Every quarter, investors wait for earnings reports.
Analysts publish forecasts. Financial media debates the numbers. Companies issue guidance. Entire industries try to answer one question:
What happens next?
For decades, earnings forecasts have been one of the main tools used to estimate future company performance. But in recent years, another source of information has gained attention: prediction markets.
Unlike traditional analyst models, prediction markets don't attempt to produce a single forecast. Instead, they create a market where participants buy and sell contracts tied to future outcomes. The result is a constantly updating probability that reflects what traders collectively believe will happen.
This raises an interesting question:
Can prediction markets predict company performance better than earnings forecasts?
The answer is more complicated than a simple yes or no.
Analysts Have Earnings Models. Prediction Markets Have Probabilities.
Traditional earnings forecasts are built using financial models.
Analysts examine revenue trends, profit margins, market conditions, guidance, and competitive dynamics. They then estimate future earnings and publish their projections.
Prediction markets work differently.
Instead of producing a forecast directly, they allow participants to trade on future events. Market prices become a reflection of collective expectations.
For example, prediction markets may offer contracts related to:
- Federal Reserve rate decisions
- Inflation reports
- Economic growth
- AI company developments
- Crypto ETF approvals
- Major mergers and acquisitions
In these markets, a contract trading at 70 cents often implies roughly a 70% probability of an event occurring.
Both approaches try to answer the same question:
What is most likely to happen next?
But they arrive at that answer in very different ways.
Why Investors Are Paying Attention to Prediction Markets
Financial markets have always relied on expectations.
Stock prices move before earnings announcements. Bond markets react before economic releases. Investors constantly price in future outcomes.
Prediction markets make those expectations visible.
Instead of guessing what the market believes, participants can observe probabilities directly.
For example, before a major Federal Reserve meeting, prediction markets may reveal changing expectations about interest rate cuts. Before a regulatory decision, markets may show how likely participants believe an approval has become.
These probability shifts can provide additional context alongside traditional analyst research.
Rather than replacing forecasts, prediction markets offer another layer of information.
What Prediction Markets Reveal That Forecasts Often Miss
Forecasts usually provide a target.
Prediction markets provide uncertainty.
That difference matters.
A forecast might suggest a company will generate $5.20 earnings per share next quarter.
A prediction market can reveal how confidence changes over time as new information enters the market.
This dynamic behavior is often more valuable than a single forecast number.
Researchers can observe:
- How probabilities change after earnings releases
- How expectations react to economic data
- How confidence evolves before major announcements
- Whether market participants become more or less certain over time
In many cases, the direction of changing expectations can be just as important as the final outcome.
Measuring Expectation Shifts Using Market Data
The real value of prediction markets appears when researchers analyze market behavior over time.
Instead of looking at a single probability, they can study how expectations evolve.
This requires detailed historical market data.
Key datasets include:
OHLCV Data
OHLCV data tracks:
- Open price
- High price
- Low price
- Close price
- Volume traded
Researchers can use OHLCV data to study how market-implied probabilities changed during specific events.
For example, they might analyze how expectations around a Federal Reserve decision shifted during the weeks leading up to the announcement.
Historical Trades
Trade-level data reveals how participants actually interacted with the market.
Researchers can examine:
- Trade size
- Trade direction
- Trade timing
- Volume patterns
This helps identify periods of increasing conviction or uncertainty.
Quotes
Quote data captures bid and ask prices throughout the trading day.
By studying quotes, analysts can measure:
- Spread changes
- Liquidity conditions
- Market responsiveness
These signals often reveal how confident participants are in their expectations.
Order Books
Order book data provides a deeper view of market structure.
Researchers can observe:
- Available liquidity
- Depth at different price levels
- Changes in buying and selling interest
This information helps explain why probabilities move, not just how they move.
Building Prediction Markets Research Projects
One challenge researchers face is data fragmentation.
Prediction markets operate across multiple platforms, each with different APIs, market identifiers, and data structures.
Collecting and normalizing that information can become a significant project on its own.
A unified data source makes research much easier.
With FinFeedAPI's Prediction Markets API, researchers can access normalized data across:
- Polymarket
- Kalshi
- Myriad
- Manifold
- Hyperliquid HIP-4 Outcome Markets
Through a single API, users can retrieve:
- Market listings and metadata
- Latest market activity
- Historical trades
- Historical quotes
- OHLCV time series
- Current order books
- Historical order book data
This allows analysts to build studies that compare expectations across multiple prediction market platforms without maintaining separate integrations.
From Research to Forecasting Models
Prediction markets are not magic.
They do not guarantee accurate forecasts.
But they provide something traditional models often struggle to capture:
Real-time changes in collective expectations.
For researchers, investors, and analysts, prediction market data can complement existing forecasting methods.
Instead of choosing between analyst forecasts and prediction markets, many organizations use both.
Analyst models provide structure.
Prediction markets provide probabilities.
Together, they offer a more complete picture of uncertainty.
Try FinFeedAPI Prediction Markets API
If you're building prediction market research tools, forecasting models, investor dashboards, or event-driven analytics platforms, FinFeedAPI provides access to normalized prediction market data from Polymarket, Kalshi, Myriad, Manifold, and Hyperliquid HIP-4 Outcome Markets through a single API.
Access:
- Historical trades and quotes
- OHLCV market data
- Current and historical order books
- Market metadata and listings
- REST, JSON-RPC, and MCP interfaces
Get your free API key and start exploring prediction market data today.
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