What are Prediction Markets?

“The primary goal of a prediction mechanism is to obtain and aggregate dispersed information, which often exists in tacit forms as beliefs, opinions, or judgements of agents. Coalescing information is a necessary first step for decision making in almost all domains.” — Yiling Chen and David M. Pennock in Designing Markets for Prediction

Prediction markets exist to aggregate dispersed information. In practice, they let people with different data, models, and priors trade event contracts. The trading price then approximates a consensus probability for the event in question. Contract prices continuously update reflecting the changing market sentiment. Decades of work show why this mechanism is useful: it gives participants an incentive to reveal what they know, and it produces an up‑to‑date forecast as new information arrives. In short, prediction markets are designed to coalesce tacit beliefs into a public probability that decision‑makers can use.

The academic record is encouraging. Markets typically incorporate information quickly, are difficult to manipulate for long, and often match or surpass conventional forecasting methods. In 2002 Goldman Sachs and Deutsche Bank launched Economic Derivatives, a dynamic parimutuel prediction market to enable hedging around core economic risk indicators like CPI or nonfarm payroll statistics. Prices yielded slightly lower forecast errors than professional forecasters across multiple indicators, with a pooled reduction of roughly 5–6% and statistically significant improvements. In election forecasting, market prices have tended to beat polls over the run‑up to election day and encapsulate much of the information in de‑biased polling averages. These findings are representative of a broader literature documenting fast updates, few arbitrage gaps, and resilience to attempted manipulation.

Why Focus on Forecasting Bitcoin?

Bitcoin is the only digital asset that combines credibly neutral governance with an auditable, rule‑based monetary schedule. It launched without insiders, has no continuing issuer, and operates as a leaderless monetary network—features that make it unusually resilient and policy‑agnostic compared with “company monies” or discretionary fiat regimes. On the mechanics, Bitcoin’s supply is capped at 21 million and released on a pre‑committed halving cadence enforced by proof‑of‑work. From a monetary‑economics lens, this makes Bitcoin a paradigmatic “rules‑based” or “synthetic‑commodity” money in which the schedule—not a committee—determines supply.

A central empirical regularity in Bitcoin is that, over long horizons, price versus time follows a power‑law relationship: plotting price against “days since genesis” on both axes (log‑log) yields an approximately straight line. A straight line on a log-log chart is a hallmark of power‑law behavior.

Giovanni Santostasi explains the Bitcoin power trend phenomena as the signature of recursive feedback loops (adoption leads to increases in security/hash rate which secures the floor price) under Bitcoin’s difficulty adjustment, which induces scale invariance over long horizons. Independent reports on the model find high goodness‑of‑fit on full‑sample regressions while also emphasizing that the framework is statistical—not an oracle. The Fidelity Digital Assets Coin Report echoes the mechanism qualitatively: how a “virtuous cycle” between users, miners, security increase the value of the bitcoin network and the value of the coin itself—precisely the kind of iterative dynamics that give rise to power laws.

We have independently verified that there exists a strong power trend of Bitcoin price and time with r-squared above 0.95. There also exist similar strong power trends (above 0.94) between Bitcoin and other commodities. Gold, silver, copper, crude oil, natural gas, soybeans, wheat, coffee, cotton, and other commodities are all diminishing in a Bitcoin unit of account according to a power trend.

Beyond its structural distinctiveness, Bitcoin’s return drivers are largely orthogonal to traditional assets. Institutional research from Blackrock characterizes Bitcoin as a non‑sovereign, fixed‑supply, decentralized asset whose long‑run correlation to U.S. equities has been low (average ~0.2 in rolling windows), making it a unique diversifier. This separation of drivers complements Glimpse’s goal to compare BTC against gold, equities, or other digital assets on a like‑for‑like probabilistic basis.

How Will Glimpse Forecast Bitcoin with Prediction Markets?

Glimpse will publish markets that translate this structural backdrop into forward‑looking probabilities. Some contracts will ask relative questions (“Will Bitcoin outperform gold over the next month?”), others will anchor to simple thresholds (“Will Bitcoin go up or down in the next month?”), and others will compare Bitcoin with major equities or digital assets over clearly defined windows. Prices on these contracts provide a live readout of market expectations—updated tick by tick as new information arrives.

Reading our markets is simple. A standard “winner‑takes‑all” contract pays 100 sats (0.00 000 100 BTC) if the event occurs and 0 sats if it does not; the trading price (say, 62 sats or 0.00 000 062 BTC) is the market’s current probability (62%) that the event will resolve “yes.” This mapping from price to probability is well established in the prediction‑market literature and makes the output easy to interpret. To keep spreads tight and maintain instant settlement even when activity is light, we employ automated market makers according to a modified version of the liquidity sensitive logarithmic market scoring rule (LS-LMSR). Prices are determined by market activity, and anyone can change the price by buying or selling sufficiently many contracts.

None of this is a claim that models—or markets—are crystal balls. Even strong fits can degrade, and every model can break. Power‑law regressions are guides and baselines, not guarantees. But combining a transparent structural lens (the power law) with a live aggregation mechanism (prediction markets) gives a disciplined framework for monitoring expectations around Bitcoin’s future performance as conditions change.

Glimpse’s mandate is to forecast the most significant trends in relation to Bitcoin. As a regulated Bitcoin prediction market soon to be operating under Bermuda oversight, we will publish clear questions, objective resolutions, and a steady cadence of methodology notes. The result is a public, continuously updated forecast that shows where informed traders think Bitcoin is heading—and how that view evolves when the facts do.

Select sources (for readers who want to dig deeper):

Santostasi, BTC is a power law because it is an infinite recursive feedback loop

Santostasi, The Bitcoin Power Law Theory

Fidelity Digital Assets, Coin Report: Bitcoin (BTC)

BlackRock, Bitcoin: A Unique Diversifier

Bailey & Warmke, Bitcoin Is King

Bailey, Rettler & Warmke, Money without State

Disclosure: This article is for information only and does not constitute investment advice or a solicitation to trade. Prediction markets reflect probabilities, not certainties; past performance and historical fits do not guarantee future results.