The recent surge in popularity of prediction markets like Kalshi and Polymarket has sparked a lot of discourse online about the nature of Prediction Markets. Those bullish on Prediction Markets (like myself) believe they will continue to grow in importance, while some bears doubt the premise of these event contracts, often with the same objection: Prediction Markets will never be "real markets" because they're just casinos for speculators, with no hedgers in sight. That critique has a grain of truth about where these markets are today, but it mistakes an early stage of market design and institutional adoption for a fundamental limitation.
1What is a Market?
In "finance-bro" speak, a mature market is a mechanism that aggregates dispersed information into a single risk‑neutral expectation or implied probability, and reallocates risk from agents who are risk‑averse / constrained, to agents who are better positioned to bear that risk.
In other words, markets have 2 functions:
- Aggregates information into prices
- Transfers risk from those who don't want it to those who can bear it
Traditional derivatives markets (FX, rates, commodities) do this with a mix of hedgers and speculators. Producers, importers, and borrowers use futures and options to reduce the volatility of their cash flows; speculators and market makers take the other side and earn a small premium for warehousing risk and providing liquidity.
Prediction markets already are pretty good at the first part: turning fragmented beliefs about elections, regulatory decisions, or product launches into a public, continuously updated probability estimate. The open question is whether they can also become venues where real players (not just college kids) hedge the risks that actually matter to them.
2Existing (but primitive) ways to Hedge
Consider a studio releasing a major film, let's say Marty Supreme (shoutout TC!). From a balance‑sheet perspective, the studio is structurally long the movie's success. Its cash flows are highly sensitive to whether global box office lands at 400M, 1B, or 1.5B, very similar to an airline being structurally long jet fuel: both face a risky distribution of outcomes tied to a single underlying.
Now imagine there is a liquid event contract that pays 1 if Marty Supreme grosses at least 1B worldwide by a certain date, and 0 otherwise. The studio could:
- Size a short position in that contract so that, for example, 20–30% of the financial pain from a box office miss is offset by gains on the hedge.
- Accept a lower best‑case payoff (because it will lose on the contract if the movie is a smash hit) in exchange for a narrower, more predictable range of outcomes.
In finance-bro terms, the studio is reducing cash‑flow volatility and lowering the probability of a really bad outcome, rather than trying to maximize expected value. This is a classic hedge, and is exactly what firms do when they sell production forward in commodity markets or use options to hedge FX and interest‑rate risk. The instrument is different (event contract instead of a futures strip) but the economic logic is the same.
Once you see the movie example, other event‑hedging use cases pop out immediately:
- A tech company hedging the probability that a specific regulation passes or an antitrust suit succeeds.
- A pharma firm hedging the binary outcome of an FDA decision or Phase III trial.
- A defense contractor hedging the odds of a key budget authorization or contract award.
If hedging is "taking the opposite side of a risk you already bear in your business," then there is nothing conceptually stopping prediction markets from hosting hedgers. The current dominance of speculators is a contingent fact about adoption, not a structural impossibility.
A second advantage of prediction markets is that they allow traders––and eventually hedgers––to trade the event itself rather than a noisy proxy. In traditional markets, expressing an event thesis almost always means going through an equity, an index, or a commodity whose price reflects a messy blend of forces:
- Systematic risk (market beta, sector moves, macro shocks).
- Factor exposures (value vs growth, size, momentum).
- Firm‑specific shocks (management changes, product news, accounting surprises).
Let's look at this example:
Suppose you believe a specific congressionally driven policy will boost gold. Buying a gold miner's stock exposes you not just to the policy, but also to equity‑market sentiment, cost inflation, operational execution, and broader risk‑on/risk‑off flows. Trading firms already try to strip out these unwanted factors with hedge overlays, shorting indices, FX, or sector baskets in an attempt to isolate a single catalyst. But, even with careful construction, you are still wrestling with a bundle of risks only loosely related to your original thesis
Event contracts on the other hand can (potentially) cut directly to the underlying thesis:
"Bill X passes by date Y."
"Central bank cuts by at least 50 bps this year."
"Marty Supreme grosses ≥ 1B by end of Q4."
The payoff is dominated by whether that event occurs, not by factor rotations or unrelated macro surprises. In portfolio language, you are swapping a noisy mix of systematic and idiosyncratic risks for a more concentrated, but cleaner, exposure to a single event variable. This does not mean event markets are "safe"—they are often binary, with a high chance of a large loss if your thesis is wrong. But for both hedgers and speculators, they can be less noisy: the P&L tracks what you actually care about, rather than being overwhelmed by unrelated shocks.
3Event-Derivatives for Prediction Markets
Now of course, in other mature markets it's rare to see firms hedging by directionally trading the underlying. The vast majority of the time, they use derivatives (like options, futures, swaps) to hedge, because those instruments let them target specific risks, preserve their exposure, and do it with far less capital than buying or selling the whole position. However, recent developments in this space have introduced new potentialities for this type of instrument.
A recent paper from the Daedalus Research team (linked below) proposes exactly the kind of solution prediction markets are missing today: a Black-Scholes style framework for event contracts and an entire derivatives layer on top of them. The core idea is to treat the probability of an event (which is what a prediction market is really trading) as a first‑class state variable, model its log‑odds with a jump-diffusion process, and then define "belief volatility", jump risk, and cross‑event correlation as standardized risk factors that can be quoted and hedged.
Toward Black–Scholes for Prediction Markets: A Unified Kernel and Market-Maker's Handbook — Daedalus Research Team
The paper is full of jargon and math that I won't pretend to fully understand, but concretely, an event‑derivatives layer could have massive implications for prediction markets as a whole. With the Daedalus framework, it could include belief‑variance swaps (contracts whose payoff increases when the market's implied probability has bounced around more than expected over a set period), corridor variance notes (kinda similar, but they only "count" volatility when the market is in a mid‑range band like 30–70%, where opinions are genuinely shifting), and correlation swaps between related events (contracts whose payoff depends on whether two probabilities have moved together more or less than expected).
The movie studio could use a belief‑variance swap on "Marty Supreme ≥ 1B" to hedge how turbulent expectations are during the marketing campaign, while still using the base event contract to insure against an outright flop; a large tech firm could use correlation swaps across several regulatory outcomes to dampen the combined impact of bad legal news without exiting its core business.
Together, these instruments would let firms manage not just "will this happen?" risk of the underlying, but also how violently + how jointly beliefs about those events evolve over time, bringing prediction markets much closer to the risk management tool kit that already exists in FX, irates, and commodities.
More on this topic coming soon!