A trader builds a position hedging unemployment risk by purchasing contracts tied to the next monthly jobless claims report. The economic intuition is sound: if unemployment rises, certain business positions suffer losses, and the contracts should gain value to offset that. Two separate Kalshi contracts address the same statistical release within days of each other. They appear identical in purpose, both reference the same government agency, and both settle based on numbers from the Bureau of Labor Statistics. Yet when the data arrives and event resolution occurs, the two contracts resolve to opposite outcomes, or one resolves while the other faces revision or dispute. The hedge fails to function as designed.
This disconnect reveals a critical gap between economic equivalence and contractual equivalence. On a prediction market where contract trading involves real money and positions can carry substantial notional value, the difference between two contracts that "should" behave the same is not a minor technical detail. It is basis risk—the possibility that seemingly correlated assets diverge at the moment when correlation matters most. Understanding why similar Kalshi contracts resolve differently requires examining how resolution criteria are written, how real-world outcomes resist binary classification, how government data undergoes revision, and how the platform's regulatory oversight and settlement procedures handle ambiguity when it appears.
A contract specification is a legal and operational document. It defines what data source will be checked, which measurement or statistic within that source, at what time, and how the resulting number maps to contract settlement. Two contracts both named "Will US Jobless Claims Drop Below 220,000?" can diverge dramatically depending on exactly which claims number is referenced. Initial jobless claims, continuing claims, insured unemployment rate, civilian labor force, and seasonally adjusted versus unadjusted figures are all published in the same weekly release, yet they move at different rates and sometimes in opposite directions.
The contract author's choice of which specific metric counts as the resolution data source is not arbitrary or obvious. A contract settling on initial claims will move differently than one settling on continuing claims during a recession when layoffs are rising but workers are remaining unemployed longer. If one contract specifies "the figure reported by the Department of Labor as of 8:30 a.m. Eastern on Thursday" and another specifies "the seasonally adjusted figure," a discrepancy in seasonal adjustment methodology—which occurs periodically—can cause one contract to resolve affirmatively and the other negatively, even if the underlying worker counts are stable. The trader who assumes both contracts track the same economic event is unknowingly exposed to specification risk.
Additionally, government data often includes footnotes, methodological changes, and technical notes that affect interpretation. The unemployment rate can be reported at multiple decimal places, and rounding rules matter when a contract's settlement threshold sits at a boundary. If a contract specifies "Will the unemployment rate be below 3.9%?" and the reported figure is 3.89% before rounding but rounds to 3.9% after, the resolution may hinge on whether the contract author intended the published rounded figure or the unrounded underlying data. These distinctions are not obvious to someone reading the contract title in a trading interface.
For traders attempting to trading prediction markets online with multiple positions across similar events, the cumulative effect is material. A portfolio of five unemployment-related contracts might have five slightly different resolution criteria, each creating a small gap between what the trader believed they were buying and what they actually contracted for. When settlement day arrives, those gaps can produce unexpected losses or gains that are not attributable to prediction accuracy but to specification variation.
Some events resist clean binary outcomes because the underlying phenomenon is continuous, phased, or subject to interpretation. A contract asking "Will the Federal Reserve raise rates in the next meeting?" appears straightforward—either the Fed does or does not. But the Fed may raise rates on some assets or in some contexts while holding others steady. If the contract specifies "the primary federal funds rate," clarity improves, but if it simply says "interest rates," ambiguity persists. Did the Fed meet the condition if it raised some rates but lowered others?
Environmental and policy events introduce even more complexity. A contract on "Will California implement a ban on gas-powered vehicles by 2030?" depends on defining what constitutes a ban, whether partial restrictions or phased approaches count, whether the mandate applies to all vehicles or only new sales, and whether state-level adoption of a prior federal standard satisfies the condition. The regulatory landscape may shift multiple times before 2030, and interim steps could satisfy one contract's definition while missing another's.
Batch event resolution compounds this problem. When multiple contracts settle simultaneously or in quick succession around the same underlying event—such as an earnings announcement that triggers contracts on revenue, profit margin, and guidance—the outcomes can create a logical inconsistency if resolution criteria are misaligned. One contract might resolve based on "revenue as reported in the earnings statement," another on "revenue as adjusted by the company in its subsequent SEC filing," and a third on "revenue as analyzed by a third-party research firm." If those three figures diverge—which they often do due to different accounting treatments—the contracts resolve to incompatible outcomes despite all three settling on the same announced event.
The platform's regulatory framework requires contract authors to provide clear, objective quantifiable outcomes, but objective specifications can still have gaps when the underlying event structure is genuinely ambiguous. The risk for traders is that they may hedge a perceived risk across several contracts, each defined slightly differently, and discover at resolution that their intended hedge was only partial or completely ineffective.
Government and corporate data are frequently revised. The initial unemployment report may show one number, the first revision one month later may adjust it upward or downward, and the final revision three months later may shift it again. A contract specifying "settlement date is the initial release" will resolve differently than one specifying "settlement uses the final revised figure." If the initial release shows 220,000 claims and the final revision shows 235,000, one contract resolves YES and another resolves NO based on which revision was written into the specification.
The choice of which revision to use is not trivial because revision sizes can be large. US economic data revisions regularly run in the tens of thousands for jobless claims, and several percentage points for employment growth. A trader who understood the direction of initial data but did not account for revision risk could find that their winning position at announcement becomes a losing position three months later when final data settles the contract.
Settlement delays also create divergence when real-world events extend beyond their expected window. A contract on "Will Company X announce earnings before January 31?" might specify settlement based on the announcement date reported on the company's website. If the company delays its announcement, posts preliminary results on one date and detailed results on another, or corrects its initial statement hours later, the contract author's specification for which announcement counts determines resolution. Two contracts created independently might make different assumptions about what counts as "announcement."
Additionally, market oversight procedures sometimes require manual review or dispute resolution when data is ambiguous or contradictory. A contract settling on a specific economic metric might be held for review if the data source publishes conflicting figures, if a methodological change affects comparability, or if the event occurs exactly at a threshold that triggers review protocols. During that review window, traders cannot unwind positions, and the final resolution outcome becomes uncertain in a way that goes beyond market price discovery. The contract may eventually resolve correctly according to specifications, but the delay and uncertainty itself creates a divergence in how traders can manage risk.
Traders often construct synthetic positions by combining multiple related contracts to express a specific view or hedge a correlated risk. For example, a position in a contract on "US GDP growth next quarter" can be combined with a position in "Q3 unemployment rate" to express a view on economic resilience independent of unemployment dynamics. The contracts are designed to capture different aspects of the same economic cycle, but they resolve at different times, reference different data sources, and carry different revision schedules.
If GDP is reported in advance and subject to two revisions, while unemployment is reported monthly and subject to one revision, a trader holding both contracts cannot execute a true pair trade where profits and losses offset exactly. The timing mismatch alone creates basis risk: the contracts may move in opposite directions due to different reporting schedules or seasonal adjustments, even if the underlying economic trend is clear.
Commodities and inflation contracts illustrate this further. A trader hedging inflation exposure might purchase contracts on "Will CPI increase above 3% YoY?" and "Will headline PCE increase above 3.5% YoY?" These are both inflation measures from the same general economic phenomenon, but CPI and PCE diverge due to different weights, different baskets, and different calculation methodologies. A hedge that works for one measure may not work for the other. Over the past decade, CPI and PCE have drifted apart by several hundred basis points, and a trader relying on them to move in lockstep could face substantial basis risk.
The regulatory requirement that Kalshi maintain transparent contract trading specifications and clear settlement rules helps mitigate but does not eliminate this risk. The specifications are posted and available, but traders under time pressure or managing large portfolios may not review them with enough care to catch subtle differences. The burden of due diligence falls on the trader, and the market does not automatically price in all specification gaps.
Some real-world outcomes are genuinely difficult to classify into the binary categories that prediction contracts require. A contract asking "Will the FTC approve the proposed merger?" may settle on whether the FTC issues a decision, but what if the company withdraws the application? What if the FTC issues approval subject to conditions, and the company disputes whether those conditions are acceptable? The contract specification must address these edge cases, and different contracts may resolve them differently.
Technology milestones create particularly acute classification problems. A contract on "Will artificial intelligence systems achieve performance above the X benchmark by end of 2024?" requires defining what counts as "achieving" the benchmark, whether preliminary results count or only peer-reviewed published results, whether the system must be publicly available or can remain proprietary, and what constitutes the relevant benchmark metric if multiple variants exist. Two contracts with slightly different definitions of achievement could both reference the same underlying technology and the same benchmark, yet resolve to opposite outcomes if a system achieves one variant of the benchmark but not another.
Election outcomes, policy changes, and regulatory actions also invite classification disputes. A contract on "Will the President veto the bill?" must specify whether a pocket veto counts, whether the settlement date is when the veto occurs or when it is recorded, and whether a threatened veto that the President declines to exercise counts as a veto or not. The specification attempts to eliminate ambiguity, but real-world events sometimes create edge cases that the author did not anticipate.
When resolution disputes occur, Kalshi's regulatory oversight mechanisms and dispute procedures become critical. However, these processes take time and may not produce a unanimous consensus on what the "correct" answer should be. A trader holding a position during a disputed resolution can face uncertainty about final settlement, and even after the dispute is resolved, discovering that their hedge was subject to classification risk is a form of gap loss that could have been prevented with more careful specification review.
Prediction markets often see clusters of related contracts created around major events. An earnings announcement might trigger contracts on revenue, profit, earnings per share, guidance, and return on equity. A government data release might trigger contracts on the headline number, the core number, revisions, and month-over-month changes. When these contracts resolve in a batch around the same event, small specification differences can create logical inconsistencies that suggest the market has misprice one or more of the outcomes.
For instance, a company that beats on revenue but misses on earnings per share because of higher tax expenses creates a situation where one contract resolves YES and another resolves NO based on different aspects of the same earnings report. This is not necessarily an error; the contracts are tracking different metrics. However, a trader who assumed that better revenue would lead to higher earnings could find their hedge was incomplete because it did not account for the tax differential. The contracts resolved correctly according to specifications, but the basis risk—the gap between what the trader intended and what the contracts actually protected—was not obvious until settlement.
Additionally, when multiple contracts resolve around the same event within a short window, traders who are long one contract and short another may face liquidity constraints if they try to exit positions before all contracts settle. The price of one contract may adjust based on the first resolution outcome, and if the second contract's resolution is ambiguous or delayed, traders holding the second position cannot hedge or exit cleanly. This dynamic can create artificial price divergence between contracts that should, in theory, converge to their intrinsic settlement values.
The cumulative effect of batch resolution gaps is that a trader's position in what appears to be a balanced portfolio can suddenly become unbalanced if one contract resolves earlier than expected or if resolution criteria interact in unexpected ways. Managing basis risk across multiple contracts requires ongoing attention to specification details and settlement schedules, not just to market prices and probability estimates.
The first step is to review the complete contract specification, not just the contract title, before trading. Kalshi provides detailed specifications including the data source, measurement date, revision policy, and settlement criteria. A trader should verify that every contract they purchase or sell for the purpose of constructing a hedge actually specifies the same underlying metric, the same data source, and the same revision rule. If the specifications differ, the contracts do not provide the hedging relationship the trader intended, and position sizing should reflect that gap.
Second, traders should identify contracts that are subject to revision and contracts that are not, and size positions accordingly. A contract settling on initial data is more liquid and resolves faster, but it is subject to revision risk. A contract settling on final data is safer for hedging purposes but may require waiting months for resolution. Combining the two types requires understanding that the trader is exposed to revision divergence, not just market-price divergence.
Third, traders constructing pairs or portfolios of related contracts should stress-test the relationship by examining historical divergences between the metrics. If CPI and PCE have drifted by several hundred basis points over a decade, a trader assuming they move in lockstep is underestimating basis risk. Historical correlations provide a baseline for the magnitude of divergence that can occur between related but distinct metrics.
Fourth, when trading around major events expected to trigger multiple contract resolutions, traders should map out the batch resolution schedule and identify which contracts settle first, which settle later, and which are subject to revision. This map allows the trader to plan exit strategies before announcement, avoiding situations where they are trapped holding a position while waiting for a contested resolution or late-arriving revised data.
Kalshi's regulatory framework requires clear event resolution specifications and transparent settlement procedures specifically to reduce the risk that contracts resolve in unexpected or unfair ways. The platform's compliance requirements ensure that contract authors provide sufficient detail that traders can understand the settlement criteria and that disputes can be resolved based on objective facts rather than subjective interpretation. This oversight is valuable and necessary.
However, regulatory oversight cannot eliminate basis risk because it cannot make the underlying world less ambiguous. A regulation requiring that contracts settle on "the official government figure" cannot force the government to stop revising data or changing methodologies. A requirement that contracts specify the data source cannot prevent economic metrics from diverging due to different definitions or calculation methods. The oversight ensures that what is written in the specification is what settles the contract; it does not guarantee that two contracts titled similarly will contain identical specifications.
The platform's approach is therefore to shift responsibility for specification review from the platform to the trader. This is appropriate for sophisticated traders who have the time and expertise to read specifications carefully. For less experienced traders, the implication is that basis risk is a non-obvious but real source of losses. A trader who buys five unemployment-related contracts, intending to build a hedge, may discover that they are not hedging a single underlying risk but rather five slightly different risks, each with its own resolution date, revision schedule, and specification gaps.
Going forward, the most useful evolution in prediction markets would be enhanced tooling that makes specification differences more salient. Contract comparison tools, automated flags when two contracts with similar titles have divergent specifications, and historical analysis of how similar contracts have diverged at resolution could help traders better understand basis risk before it affects their positions. Until such tools become standard, traders must treat specification review as a mandatory step, not an optional detail.
Yes, if their specifications differ. Two contracts both tracking unemployment might settle on different claims measures (initial versus continuing), different data sources (Bureau of Labor Statistics primary release versus revised data), or different calculation methods (seasonally adjusted versus unadjusted). Even though they address the same general economic phenomenon, the contracts are not identical and can resolve differently. Always review the complete specification before assuming two similarly titled contracts are equivalent.
Basis risk is the gap between the hedge a trader intended to construct and the hedge the contracts actually provide. Two contracts that appear to track the same economic outcome but have different resolution criteria, revision schedules, or data sources will diverge at settlement. A trader relying on the contracts to offset exactly may find that one contract gains while the other loses due to specification differences rather than prediction error. Managing basis risk requires detailed attention to contract specifications, not just market prices.
Government economic data is often revised one or more times after initial release. A contract specified to settle on initial data will resolve differently than one settling on final revised data. If initial unemployment claims are reported at 220,000 and later revised to 235,000, the two contracts resolve to opposite outcomes. Traders hedging against a risk using contracts with different revision schedules are exposed to revision risk, a form of basis risk that can produce unexpected losses at settlement.
