This article was compiled and organized by BlockWeeks
The success of prediction markets demonstrates that there is strong market demand for financial instruments that can link specific events to outcomes. Impact Markets (Impact Markets) is designed precisely for this purpose, using a conditional pricing mechanism to translate traders' beliefs into direct expressions of conditional valuations. The latest in-depth report released by Galaxy Research systematically analyzes the operating principles of Impact Markets and compares them with traditional prediction markets, pointing out their significant advantages in information expression, risk control, and capital efficiency.
I. Limitations of Directional Prediction Markets
Traditional "will rise/fall" prediction markets only aggregate unconditional beliefs about the direction of price movements, ignoring the magnitude, convexity, and dispersion of the movements. For example, the following two scenarios would produce the same "rise" outcome:
- Bitcoin rises 1% with 90% probability;
- Bitcoin rises 40% with 10% probability.
Directional markets treat these two vastly different risks as equivalent, thereby losing a great deal of information crucial for decision-making. Moreover, when two traders agree that Bitcoin "will rise" but disagree on the magnitude of the rise, directional markets mask this disagreement, creating an illusion of false consensus.
II. Information Loss and Inference Costs
Even if traders hold precise conditional views, directional prediction markets cannot encode them into the market. For example, a trader believes that "if the Federal Reserve cuts rates by 75 basis points, Bitcoin is worth $110,000," but in a market asking "if the Federal Reserve cuts rates by 75 basis points, will Bitcoin rise?" they can only choose "yes" or "rise," which inevitably loses key information such as conditional valuations and probability distributions. Traders must reconstruct the market's implied meaning on their own outside the market, adding extra reasoning costs.
III. Conditional Execution Improves Risk Definition
Directional prediction markets require traders to expose their full principal risk before an event occurs; even if their judgment about the event's impact is correct, as long as the event does not occur, the principal faces losses. Impact Markets reverse this structure: capital only bears risk when the specified event occurs; if the event does not occur, the trade is closed and the principal is not exposed to irrelevant states. This conditional execution allows traders to purely express the belief that "under a certain condition, the asset value is Z" without simultaneously speculating on whether the event will occur.
IV. How Impact Markets Aggregate Conditional Beliefs
Impact Markets do not attempt to predict price direction, but rather reveal the market's central expectation of asset value under given conditions. What they aggregate are cardinal beliefs (such as "under this condition, the asset is worth Z dollars"), rather than ordinal beliefs (such as "the probability of rising is greater than falling"). This mechanism makes valuation dispersion directly visible rather than hidden behind a unified symbol, thereby providing richer information for decision-making.
V. Markets Already Price in Interaction Effects
In reality, events rarely occur in isolation. Rate cuts are often accompanied by economic slowdowns, and regulatory actions often coincide with political changes. Spot prices already reflect the combined expectations of multiple driving factors. Impact Markets make this aggregation transparent by explicitly specifying conditional events: traders clearly know what is being priced, disagreements are directly reflected in prices, and conditional valuations under different scenarios can be directly compared. In contrast, methods that combine directional prediction markets with off-market models often hide assumptions within private models, lacking transparency.
VI. Conditioning Narrows Uncertainty While Complexity Persists
Conditioning does not eliminate uncertainty, but it can narrow its scope. Taking Google (GOOGL) as an example, its price over the next month could span thousands of macroeconomic, regulatory, competitive, and geopolitical paths. However, once conditioned on the event "GPT-6 releases next week," the probability distribution narrows substantially, although some uncertainty remains. This residual risk is precisely what market design aims to price.
VII. The Most Informative Scenarios for Impact Markets
Impact Markets are most informative when conditional events can substantially restructure the probability space. Typical scenarios include:
- Elections and major political outcomes;
- Central bank and fiscal policy shifts;
- Platform launches and technological breakthroughs;
- Cryptocurrency protocol upgrades or forks.
In these situations, conditioning not only provides price discovery but also guides capital allocation toward specific outcomes, enhancing the overall market's efficiency and resilience.
Conclusion
Although Impact Markets are still in their early stages of development, their payoff structure derives from standard financial principles rather than purely speculative theory. It inherits precedents from decision markets and prediction markets and is being explored by teams such as Lightcone and Butter. As the market's demand for more refined risk expression tools continues to grow, Impact Markets are expected to become an important component of the next generation of crypto asset pricing infrastructure.
