Impact Markets: Addressing the ‘How’ and ‘Why’ of a Prediction Market Spinoff

Introduction

This month Galaxy Research published a high-level explainer report on Impact Markets and Decision Markets. It provided insight into what these markets are, the use cases they solve for, and how they map back to prediction markets. In past reports we covered the why and how of Decision Markets but did not do the same for Impact Markets. The following covers these points for Impact Markets through the lens of questions and critiques some have made of them. Notably, these three points have been raised:  

1. Why not just use “Will go up/down if occurs?” prediction market instead? 

2. Markets are multi-dimensional; how can individual conditions be adequately isolated in complex markets? 

3. Why do prediction markets’ success imply demand for Impact Markets? 

Impact Markets are nascent, but their payoff structures follow from standard financial principles rather than pure speculative theory. They build on precedent established in Decision Markets and prediction markets, and are being explored by a couple teams, including Lightcone and Butter. For background on Impact Markets and how they work refer to this Galaxy Research report. 

Why Not Just Use 'Will Go Up/Down if Occurs?' Prediction Markets Instead? 

Impact markets and prediction markets encode fundamentally different economic objects. Directional prediction markets aggregate unconditional beliefs about movement. Impact Markets aggregate beliefs about value conditional on a specified event occurring. This difference determines what information can be expressed, what risks are taken, and what decisions the resulting signal can support. 

Directional Markets Collapse Distribution Into a Sign 

An up/down market answers only the sign (direction) of the expected price move, not the distribution of possible outcomes. That distinction matters economically.  Two very different scenarios can produce the same “Up” outcome:  

• BTC rises 1% with 90% probability 

• BTC rises 40% with 10% probability 

 A directional market treats these outcomes as equal, even though they imply much different:  

• optimal position sizing 

• hedging strategies 

• capital allocation decisions 

• tail risk exposure 

By default, directional markets discard magnitude, convexity, and dispersion. Impact Markets, by contrast, do not attempt to predict direction. Instead, they surface where the market believes the conditional distribution of value is centered. This is not just a richer opinion about the same question, but a different one entirely. 

Up/Down Markets Remove Cardinal Information and Overstate Consensus 

Prediction markets are effective at aggregating ordinal beliefs such as “more likely than not.” Impact Markets aggregate cardinal beliefs such as “the asset is worth $Z under this condition.” 

This difference has important coordination consequences. Two traders can agree that BTC “goes up” while disagreeing on how much. Directional markets make those traders appear aligned, even though their economic beliefs, risk tolerances, and optimal downstream actions are fundamentally different.  

By forcing disagreement into a binary outcome, up/down markets systematically overstate consensus. Impact Markets force disagreement into price discovery. Dispersion appears directly in valuation rather than being hidden behind a shared sign. That dispersion is often the most relevant information for decision making. 

Directional Prediction Markets Are Informationally Lossy by Construction 

Even when traders hold precise conditional views, directional prediction markets cannot telegraph them. 

 A trader who believes “BTC is worth $110,000 if the Fed cuts 75 basis points” has no way to encode that belief directly into a “Will BTC go up if the Fed cuts 75bp?” market. The best he or she can do is vote “Yes” or “Up,” which necessarily discards:  

• magnitude 

• convexity 

• tail beliefs 

 This loss is a consequence of the market design. Once a conditional valuation is collapsed into a binary outcome, the missing information cannot be inferred.  As a result, traders must reconstruct what the market “really means” off-market by layering assumptions about:  

• conditional probabilities 

• correlations 

• timing 

• basis risk 

Impact Markets eliminate this inference step by allowing traders to express conditional valuations directly. The market price is the object of interest, not an input into a separate modeling pipeline. 

Conditional Execution Improves Risk Scoping 

Directional prediction markets impose unconditional capital risk. Once a position is entered, principal is exposed regardless of the outcome. Even if a trader’s view about an event’s impact is correct, they lose their stake if it does not happen. This is an inherent limitation of prediction markets’ design. 

Impact Markets invert this structure. Capital is put at risk only if the event occurs. Trades are executed conditionally:  

• if the event does not occur, the trade is unwound 

• principal is not exposed to unrelated states of the world 

• risk is scoped precisely to the scenario the trader intends to express 

 This allows traders to express views such as “BTC is worth $110,000 if the Fed cuts 75bp” without being forced to speculate on whether the Fed will cut at all. In prediction markets, those two beliefs are inseparable. In Impact Markets, they are cleanly decoupled. 

The result is an improved risk/reward profile. In prediction markets traders are effectively just speculating on direction and probability and can lose principal regardless of how correct their economic intuition is. On the other end, Impact Markets settle conditionally and align payoff and risk with an event occurring. 

Directional Markets Cannot Produce Hedge-Equivalent Payoffs 

Direction markets can generate the 10x-plus returns that hook some users into prediction markets, but in an up/down market, the payoff is orthogonal to the underlying exposure.  In other words:  

• Winning the prediction does not offset losses on the asset 

• Losing the prediction does not cap downside (losing bets go to 0) 

• The hedge ratio (hedge value to position value) is undefined 

This makes these markets closer to speculative complements rather than hedging instruments. Impact Markets, by contrast, produce economically isomorphic payoffs:  

• The conditional trade directly offsets (or locks in) the asset outcome 

• The hedge is exact by construction 

• No post-hoc rebalancing is required 

 The distinction is structural. 

Directional Markets Collapse Causality into Correlation 

“Will BTC go up if Z happens?” expresses a causal intuition through a correlational instrument. Directional prediction markets can only encode whether two variables move together, not what the event implies for economic outcomes. Even when traders hold rich causal beliefs, the market collapses those beliefs into a binary sign.  Impact Markets do not attempt to isolate the pure causal effect of Z holding all else constant. Instead, they force traders to price the expected economic outcome in the world where X occurs, incorporating all anticipated second-order responses and interacting forces. The distinction is not between clean causality and noise, but between pricing consequences versus signaling direction.  This distinction matters most in regimes where:  

• multiple events happen at the same time  

• second-order effects dominate 

• policy actions, competitive responses, or regulatory shifts interact 

In such environments, directional markets blur heterogeneous mechanisms into a single yes/no outcome, obscuring magnitude, dispersion, and downstream implications. Conditional pricing does not eliminate complexity, but it channels it into an explicit economic object: the asset’s value in a specified state of the world.  

As a result, Impact Markets do not claim causal purity. They offer economic specificity. They replace coarse correlation with conditional valuation, allowing markets to express what an event is worth rather than merely whether it coincides with price movement.  

This leads us into the next section covering the complexity of markets, the place Impact Markets have in them, and the pricing problem they solve for. 

Markets Are Multi-Dimensional; Conditional Pricing Does Not Require Causal Isolation 

Markets are already multi-dimensional. Asset prices reflect the aggregation of thousands of interacting uncertainties, none of which are priced in isolation. Impact Markets do not attempt to decompose this complexity into clean causal channels. Instead, they introduce explicit conditioning, asking what an asset is worth in the branch of reality where a particular event occurs. The purpose is not to eliminate complexity and introduce perfect causality, but to remove the largest and most consequential uncertainty. 

Impact Markets Price Conditional Worlds, Not Isolated Causes 

Impact Markets do not attempt to measure the pure causal effect of an event holding all else constant. No market does; they price outcomes, not mechanisms. 

An Impact Market instead asks a more realistic question: what is the expected value of an asset in the world where a specified event occurs? That valuation integrates all anticipated second-order responses and interacting forces as they are expected to play out in that world.  

For example, an Impact Market for “GOOGL | GPT-6 launches next week” does not claim that GPT-6 alone causes GOOGL to be worth a specific amount. It claims that in the world where GPT-6 launches next week, given everything else the market expects to occur in and around that scenario, GOOGL is worth $Z. 

Markets Already Price Bundles of Interacting Effects 

In practice, events rarely occur in isolation. Rate cuts occur alongside macro slowdowns. Regulatory actions occur at the same time as political shifts. Product launches trigger competitive and policy responses. 

Spot prices reflect bundles of interacting expectations, collapsing many individual drivers into a single price outcome.  

Impact Markets simply make this aggregation explicit by specifying the conditioning event. This improves transparency because traders know exactly what is being priced, disagreements surface directly in price, and conditional valuations across scenarios can be compared. 

The alternative workflow of combining directional prediction markets with off-market models does not produce cleaner causality. It hides assumptions inside private models that are invisible to other participants. 

Conditioning Narrows Uncertainty Even When Complexity Remains 

Conditioning on an event does not eliminate uncertainty, but it narrows it. 

The future price of an asset like GOOGL spans thousands of possible macro, regulatory, competitive, and geopolitical paths. Conditioning on a salient event removes a large subset of those timelines from consideration and forces traders to reweight the remaining ones. 

“GOOGL next month” is a wide distribution. “GOOGL next month | GPT-6 launches next week” collapses that distribution substantially, even though many uncertainties remain. 

The remaining uncertainty is residual risk, and markets are designed to price it. 

When Impact Markets Are Most Informative 

Impact Markets are most informative when the conditioning event materially restructures the probability space rather than resolving all uncertainty. This typically includes:  

• elections and major political outcomes 

• central bank and fiscal policy shifts 

• major regulatory actions 

• platform launches and technological breakthroughs 

• cryptocurrency protocol upgrades and hard forks 

Such events do not eliminate uncertainty, which is a fact of life, but they reorder it. Impact Markets surface how that reordering translates into economic value.  Impact markets on less consequential events are not entirely useless, but they may trade thinly or converge close to spot. That outcome is itself informative. The market is signaling that conditional on this event, the world does not look meaningfully different. 

Why Do Prediction Markets’ Success Imply Demand for Impact Markets? 

Because the highest-volume prediction markets cluster around events with large economic consequences, their success reveals latent demand for instruments that map events to outcomes. Impact Markets satisfy that demand more fully by offering a more expressive way to directly price and hedge those consequences. 

Prediction Markets' Success Reveals Latent Demand for Conditional Economics, Not Just Probabilities 

The strongest evidence there is demand for Impact Markets is not total prediction market volume. It’s where that volume concentrates and why users participate.  Empirically, the largest and most persistent prediction markets (outside of sports markets) cluster around:  

• elections 

• central bank decisions 

• major regulatory actions 

• leadership changes 

 These are not just popular because users enjoy betting on politics. They are popular because they:  

1. materially affect asset prices, businesses, and balance sheets 

2. are difficult or impossible to hedge cleanly in traditional markets 

Prediction markets succeed where event risk intersects with economic exposure. That intersection is the natural entry point for Impact Markets. 

Volume Concentration Signals a Demand for Expressiveness 

The fact that the average volume per market type concentrates in a small number of macro and political markets is itself telling. This is revealed through average notional volume per market type (excluding sports markets).

It is also revealed in outright notional volume by market type (again excluding sports).

If users purely wanted: 

• intellectual forecasting 

• entertainment 

• pure probability estimation 

... volume would be more evenly distributed across market types. Instead, it’s clustered around events with large economic surface areas. This suggests:  

• demand scales with downstream economic consequence 

• users are willing to pay for instruments that map events to outcomes 

 Impact Markets do exactly that. 

Prediction Markets Are Being Used as Crude Economic Proxies 

In practice, many users already treat prediction markets as imperfect economic instruments. Examples:  

• A BTC holder buying yes contracts for an anti-crypto presidential candidate 

• An equity investor hedging election outcomes via political markets 

• A fund expressing macro views through policy-linked bets 

 These users are not primarily seeking probabilistic truth. They are:  

• hedging exposure 

• managing risk 

• approximate conditional exposure using unconditional markets 

Prediction markets “work” here only because nothing better exists. Impact Markets offer a more expressive instrument for the same intent. They are the saxophone to prediction markets’ kazoo. 

Conclusion 

Impact Markets are more than just an incremental improvement on prediction markets. They are a structural extension.  

Where prediction markets collapse rich conditional beliefs into binary outcomes, Impact Markets price the conditional world itself. This allows markets to express magnitude rather than direction, disagreement rather than false consensus, and economically hedgeable payoffs rather than orthogonal “wagers.”  

The success of prediction markets around elections, policy decisions, and regulatory actions signals not just demand for probabilities, but demand for instruments that map events to outcomes. Impact Markets meet that demand by making conditional valuation explicit, event-bounded, and directly actionable.