From Speculation to Signal: Why Prediction Markets Need Fair Value

Prediction markets are often described as some of the most efficient information-pricing systems ever created. By allowing participants to trade on future outcomes, they aggregate dispersed beliefs into prices that represent collective expectations. In theory, these prices should converge toward truth.

In practice, however, prediction markets remain structurally incomplete. Their efficiency breaks down not because of insufficient participation or liquidity, but because they lack a clear and consistent notion of fair value - a benchmark probability against which prices can be rationally evaluated.

This does not mean prediction markets operate without models or external forecasts, but rather that there is no widely adopted, standardized fair-value reference that is native to prediction markets themselves. 

This missing layer becomes increasingly consequential as prediction markets mature into serious financial infrastructure.

Where Prediction Markets Fall Short

Prediction markets excel at aggregating information. They outperform polls in many contexts, update continuously, and incorporate incentives that reward accuracy over opinion. Unlike traditional forecasting mechanisms, they allow beliefs to be expressed quantitatively and refined in real time.

But prices alone do not explain themselves.

A market price reflects more than probability. It also reflects liquidity conditions, risk preferences, capital constraints, leverage, and sentiment. As a result, the same price can emerge under very different underlying assumptions about true likelihood.

Prediction markets tell us where the market is trading. They do not tell us whether the market is right. 

They do not, by themselves, provide a clear reference for evaluating whether that price represents a statistically reasonable estimate of true probability.

The Structural Absence of Fair-Value Benchmarks

In most mature financial markets, fair-value frameworks play an important role. Options markets rely on pricing models, fixed-income markets rely on yield curves, and risk systems use scenario analysis to guide positioning.

These models are themselves uncertain, model-dependent, and often calibrated from market prices rather than independently derived. Their value lies not in precision, but in providing a shared analytical framework for pricing and risk management.

Prediction markets already draw on polls, forecasting models, and expert analysis as external reference points. What they generally lack is a standardized, continuously updated fair-value signal designed specifically for prediction markets and integrated directly into trading decisions.

This absence does not prevent markets from functioning, but it limits their ability to operate as disciplined probability-pricing systems rather than sentiment-driven venues.

Why Sentiment and Price Are Not Substitutes for Probability

Market prices are often mistaken for probabilities. While prices encode information about likelihood, they are shaped by many factors beyond probability itself.

Prices are influenced by:

• who is participating,
• how much capital they control,
• their risk tolerance,
• their time horizon,
• prevailing narratives.

Sentiment can push prices away from statistically grounded estimates, especially in politically charged or event-driven markets. Liquidity constraints can prevent prices from correcting. Short-term narratives can dominate longer-term fundamentals.

Probability, by contrast, is a property of the outcome, not of the market. Without an explicit probabilistic reference point, participants are forced to infer probability indirectly from price action, a process that is inherently noisy and uneven.

Fair Value Reframes Decision-Making

Fair value reframes prediction markets from speculative environments into structured decision systems.

The logic is simple:

• If fair value is higher than the market price, buying “Yes” is statistically favorable.
• If fair value is lower, selling “Yes” is statistically favorable.

Fair value does not guarantee correctness on any single event. Like all models, fair-value estimates are uncertain and dependent on assumptions and inputs. Their value lies in consistency and discipline.

In this way, fair value functions as a north star: not a promise of certainty, but a guide for rational behavior under uncertainty.

Why AI Agents Are Uniquely Suited to This Problem

Estimating fair value in prediction markets is difficult precisely because outcomes depend on many interacting variables. Unlike options pricing, there is no single closed-form equation that captures political dynamics, macro shocks, sentiment shifts, and behavioral feedback loops.

This is where AI-native systems offer a potential advantage.

AI agents can:

• integrate heterogeneous data sources,
• update probabilities dynamically,
• learn from historical outcomes,
• and operate directly in probability space rather than narrative space.

At the same time, AI-generated fair values are not inherently reliable by default; their usefulness depends heavily on model design, data quality, and empirical validation. The opportunity lies in making fair-value estimation measurable, testable, and continuously improvable rather than implicit or ad hoc.

Yala: Fair Value as Market Infrastructure

Yala approaches fair value not as a trading strategy, but as infrastructure.

Its goal is to build AI-native fair-value agents that generate explicit probability signals for prediction markets - signals that can serve as shared reference points rather than authoritative forecasts.

These signals are intended to be evaluated in live markets over time, with performance measured empirically rather than assumed in advance. By making probabilistic assumptions explicit and testable, prediction markets can evolve beyond speculation toward more disciplined information pricing.

As prediction markets increasingly intersect with finance, governance, and global decision-making, fair value becomes less about prediction and more about coordination.