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Neither instrument is universally better. Choose a well-defined prediction-market contract when you need a direct probability for a binary event, such as whether a specified phrase appears on an earnings call. Choose stock options when you need the market-implied distribution, tradable exposure, or a hedge for the stock-price reaction to earnings. Neither market, on the available evidence, has been shown to forecast earnings surprises or post-earnings returns better in every situation.
The short answer
| Your forecasting question | Better starting point | Why |
|---|---|---|
| Will a precisely worded event occur? | Prediction market | The contract settles directly on that event and its price is commonly read as an event probability. |
| How large could the stock move be after earnings? | Stock options | The option surface prices exposure to the underlying stock, including jump risk around the announcement. |
| Do I need to hedge or trade the stock reaction? | Stock options | Options are designed to transfer, limit, or take stock-price risk. |
| Do I want a clean yes/no forecast for a research scorecard? | Prediction market, if liquid | A binary contract is easier to score with calibration, Brier score, or log loss. |
| Will the company beat EPS or revenue estimates? | Neither automatically | The contract and option price may be informative, but the cited evidence does not establish a universal winner for earnings surprises. |
The comparison is meaningful only when both instruments address the same issuer, event, horizon, settlement rule, liquidity conditions, fees, and scoring method. A contract about a CEO mentioning layoffs is not a forecast of EPS, revenue, guidance, or the share-price return.
What a prediction market forecasts
A contract is a definition, not a general opinion
A prediction-market position pays according to a stated settlement rule. A “yes” share priced at 64 cents is often interpreted as an approximately 64% probability, provided the contract is liquid, the wording is unambiguous, and the price is not materially affected by fees or risk preferences. The actual question is always the one in the contract: whether a named phrase is mentioned, whether a data release exceeds a threshold, or whether another precisely defined event occurs.
That distinction matters around earnings. “The chief executive mentions layoffs on the call” has a binary outcome and a specified source. “The company beats the consensus EPS estimate” requires a definition of consensus, a data source, a reporting period, treatment of adjusted figures, and a cutoff time. “The stock rises after earnings” requires a return window and a price reference. Those are different contracts, even when they concern the same announcement.
Why the price can be useful but imperfect
Large-sample evidence indicates that prediction-market prices can be well calibrated in aggregate. A 2026 Kalshi analysis of 2,243,741 resolved markets across eleven categories, covering the platform from its 2021 launch through mid-2026, reported Brier scores of approximately 0.08–0.09 at a three-month horizon and about 0.02 near closing. Naive accuracy increased from 88.3% to 97.2% as resolution approached. The same analysis reported that forecasting quality improved with market depth and participant count.
Calibration is not guaranteed for an individual quote. Time to resolution, displayed depth, bid-ask spread, recent volume, position limits, fees, and the settlement source all affect how much information a price contains. A thin market can move because of one order rather than a broad change in expectations. Prices can also reflect risk preferences rather than a statistically unbiased probability.
The strongest earnings-specific evidence
The most directly relevant study identified 1,437 earnings conference-call mention contracts. William J. Mayew and Boyan Zhu reported an area-under-the-curve score of 0.893 for market prices versus 0.891 for disclosures alone. These contracts concern whether specified information is mentioned during a call; they do not directly test EPS surprises, revenue surprises, or post-earnings stock returns. They therefore support the usefulness of a well-designed event market, not a claim that prediction markets dominate options for every earnings forecast.
What stock options forecast
Options price tradable risk, not a promise about the real-world probability
An option is a derivative on the underlying stock. Prices across strikes and expirations form an implied-volatility surface that reflects the market’s priced uncertainty about future stock moves. Around earnings, that surface incorporates the possibility of a discontinuous announcement jump, changes in volatility before and after the event, skew between downside and upside strikes, and demand from traders hedging or seeking exposure.
Rank #2
Probabilities extracted from option prices are risk-neutral probabilities. They are the probabilities that make derivative prices internally consistent after accounting for discounting and risk premia; they are not automatically the physical, real-world frequencies an observer would estimate from repeated earnings announcements. Supply and demand, inventory, hedging pressure, funding, and volatility risk premia can all move an implied probability or expected move.
What an option surface can tell you
- Expected-move scale: the market’s priced range for the stock over the option’s horizon, often summarized from at-the-money options.
- Distribution shape: skew and the relative pricing of out-of-the-money puts and calls indicate how the market prices asymmetric outcomes.
- Timing: the term structure shows how pricing changes for expirations before and after the announcement.
- Tradable exposure: calls, puts, and combinations can hedge or express a view on the stock reaction.
An option quote does not answer a binary question unless you define a price threshold, expiration, and exercise convention, then derive a probability from a model or an arbitrage-consistent distribution. Even then, the result remains risk-neutral and model-dependent.
Why “better” depends on the target event
| Target | Prediction-market interpretation | Options interpretation | Main mismatch to avoid |
|---|---|---|---|
| Specific call mention | Direct binary probability of the contract’s wording | Usually no direct equivalent | Do not treat option volatility as a phrase-mention probability. |
| EPS or revenue beat | Direct only if a contract defines the metric and source | Indirectly reflected through expected stock repricing | A stock move can occur without a beat, or a beat can produce a small move. |
| Absolute post-earnings move | Requires a contract with an explicit threshold and window | Implied distribution is designed for stock-price exposure | Match expiration and the exact price reference. |
| Directional stock return | Requires a return-direction contract | Can be approximated from a risk-neutral distribution | Option-derived odds include risk premia and are not guaranteed frequencies. |
| Hedge for an existing position | Usually cannot offset stock exposure precisely | Purpose-built hedge instrument | Forecast quality and hedge suitability are different objectives. |
What the broader evidence says
Large-sample prediction-market calibration
The 2026 Kalshi analysis covered more than 2.2 million observations and concluded that forecasts were closely correlated with how often events occurred. Its results show why horizon matters: a quote several months before resolution contains more uncertainty than the same contract near the close, and accuracy naturally rises as information arrives. Comparing a long-dated prediction-market quote with an option expiring immediately after earnings would mix different information sets.
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Microstructure and favorite–longshot bias
A 2026 CESifo Working Paper by Constantin Bürgi, Wanying Deng, and Karl Whelan examined more than 300,000 contracts. It found that prices were informative and improved near closing, while also documenting favorite–longshot bias and microstructure effects. In practical terms, very high- or very low-priced contracts may not map one-for-one to objective frequencies, and spread, depth, and order flow deserve attention.
A public comment to the Commodity Futures Trading Commission similarly identifies risk-adjustment concerns. Treat a displayed probability as a quoted market estimate, not as a guaranteed unbiased statistic.
How the Federal Reserve frames prediction markets
Federal Reserve authors Anthony M. Diercks, Jared Dean Katz, and Jonathan H. Wright describe prediction markets as “a new market-based approach to measuring macroeconomic expectations in real-time.” They characterize Kalshi markets as “a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers.” Those descriptions support using such prices as timely information, while leaving open the question of whether they outperform options for a particular earnings target.
How to compare the two instruments fairly
- Write the target before looking at prices. Specify the issuer, metric or event, threshold, announcement date, settlement source, and observation window. For a stock forecast, state whether the return is measured from the prior close, the announcement price, or another reference.
- Match the horizon. Compare a prediction contract at the same time before earnings with an option expiration that covers the same event window. A three-week contract and a one-day-to-expiry option are not substitutes.
- Record executable prices. Save bid, ask, midpoint, displayed size, recent volume, open interest where available, fees, and position limits. A midpoint with a wide spread is not the same as a price at which a meaningful position can be traded.
- Translate options carefully. Use the full option surface when estimating a distribution rather than relying only on one at-the-money implied volatility. State that the resulting probabilities are risk-neutral and note the model, interpolation, dividends, rates, and expiration assumptions.
- Use the same realized outcome. Score both forecasts against the identical settlement value. For binary events, use Brier score, log loss, calibration curves, or a threshold-independent measure such as AUC. For returns, use a clearly defined forecast-error metric and preserve the sign and magnitude of the move.
- Include implementation costs. Deduct fees, bid-ask slippage, market impact, financing, and the cost of carrying an option through the announcement. A theoretically superior forecast can be economically inferior after trading costs.
- Repeat across announcements. One earnings date cannot establish superiority. Use a predeclared sample, avoid changing the target after seeing the outcome, and separate calibration from trading profitability.
Choosing by use case
You need a binary event probability
Start with a prediction contract whose wording and settlement source are exact. Check the contract’s remaining time, spread, depth, volume, and participant activity. Read the resolution rules, including what happens if a call is cancelled, a transcript is amended, or the source is unavailable. If the market is thin or ambiguous, the apparent probability may be less reliable than its precision suggests.
You need the expected stock move
Inspect the option chain around the earnings date. Compare expirations immediately before and after the announcement, examine the at-the-money straddle or another stated expected-move method, and review skew and term structure. Remember that the result is a priced distribution under risk-neutral assumptions. It can be useful for sizing a hedge or comparing scenarios without being a promise that the stock will realize the midpoint of the range.
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You need to hedge a portfolio
Use options because their payoff is linked directly to the stock. Choose strike, expiration, and quantity based on the exposure you need to offset. A prediction-market position on a related event may express information, but it generally will not provide a stable hedge ratio for a specific share position.
You are building a forecasting study
Pre-register the event definition, data timestamp, fee treatment, and scoring rule. Keep prediction-market and option observations synchronized. Report both raw and cost-adjusted results, calibration by probability bucket, and performance by time-to-resolution. This approach prevents a favorable but incomparable snapshot from being presented as evidence of general superiority.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common mistakes and how to avoid them
Equating a mention with an earnings result
A call-mention contract can be informative about what management says, but it does not settle whether EPS beats estimates or whether investors will buy the stock. Use the contract only for the event it names.
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Calling implied volatility a probability
Implied volatility summarizes option prices under a pricing framework. Convert it into a probability only after defining the threshold and horizon, and label the result risk-neutral. Do not present it as an unbiased forecast of real-world frequencies.
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Ignoring the announcement jump
An option expiring after earnings includes jump risk that an option expiring before earnings does not. The expiration date, not just the ticker and strike, determines which information is priced.
Reading a thin quote as consensus
Check spread, depth, volume, and the number of active participants. A price formed by little trading can be stale or highly sensitive to one order. Prediction-market calibration evidence improves with participation and market depth; the same implementation discipline is needed when reading options.
Comparing unlike scoring rules
A prediction-market Brier score for a binary event cannot be compared directly with an option strategy’s profit and loss. Choose a common target and report the appropriate statistical and economic measures separately.
A practical decision checklist
- Is the question a single, explicitly settled event? If yes, a prediction market may be the clearer probability instrument.
- Is the question about the size, direction, or distribution of a stock move? If yes, start with the option surface.
- Do you need a hedge, leverage, or a defined payoff on shares? Use options.
- Can you verify settlement wording, source, timing, fees, and liquidity? If not, do not treat the displayed quote as a precise probability.
- Are you comparing the same issuer, horizon, event definition, and outcome? If not, the comparison is not valid.
- Will you score forecasts out of sample and after costs? If not, you are observing prices, not demonstrating predictive superiority.
Frequently asked questions
Do Kalshi prices represent real probabilities?
They can be useful probability-like estimates for the precisely defined settlement event, and aggregate calibration has been strong in large samples. They are not automatically unbiased probabilities for every contract because liquidity, risk adjustment, favorite–longshot bias, fees, and settlement design can affect the quote.
Can a prediction market tell me whether a company will beat estimates?
Only if a contract explicitly defines the estimate, accounting metric, data source, and settlement rule. A contract about a conference-call statement or another related event is not an EPS or revenue forecast.
What does an options market forecast before earnings?
It prices a risk-neutral distribution of the stock’s future value over the option’s expiration, including the potential announcement jump and the compensation investors demand for bearing risk. It does not guarantee the real-world probability of a particular return.
Is one instrument better for predicting post-earnings returns?
The available evidence does not establish a universal winner. A fair test must compare matched events and horizons against the same realized return, with liquidity and trading costs included.
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