In this episode of Professional Punters, we sit down with Daniel Zheng, who has already worked across a wider slice of finance than most people do in a decade.
Daniel traded fixed income at a top investment bank and analyzed public and private investments at a tech-focused, single-manager hedge fund. He was also forward-deployed at Palantir and has a research background in development economics. He is now a startup founder in prediction markets, building low-latency trading infrastructure to sports markets.
This conversation is about what actually separates a bank from a hedge fund, the esoteric risks bank traders are paid to warehouse, how the AI data-center buildout is being financed, and where the edge really lives in prediction markets, from Strait of Hormuz contracts to correlated parlays and courtside live betting.
Banks Move Risk, Hedge Funds Take It
The cleanest way to understand the two institutions is by their relationship to risk. A bank, to some degree, is an institution that does not really try to take risk itself (partially due to regulatory constraints post 2008). It is in a sense a market maker: it wants to buy something, sell something, and collect a fee for standing in between.
A [discretionary] hedge fund is structurally built to take risk. It has a fundamental view on how a company or an asset will perform, and its investors give it money precisely to make that bet. At the fund where Daniel sat (long-biased, longer-duration) the work looked closer to traditional value investing: is this company going to beat earnings, is it actually in structural decline because of AI, or is the story overblown.
Banks Also Warehouse Esoteric Risks
In an ideal world, a trader at a market-making firm goes home with close to zero exposure. In practice that is almost impossible for a dealer in anything even slightly exotic or unlisted. So a lot of bank traders are essentially paid to warehouse esoteric risks, on an OTC or bilateral basis.
The best example is the deal-contingent hedge. Investment banks take companies public and finance M&A, where lenders borrow vast sums to buy a company that may be listed on public markets. Both prices move minute to minute: the equity through the stock market, and the cost of borrowing through the capital and money markets.
A macro shock may send the funding rate through the roof; the economics of the deal change, the buyer wants to pull out or the seller wants to force it through. A deal that takes six-plus months to close carries real timeline risk the whole way. The bank gets called in to hedge all of it with over-the-counter swaps across both the equity and bond markets. It is genuinely exotic risk, and it is warehoused rather than offloaded, because it is very hard to package into a product.
The Blurry Regulatory Line
So where is the line between market making and prop betting? Daniel’s answer: capital markets facilitation. Where you are directly supporting the investment banking practice, is something very few regulators take issue with. The rough rule is that speculation is not okay, but risk taken on in the facilitation of genuine economic activity or capital formation is. In addition, banks may use intermediaries to distance themselves from risk to be extra safe.
Mortgages are the cleanest illustration of how the machine got rebuilt around that rule. Banks mostly do not structure mortgage-backed securities themselves anymore. Instead they provide private credit financing to the firms that do (though these private credit firms are at times highly leveraged).
A mortgage-backed security, scary as the phrase sounds after 2008, is at some level just a giant pool of mortgages and a relatively safe investment. Lending to the structurer instead of structuring is safer, since there is another level of protection.
While the bank do still stay fundamentally exposed to some mortgage risk, regulators allow for this structure as banks are the ONLY financial intermediaries with sufficient capital to finance the US housing market.
Securitizing the AI Buildout
The most interesting thing happening in credit right now is the data-center buildout. If you pay close attention to Bloomberg you see such deals constantly, such as the one that Meta just closed with Blue Owl.
The logic is that very few capital providers, even the Blackstones of the world, are willing to take down $50 billion of data-center risk by themselves. So consortiums of banks lend alongside private credit providers, facilitating the creation of bonds backed by the data centers, the land, the revenue, the chips (i.e. GPU-backed loans), and ancillary equipment.
Is there a risk that new chips make today’s GPUs depreciate faster than the pricing assumes? Always. But Daniel’s more cynical observation is that much of private equity already faces exactly that risk elsewhere: B2B SaaS was underwritten as bulletproof recurring revenue, sometimes with loans literally made against ARR, and the advent of AI has put a lot of those prices into question.
The primary concern right now is simpler. Google, Anthropic, and OpenAI want to build as many data centers as possible, and the system’s job is figuring out how to finance that.
“Bonding” in Prediction Markets
Daniel trades prediction markets personally. His framework is that there are a few ways to make money, and they are the same in financial markets and prediction markets. One such way is being paid to take on risk.
At the time of recording there was a contract on whether the Strait of Hormuz returns to its pre-war import and export state by mid-July, with the market around 10 to 20%. Take the other side and $100 can make you roughly $120 in a month. But a high rate of return is not the same thing as a good bet. There is a legitimate chance the strait does not reopen, and you get hosed. Eight times out of ten you make money. The other two, you lose everything.
Nonetheless, some markets fundamentally inefficient. There is a contract on whether the Shah’s heir returns as Iran’s next political leader within the year. There is legitimately a chance it happens, but the market pays around 5% by year-end, roughly 10% annualized, way more than the risk-free rate of the US Treasury market.
That price is less a reflection of the actual probability than of market structure: some people really want to make that bet, and market makers do not want to warehouse long-duration risk for a limited gain.
This is why you see people making 100 to 200% a year on these markets, and a whole class of freebies yielding 5 to 15% from selling very-low-probability events that are still overpriced. The edge persists because there are so many other edges competing for attention, and diversified across enough contracts the positions are close to uncorrelated. The caveat worth flagging: the trade only works if your cost of capital is below that 5 to 15%, which is not always true for a lot of people.
Sports Betting Combo Market Structure
Prediction markets found their product-market fit in sports trading. On Kalshi it is still over 90% of volume one way or another, either directly through sports contracts or through combos, which are parlays with subtle differences (every sportsbook resolves parlays in its own way).
The structural difference is that combos on Kalshi and Polymarket are quoted through an RFQ system. You structure the bet yourself, say the Seahawks, the Ravens, and the Commanders across games, put the request out to the world, and market makers quote you a price.
Intrinsically that is a little more adverse; the market maker is trading a unique esoteric bet rather than a liquid contract, and only gets to trade it if other market-makers do not. In addition, the user keep the last look as they do not have to trade after the RFQ comes back. Nonetheless, on average the pricing produced by this system is still significantly better for users than what they get on sportsbooks.
The quoting side is more nascent than you would guess. Most combos on Kalshi today are quoted by much smaller independent traders, not large integrated firms, partly because the concept is only about three months old.
On collateral, smaller firms post close to full collateral while large market makers get preferential terms, and Kalshi now pays interest on balances at roughly 25 to 50 basis points below the risk-free rate. Your edge on the parlay quote has to overcome your firm’s cost of capital minus that rate, though this is probably not hard as most parlays expire within a week anyway.
Edge in Implied Correlations
Part of the reason dealers are compensated so much for quoting parlays is that they take on an extra dimension of correlation risk (two 50% legs do not make a 25% parlay when the legs are correlated).
Daniel talks about a case where, back in the early days of DraftKings, bet the under on every single player in a basketball game and the over on the team total would have made money. Those legs are obviously highly [anti]correlated: it is essentially impossible for the team to hit the over if every individual player hits the under.
However, the parlay was completely mispriced, treated as if it had a literally zero percent chance of happening (instead of merely very small), so on the rare occasions the combination did hit, the payout was something like 1000x at a time. In this case, the allowed betting size would have let a user make on the order magnitude of hundreds of thousands of dollars in total.
That is less an indictment of one book than of the category. Sportsbooks are known for being pretty bad at trading in general. Most of the company is focused on distribution and getting users onto the platform, and the business model is taking a cut of retail bets, not surviving a competitive landscape where shark hedge funds chase down mispricing.
Courtside Beats the Couch
Around six months ago, a big phenomenon in the development of sports betting on Kalshi and Polymarket was people going to games in person and live betting with literal knowledge of what was happening on the floor. Watch Steph Curry hit the three in front of you and you are trading ahead of the market. In the early stages of prediction markets, simply being at the game made you one of the fastest traders in the market.
The flip side: betting off your TV is a bad bet. Depending on where you are in the US, the broadcast is 20 to 30 seconds delayed, so the person you are trading against already knows what happened. There is still a very large volume of people who bet from what they see on TV anyway.
Market makers adapted quickly. Small size arriving very early in a move is probably someone sitting in the arena trading on their personal calculus. Some market makers pay for professional data feeds, and a whole industry has been built around the collection of sports data. If you trade within one or two seconds, the market probably has not fully priced it in. Wait five and you are late. Even DraftKings, which might not know exactly what is happening in the game, knows enough to anticipate volatility, lengthen its lines, and prevent you from trading.
Be Smarter, Be Faster, or Cheat
When the short-duration crypto markets first came out on Polymarket, the 5-minute and 15-minute Bitcoin contracts were at one point literally based off a single Binance price. A large individual whale could buy one side of the “Bitcoin up” contract, manipulate the market to push the indicator down for a while, accumulate even more shares, and then fire a huge buy order on Binance at the very last minute so the contract resolved their way. This happened with some regularity in the earlier Bitcoin markets, and people made quite a bit of money. It is also definitely illegal in any type of regulated financial market.
Which is the note the conversation ends on. As the line from Margin Call goes: be smarter, be faster, or cheat. There has been no new idea in trading in the last twenty years, just the same concepts implemented in new markets, and prediction markets are simply the newest place the old game is being played.
Professional Punters is presented by Freeport Markets: Freeport lets users trade 24/7, weekends included, with no KYC and up to 200x leverage across stocks, indices, commodities, crypto, rates, and more, with access to pre-IPO exposure in companies like OpenAI & Anthropic. For traders who already follow sharp sources across Twitter, Substack, hedge fund filings, corporate insider activity, and political trading disclosures, Freeport uses AI to read the sources they trust, connect the dots across markets, and surface trade ideas as narratives develop.
About the Host: Lihong Wang is the founder and CEO of Freeport Markets. Before starting Freeport, he traded discretionary semiconductor names at quantitative market-making firms including Jane Street and IMC Trading, building both systematic and discretionary strategies. Lihong graduated from Duke with a degree in mathematics and statistics, and has raised over $2.5M to build Freeport from investors including Y Combinator, Alliance DAO, and Informed Ventures. For more of his long-form research, visit freeportlogbook.substack.com.
Disclaimer: The information provided on TheLogbook (the “Substack”) is strictly for informational and educational purposes only and should not be considered as investment or financial advice. The author is not a licensed financial advisor or tax professional and is not offering any professional services through this Substack. Investing in financial markets involves substantial risk, including possible loss of principal. Past performance is not indicative of future results. The author makes no representations or warranties about the completeness, accuracy, reliability, suitability, or availability of the information provided._
This Substack may contain links to external websites not affiliated with the author, and the accuracy of information on these sites is not guaranteed. Nothing contained in this Substack constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments. Always seek the advice of a qualified financial advisor before making any investment decisions.
