there's an instrument and it's either going to end at 100 or zero, et cetera. Why does this market need market makers? Why can't it be entirely peer to peer such that if all of us in the room just wanted to trade, we make a price amongst each other on an exchange? Why is a market maker an important part of the infrastructure for this to work? So if you have a market where you have an insane amount of people that are all trying to trade all the time, you might be able to make it work without a market maker.

Okay. But what a market maker is really doing is it's helping to bridge the gap, between the different people who are trying to trade. So, Joe, you might want to trade now, and Tracy might want to trade in an hour, right? But if there's no, but that doesn't help you if she's not there now. So we're basically saying, hey, we're going to be there

when you want to trade. And then we're going to wait. And when Tracy wants to trade, we'll take the other side of it. So we're basically a function that matches the buyers to the sellers across time and also across size. So how big does your balance sheet have to be

to be like dedicated to the particular business. Like, how sizable are you with an entity like Calci? So, you know, it really depends on the way that you do it. There's a lot of small independent groups or just individual people who are able to be out there and they make markets and their goal is to try to balance it and they have to think a lot about capital. We are very fortunate that capital isn't generally a constraint for us, right? So that's one of the things that it's an advantage for us, but it's also something we can bring to the market. We can provide much, much larger size on things. It's why we're well suited to bootstrap a market and to do institutional

type size because we're not constrained by capital in that way. And do you generally try to stay like risk neutral? No. I think that is pretty deeply in our culture that obviously all things equal. We would like to be risk neutral, but we're willing to put ourselves out there and wear a position. And in general, on our options trading, we tend to warehouse a lot of risk for the street. Sometimes there's just something where everyone needs to hedge a risk in one direction and someone needs to be on the other side of that. And that's an important function in prediction markets, especially when you think about use cases such as hedging, where you've got some global risks to the world. Everybody needs to hedge that risk on one side. Someone needs to be on the

other side. And we're willing to hold ourselves out there to do that. And, you know, there's a lot of pieces that go into being able to do that. Obviously, when you're not neutral on a risk basis, you have to be more confident that you're right. But, you know, that's a lot, a big part of what our team has driven to build. So this sort of leads into the next question. And it's maybe the multi-billion dollar question of prediction markets, which is, okay, we know that there is a huge amount of the prediction markets business, which is just sports betting under a slightly different form.

But you mentioned hedging. And this gets to the core question, which is, in theory, there are a lot of instruments on these prediction market platforms that could be useful hedging instruments for corporations, say a market on snowfall in New York City, which might affect an airline or something like that. Maybe they want to hedge that. When I look at the platforms currently, I see a price for like snowfall, but I don't see anywhere near the volume level that say would really justify like an airline could, you know, you see $150,000 in volume on whether or something.

there'll be between six and eight inches of snow. That's obviously nowhere near deep enough for a serious economic actor to participate in. So where are we on that in terms of the promise of actually useful instruments for hedging? Right. So when we first got involved in prediction markets, our real role was to bootstrap the liquidity. There were no institutions yet. And it was largely going to be a retail product. We were bootstrapping for large volume retail liquidity. Now our next challenges, we want to bootstrap institutional liquidity. So, yes, you might look at a market that doesn't seem like it has enough volume for an institution to hedge, you know, tens of millions of dollars of risk. But that's, you know, part of the reason that I'm out here doing a podcast

today is we're trying to make sure that people start to understand this is viable and we're putting ourselves out there that we will be willing to put that kind of risk. And we can put out that kind of risk on a contract where far less volume is traded. And that's because, Because what the prediction markets really provide is information. It's a price discovery mechanism. So you have this phenomenal community of super forecasters that exist on a prediction market. And it doesn't take as much volume as you would think to get to a fair price. And that allows us to say, hey, okay, we've got a reasonably fair price on this prediction market.

Maybe it's only traded $100,000. But we know that there's been a lot of smart people that have looked at this. We can do our own internal vetting at the same time also. And now we're comfortable going out there and saying, we're confident enough in this price because of the price discovery mechanism that will make tens of millions of dollars of risk to a company that needs to hedge its risk of what, you know, some regulation or straight of Hormuz or whatever it is that's happening in the world.

So, you know, someone needs to go out and do that. And I think we're kind of uniquely positioned because of our culture of saying, yes, we are willing to take that risk. And yes, we want prediction markets to grow. What have your conversation's been like with institutional players so far? Like, what do they say is their main either constraint or, I guess, reluctance to get on some of these markets? So the first piece is the exact thing that you just brought up awareness. They look at the markets and they might say, well, I don't see how we could actually hedge some of these things because there's not enough volume and liquidity.

To which our response is, we will be the liquidity. You know, then the other question is, okay, well, we sort of need our compliance to get comfortable with this. This stuff is so new. What's the legal landscape? What, you know, how do we get this stuff even over our firewall? I think institutions were always going to be the slower moving player relative to retail. And so that's why we really want to sort of hold some of their hands to go through this and

figure out a lot of different ways that they can use prediction markets. Some of it might be an institution connects to a prediction market platform. does a block trade on an exchange with us. It could be that a trade goes up often exchange as a swap, but it's licensing the exchange market data. And we're trying to create as many different setups as possible so that people can do these trades. And we're going to basically be, you know, be the facilitator to say,

okay, you need a hedge. We're going to figure out how to let that happen. So this is really important. So if I look at a contract and I see a number there like $150,000, it's possible that there's a hundred and there's that there was a trade, an off-platform trade of much bigger size that was not printed on that, but that was more by swap form? And is that currently happening? It is possible. I'm not going to go and say that that's a big thing that's happening yet. We're trying to build out the infrastructure

to have as many options as possible because we want institutions to start moving. And the more they do it, the more they're going to read. If an institution, again, let's go to the airline hedging snowfall, Sure. Would they have a relationship with you? Would they go through a prime broker who then has a relationship with you? Like, what is the actual sort of like chain of phone calls or whatever that happened? So it can be both. But ultimately, when I think of what we're really good at and what we're not really good at, we're not necessarily the best in the sort of know every single airline customer business. So we want to work with a lot of those other intermediaries. That could be a broker. It could be a bank, it could be an insurance company, the people who have those relationships

and are in the business of constantly advising this is what you should do. We understand we want to work with them and they could have a very important role in helping to be a part of the infrastructure that gets it from the customer with the risk to us being the one that have the other side of this. Residents in downtown Montreal, flights from Porter Airlines to weekend, go tickets and $1,000 of cash. Please love.

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Listen to the big take from Bloomberg News every weekday afternoon on the IHeart radio app, Apple Podcasts, or wherever you get your podcasts. Joe's chosen a pretty, like, reasonable contract, I would say for his example there. But like, are you committed to making markets in all the contracts available on these platforms? because I'm thinking about, you know, like the return of Jesus Christ or will aliens invade? How would you even go about making markets? Like the length of a halftime show or something like that. Right. Yeah.

So we don't make everything. And honestly, we're not the best to make absolutely everything. Some of, you know, some of the things that you might say that something perhaps in pop culture, we're probably not going to be that good at it relative to others, right? There are certain things that... Are you trying to build that capacity up? Well, we'll see. But, you know, there is a community of a lot of people that can make a lot of these types

of markets. And what we want to do is we want to scale our sort of capacity with our technology, our quantitative models, our sort of very trader-driven insights that we have in our capital. That's where we're going to add the most value to an exchange. If something just requires a bunch of people to dig into it, the universe of all the different super forecasters, we're probably not as necessary. you probably don't need us to know something about when Taylor Swift is going to get married. But the fact is that the things that have the most economic value tend to be the things that

need us the most. And so that's really where we try to play. And I think it's great that the ecosystem has these complementary forces of like the community of all of the super forecasters and then the people like us who both kind of have different skill sets. Do you see right now, so again, the people say, yeah, these are just, it's sports betting, etc. And then maybe every two years or every four years, there's some election activity, maybe there's some trades that are sort of like, look like crypto derivatives, et cetera. When you actually look at today's volume of activity, do you see a meaningful shift towards what polite people who wear suits would say like real things? Is that happening?

It is. Yeah. And so when you look at the volumes, obviously there's a real percentage of the volume, more than half of the volume, certainly, is sports. Yeah. And there's a reason for that, right? Sports has been a big market in the United States for a long time. And it's happened in kind of a fractured way across a lot of different states. And there's certain states where you can't do it and it's a different regulatory system everywhere. But there was a big market that existed.

And then prediction markets basically came around. They provided a better way for sports to trade. So it was natural that there was going to be this massive base from the start. But what we're seeing is it's dragging up all of the other things. So it's brought awareness to prediction markets. And the more we see that, the more we see everything else growing. And by the way, a lot of that other stuff, the real market, they're growing at a faster rate than sports just from a lower starting point.

And our goal now is really to boost that with the hedging cases, which, you know, that's growing too. But it's not a huge part of the market as of now. and we understand it needs to be. From an ecosystem perspective, it's essential that those hedging trades become a much larger part of what the market is. So if you're a market maker for an event contract

that's something like the Fed is going to raise rates or hold or lower or whatever, that's pretty similar to something that you might see in traditional markets. But how different, can you like explain it to us from a mechanical perspective, how different the market making process would be for a prediction market contract

versus like a traditional option. Right. So honestly, there's not a one size fits all because there's so many different things out there. So if you're thinking about kind of what's the Fed going to do next, there are instruments that capture that reasonably well. And same thing as, you know,

what's the price of the S&P 500 going to be at the end of today, for example. There are other instruments out there that capture that reasonably well. And really, this is just distilling it into a way that might even better capture the idea someone has. There's, there's like quirks to the other types of things that you might have that are the proxies. So we can translate what's happening in the, in sort of like, you know, the traditional financial markets to prediction markets.

For other products that is sort of, is this random one-off event going to happen in the world? Is the Strait of Hormuz going to be open by the end of August? Is Kier Starrmer going to be out as prime minister? Yeah. We're, that stuff requires a lot of independent research. But the good news is that there's so much information contained within the prediction market itself. So one of the core market making principles we have is, you know, it really honestly comes from, you know, kind of a thing we teach with poker of understand what other people know and what other people are doing. We can learn from the markets, seeing where they are. This is what all these other super forecasters know. We can combine that with our internal research and then get to a number.

So that's, you know, some things kind of have to be, we draw it up and figure it out like that. Some stuff is we can use the information that's in other markets. And some stuff is we're going to build a brand new model to figure this thing out because that's kind of in our DNA to say like, all right, here's a random thing. But, you know, how are we going to, we're going to have to figure out compute, right? Let's figure out how to model compute and, you know, we'll build it. So one of the concerns or one of the things that people talk about with prediction markets is the possibility of insider trade.

And, you know, like, obviously, like, when I think of, like, a market-making firm or some of these firms that do a lot of flow and volume, you just sort of assume there's a lot of noise and it all sort of washes out, right? How does it change you? Like, you have to think, like, if there are participants in the market that are, like, not just forecasters here, like, not just people who are good at predicting things, but deeply informed flow where they maybe just know the answer already. Are you able to spot that? Are you able to sense that? And does that change how you think about risk management within a given market? Right. So I guess the insider trading is definitely something that comes up. And there's certainly been plenty of articles that are written about it. I think there's a couple things to point out there. The first one is that there's actually really two types of prediction markets that it's not always clear to people what the distinction is.

But there's the regulated prediction markets, such as Kalshi, such as Rothair Exchange that just launched, such as, you know, CME has one and Polymarket has a regulated exchange. And then there's the crypto-based decentralized finance platforms. And those are, you know, they don't have KYC, right? And they're crypto-based, they're D-Fi. So I think most of what you've seen, the vast, vast majority where people find something that's insider trading based is on the D-Fi platforms, because there is K-YC-5. on the regulated space.

And that's why, you know, we are participating in the regulated space. So that's the first important thing is if you're in the regulated space, you're much more protected. The other thing is, you know, insider trading is something that does exist everywhere. And there's a mechanism that works to suss it out. And that's reporting from the people that are in the market. Generally, one of the things that we're always paying attention to is what are the incentives of the person on the other side of the trade? And when someone's insider trading, it's a lot more obvious that, okay, well, what are the incentives

they don't make sense? The thing then happens. And, okay, we can make a pretty decent Bayesian update that, like, this was probably insider trading and report it. And it's actually easier in prediction markets because this stuff is more obvious. There's a million reasons that someone can buy Apple stock. But, like, there's not that many reasons that someone can buy, like, is Maduro going to be out?

So if you see a lot of people slamming that. Right. So it's actually easier to spot this stuff. And by the way, like, one thing we're extremely happy about is the DOJ is now even going after the, you know, the crypto-based platforms where, you know, people kind of, I think they kind of knew people probably thought they were safe. You're not safe in crypto land. Actually, everything's on the blockchain, right? So you'll get caught. So there is an infrastructure, especially in the regulated space, but also now in the unregulated space for catching people that do it. And I think that's why you don't see more, like, that's what stops inside our trading. People don't want to get caught. This is new. people probably thought, like, oh, I can do it in crypto and we'll get caught. Now they're like, oh, I am going to get caught. Okay. Aside from insider trading, one of the other concerns or criticisms of prediction markets is the idea of you could have whales, basically, in the market. Like people with a lot of money who are willing to spend it to perhaps influence a particular

probability with the hope of, I guess, influencing the ultimate outcome. You must have pretty good visibility into order flow. Like, how realistic is that concern? from your perspective? Honestly, I don't think it's something that's really come up for us to this point in time. Like, there are concerns we have around certain things in prediction markets, but realistically, like, there's a lot of people in prediction markets with deep pockets, certainly us. And we're pretty good at, as I said, understanding the incentive of who's involved.

And if someone's going out there and trying to move a market to like influence an outcome, you know, if it's, I guess it depends on what the outcome is, if it's an outcome that's easy to to influence. We try to avoid those markets in the first place. So we don't trade like mention markets, right? Someone could put a bunch of money on something and then, you know, on this podcast for apps and then say, Joe, how, here's a little, right? But does everyone is, does everyone know, like a mention market is like, will Joe Tracy or Jeremy say the word like doge coin? Exactly. Exactly. Someone bets like, it's crazy. Right. So whatever. So we're not going to, we're not going to participate in those. If you're worried about something like that, like you probably shouldn't trade

those either. They're more on the side of just like, oh, this, you know, if people want to punt around and then fine, we're not in those. But for more serious markets, I don't have that much concern. If there's someone trying to do that, they're, you know, if it's not manipulable particularly, it's something that's real and someone's just trying to move the market, the marketplace is probably going to figure it out and take the other side of it. And if they can actually influence the outcome in a meaningful way, then it probably need, you probably need to think about what the settlement mechanism is. And, you know, those are the types of markets we try not to get involved in.

But I don't think you see a whole lot of that in the market. So as you mentioned, you're not, you don't make a market in every market and some things are more logical. Do you have, when you say, okay, you're market maker for Kelsey. Do you have a list of like, this is what we do and don't? Or is it like, like, I'm sort of in my mind, the way I think about your role a little bit like a Lloyd's of London in the sense that it's like, okay, so someone has some risk and then they call you up and they like, can you make us a price for this? So is it a set list or is it like you take it

as you see it? Yes, okay, we can make a market. No, this is not something we're going to participate in on a sort of like per market base. Right. So it's a little both. There are some markets that we do all the time. We sign up for obligations. We're going to be out there all the time 24-7. And then there's some things that we kind of do on a more ad hoc basis. Like, you know, it comes up. It's important. We think we can have a meaningful role in the market and we just figure out how to get involved in it.

So a little of both. I don't know if you read it, but there is a piece in, I think it was the New York Times this week about sharps in the prediction market. He definitely read it. Yeah. People who are making a lot of money. Oh, yeah, that's right. Okay.

So you better have read it. I have read it. Okay. But there was someone quoted in there who was like an independent trader in prediction markets. And they said they interviewed at Susquehanna, but the firm said that, like, Susquehanna is not allowed to scrape certain data or it has certain data restrictions. And I guess that made the job unappealing to this guy because he doesn't seem to have taken it. But like, what are your data restrictions exactly?

So we're, you know, we have a large franchise and a large reputation that we need to take care of. So we're not going to violate the terms and conditions of a website, right? If you're a random person who's doing it, like, you probably can do that and you're probably not going to get in trouble more likely than not. Although that's certainly not legal advice. People like putting in URLs that haven't gone live yet, right? And they have some feel and they test them out. It's not even necessarily. I mean, that's a thing that can happen.

But it could just be like, you have a website that has terms and conditions that says like you're not permitted to scrape this data. Right. And so, and like, you know, plenty of people scrape the data, right? And we're not going to scrape that data. Like, we're not going to violate the terms and conditions of a website. So we hold ourselves to a more conservative standard when it comes to those types of things. So I think that person was probably just referring to, hey, you get restrained in some ways if you want to work for us. Institutional, the usual, like, institutional things.

Like, we're going to be very careful when it comes to things around our reputation. But, you know, but I think that that's, you know, there's a lot of advantages to working here as well. I know this isn't a prediction market question per se. But like do GPU markets that we talked about the first half of the show, do they have the sort of contours of what to you look like could be something a very actively financialized market? Absolutely. I mean, it's something that the prediction markets are looking at. And really, when I think about what's made prediction markets so different, like what actually changed? Yeah.

It's the speed to market. So we say more about this. So, you know, it's funny, when we first started wanting to get involved with prediction markets, I have like a little bit of an origin story where I have a friend who is a CFO of a musical instrument company. During the first China trade war, he was worried that their company might go out of business because they imported instruments from China. And I was like, well, I'm a macro trader. I could hedge this. It's what I do all the time.

We actually tried to list a product, and it took about a year. And it was just too slow. And then, you know. Where did you try to list it? We tried to list it with my ex. And, you know, just that was the process for listing a future. And now we got, now we, you know, prediction markets came around. And that process went to a day or even inside of a day.

So I think really that's the most, that's a real valuable thing that happened with prediction markets. And I think that with compute, you see this, you see this ability of prediction markets to potentially move very quickly. So they can launch a product. There's, there are products on prediction markets that have compute. these other things that kind of look like compute, you might say DRAM prices. People worry about that all the time, right? The shipping costs, these things that are kind of like a commodity,

but they don't really have a commodity. Prediction markets can be speed to market, and the system works in that way. So I absolutely think. It's something we're thinking about, and I think they can totally have a role in that ecosystem. So just going back to the very beginning of this conversation, your head of macro trading at Susque, sorry, I can't say head of macro without cracking up. Your head of macro trading, but also prediction markets at Susquehanna, which is like kind of an unusual combined role. Like, what is the idea there? Is there some synergy between those two markets that you're trying to capture?

You know, honestly, it all just comes down to the election. You know, as head of macro, I had sort of a niche for trading the election. And I kind of got exposed to prediction markets because, they were trading in Europe on betfair and we would be we had you know European entity and we were involved with it there and I saw the value of it and I think 2016 election was like a place that it was really valuable because people had these massive risks they wanted to hedge people would do it with these proxy products banks would put out baskets of like do all these put put together all

of these names and the broad market consensus in 2016 was that the market was going to be down five to seven percent if Trump won and it turned out the market It was up. It was for like five minutes. It was for like five minutes. It was just five minutes. But yeah, by the end of the next day, it was up, right? And so the, you know, the hedge didn't work.

But we saw like the prediction market worked. Like if you actually just hedged this in a prediction market, it would work really well. And so honestly, there's not, it doesn't seem like there's that much synergy. It's more just it evolved out of that of saying like, hey, we're involved in this space and we see how valuable prediction markets can be. And we really want to make it happen. And so I think that it's really just. kind of an evolution as opposed to this is how we would draw it up from scratch.

Every once in a while I get sent one of those like basket trades where it's like trade this basket we could do $100 million. Right. This, you know, D-Ram winners, D-Ram losers basket. Anyway, Jeremy Mallet's head of prediction markets in Susquehanna. Thank you so much for us. Thank you so much for us. That was our conversation with Jeremy Mallets of Susquehanna recorded live at our New York show. I'm Tracy Alloway. You can follow me at Tracy Gailaway. And I'm Joe Wisenthall. You can follow me at the stalwart. Follow our producers, Carmen Rodriguez at Carmen Armin, Dashel Bennett at Dashbot, Kale Brooks at Kail Brooks, and Kevin

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