When Prediction Markets Can’t Agree: The Problem of Event Identity

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Author: Denis Avetisyan

A new analysis reveals that fragmented definitions of the same event across prediction markets lead to price discrepancies and limit their effectiveness as global information aggregators.

The fluctuating activity within prediction markets-illustrated by a log-scaled trend of event launches and resampled platform shares-demonstrates a clear sensitivity to U.S. regulatory interventions, specifically the CFTC action against Polymarket in early 2022, the subsequent withdrawal of PredictIt’s protective status, and the 2024 rulings permitting Kalshi’s markets to remain open during appeals.
The fluctuating activity within prediction markets-illustrated by a log-scaled trend of event launches and resampled platform shares-demonstrates a clear sensitivity to U.S. regulatory interventions, specifically the CFTC action against Polymarket in early 2022, the subsequent withdrawal of PredictIt’s protective status, and the 2024 rulings permitting Kalshi’s markets to remain open during appeals.

Lack of semantic interoperability in event definitions causes price divergence and hinders arbitrage opportunities in prediction markets.

Despite their design to aggregate dispersed information, prediction markets are hampered by a fragmented ecosystem lacking a shared understanding of event identity. This paper, ‘Semantic Non-Fungibility and Violations of the Law of One Price in Prediction Markets’, demonstrates that this semantic non-fungibility leads to systematic price divergence across platforms, hindering arbitrage and limiting effective information aggregation. Analyzing a novel dataset of over 100,000 events, we find persistent mispricings of 2-4% in semantically equivalent markets-driven by structural frictions, not informational disagreement. Can resolving event identity unlock the potential for prediction markets to function as truly global information processors?

The Illusion of Liquidity: Why Prediction Markets Can’t Seem to Agree

Prediction markets, despite their potential to aggregate information and forecast future events, are hampered by a pervasive issue: liquidity fragmentation. Rather than consolidating trading volume, these markets operate as a constellation of independent platforms, each listing the same event multiple times. This dispersal of trading activity diminishes the overall liquidity available for any single event, creating thinner order books and wider bid-ask spreads. Consequently, the efficiency of price discovery is compromised, as prices may not accurately reflect the collective wisdom of the crowd. The problem isn’t a lack of overall interest in prediction markets, but rather the difficulty in channeling that interest into a unified, liquid exchange for each specific outcome, ultimately hindering their predictive power and potential for robust forecasting.

Despite the promise of prediction markets, a significant degree of price divergence exists due to fragmentation across multiple platforms. Research indicates that while only a small fraction – approximately 6% – of listed events demonstrate semantic overlap between platforms, this limited overlap still accounts for roughly 10% of all event-days tracked. This discrepancy highlights a core inefficiency; in truly efficient markets, identical underlying events should command consistent pricing regardless of listing location. The observed price variations suggest that information isn’t flowing seamlessly between platforms, preventing arbitrageurs from capitalizing on mispricings and ultimately undermining the accuracy of these markets as collective intelligence tools.

Prediction markets are built on the economic principle of the ‘No-Arbitrage Condition’ – the idea that identical assets shouldn’t trade at different prices across different venues. However, observed data reveals frequent and substantial violations of this condition. Analysis indicates the presence of arbitrage opportunities yielding price deviations of several hundred percent APY, suggesting significant inefficiencies within these fragmented markets. This isn’t merely a theoretical concern; these discrepancies represent real, exploitable price differences that undermine the reliability of prediction market signals and challenge the assumption of rational pricing. The persistence of such large deviations points to systemic issues preventing efficient price discovery, even for seemingly liquid events, and highlights a critical area for improvement in the design and operation of these platforms.

The persistent fragmentation of prediction markets stems from a fundamental challenge: accurately identifying the same real-world event across different platforms. While seemingly straightforward, variations in event descriptions, tagging conventions, and even subtle differences in phrasing create discrepancies that prevent aggregation of liquidity. This inability to consistently link identical events results in artificially inflated prices on some platforms and depressed prices on others, violating the core principle of market efficiency. Essentially, the market behaves as if it’s trading multiple, distinct outcomes when, in reality, it’s dealing with the same singular possibility, leading to substantial and exploitable price deviations and hindering the overall effectiveness of these markets as accurate forecasting tools.

For the top 1,000 trading relations, price deviations from equilibrium increase with decreasing effective liquidity, with arbitrage type (<span class="katex-eq" data-katex-display="false">\bigcirc</span>: equivalent, <span class="katex-eq" data-katex-display="false">\circ</span>: subset, <span class="katex-eq" data-katex-display="false">\triangle</span>: negative risk) and market structure influencing the magnitude of these deviations as shown by robust smoothed averages surrounding equilibrium (dashed lines), and only persisting for at least one hour.
For the top 1,000 trading relations, price deviations from equilibrium increase with decreasing effective liquidity, with arbitrage type (\bigcirc: equivalent, \circ: subset, \triangle: negative risk) and market structure influencing the magnitude of these deviations as shown by robust smoothed averages surrounding equilibrium (dashed lines), and only persisting for at least one hour.

The Semantic Mess: Why Markets Can’t Agree on What’s Actually Happening

Semantic Non-Fungibility arises from the inconsistent and varied ways events are described across different prediction markets and data sources. This means that two markets referencing the same real-world event may use distinct phrasing, terminology, or levels of detail in their descriptions, preventing automated systems from recognizing their shared reference. The lack of standardized event representation hinders accurate comparison of market outcomes, aggregation of liquidity, and the reliable calculation of cross-market probabilities. Consequently, even if the underlying event is identical, differing semantic descriptions necessitate manual review or complex disambiguation processes to establish equivalency, creating a significant barrier to interoperability and efficient market analysis.

Establishing Event Identity is fundamental to resolving ambiguity in contingent claims markets. A machine-verifiable understanding requires defining a consistent reference point for each event, allowing for accurate comparison across different market descriptions. Without this, determining whether two markets resolve to the same outcome – or whether one market’s resolution logically implies another – becomes computationally intractable. This necessitates a system capable of abstracting the core meaning of an event description, independent of surface-level variations in phrasing or data source. Successfully establishing Event Identity is therefore a prerequisite for interoperability and efficient risk management within decentralized prediction markets.

The Semantic Matching Pipeline is designed to determine if differing market descriptions refer to the same underlying event. This is achieved by establishing relationships between events, specifically differentiating between ‘Equivalence Relations’ and ‘Subset Relations’. Equivalence signifies identical outcomes – two markets resolving with the same result – while a Subset Relation indicates that one market’s possible outcomes are contained within another’s; for example, a market on “Team A wins” is a subset of a market on “Team A wins or the game ends in a draw”. The pipeline’s output categorizes these relationships, facilitating accurate comparison and aggregation of data across different market listings.

The Semantic Matching Pipeline utilizes Large Language Models (LLMs) to perform comparative analysis of market description texts, enabling the identification of semantic similarities and discrepancies. This LLM-based semantic analysis involves embedding market descriptions into vector spaces, allowing for the calculation of cosine similarity scores to quantify the relatedness of different markets. Ambiguities are resolved through a multi-stage process: initial vector comparison identifies potential matches, followed by LLM-generated rationales explaining the degree of semantic overlap, and finally, a rule-based system applies thresholds and contextual factors to determine if markets are referencing the same underlying event, distinguishing between equivalence and subset relationships.

A t-SNE projection of event embeddings reveals coherent semantic clusters, demonstrating that embedding similarity effectively captures event-level meaning and supports its application in high-recall candidate retrieval.
A t-SNE projection of event embeddings reveals coherent semantic clusters, demonstrating that embedding similarity effectively captures event-level meaning and supports its application in high-recall candidate retrieval.

Exploiting the Chaos: Finding Profit in Predictable Inefficiency

Semantic alignment of markets establishes a standardized basis for price comparison, enabling the identification of arbitrage opportunities arising from temporary price discrepancies. These discrepancies represent instances where the same asset is priced differently across ostensibly equivalent markets, creating a risk-free profit potential. An arbitrageur can exploit this by simultaneously purchasing the asset in the lower-priced market and selling it in the higher-priced market, capturing the difference as profit. The effectiveness of this strategy relies on the speed of execution and minimization of associated costs, but the fundamental principle is predicated on the ability to accurately compare prices after semantic normalization of market data.

Execution costs, comprising transaction fees and slippage, represent a significant factor in arbitrage profitability. Transaction fees are levied by exchanges or networks for processing trades, while slippage occurs when the expected price of an asset differs from the price at which the trade is executed, particularly in volatile markets or with large order sizes. These costs directly reduce potential arbitrage profits; a seemingly advantageous price discrepancy can be entirely negated, or even result in a loss, if execution costs are not accurately assessed and factored into the trading strategy. Therefore, a rigorous evaluation of both fixed transaction fees and anticipated slippage is essential prior to initiating any arbitrage trade.

Cross-chain arbitrage leverages price discrepancies of the same asset across multiple blockchain networks. These discrepancies arise due to market inefficiencies and varying liquidity between chains. The process involves purchasing an asset on a blockchain where it is trading at a lower price and simultaneously selling it on another blockchain where the price is higher. Successful execution requires consideration of transaction speeds, bridge transfer times, and associated fees on each chain. While potentially highly profitable, cross-chain arbitrage is subject to risks including smart contract vulnerabilities and impermanent loss when utilizing decentralized exchanges.

A systematic analysis of 102,275 market events was conducted to validate the efficacy of semantic alignment in identifying arbitrage potential. This analysis demonstrated that a mechanical arbitrage strategy, leveraging identified mispricing, achieved an annualized return of 1218.66% over an 800-day period. These results indicate that semantic alignment provides a quantifiable method for discovering and capitalizing on market inefficiencies, supporting its practical application in automated trading strategies.

Analysis of prediction market pairs reveals a strong correlation between liquidity and arbitrage opportunities, with higher liquidity associated with both increased potential annualized returns and a greater frequency of executable, risk-free arbitrage, as indicated by median time to resolution and log-volume binning, respectively.
Analysis of prediction market pairs reveals a strong correlation between liquidity and arbitrage opportunities, with higher liquidity associated with both increased potential annualized returns and a greater frequency of executable, risk-free arbitrage, as indicated by median time to resolution and log-volume binning, respectively.

Beyond Prediction Markets: The Wider Implications of Semantic Order

The study reveals that semantic fragmentation – the existence of multiple, subtly different ways of expressing the same underlying event within prediction markets – introduces a measurable degree of inefficiency. By developing a novel semantic matching pipeline, researchers quantified how these variations in phrasing lead to price discrepancies for essentially identical outcomes. This isn’t simply a matter of noise; the analysis demonstrates that traders systematically fail to fully aggregate information across semantically similar, but distinct, market offerings. Consequently, opportunities for risk-free profit – arbitrage – persist longer than they should in efficient markets, indicating that a substantial portion of potential gains remains unrealized due to this fragmentation of information. The findings suggest that addressing semantic ambiguity is crucial for improving price discovery and maximizing the overall efficiency of prediction markets.

The developed semantic matching pipeline functions as a crucial instrument for enhancing price discovery and curtailing arbitrage possibilities within complex markets. By leveraging natural language processing, the pipeline accurately identifies and connects semantically similar, yet syntactically distinct, predictions – a task previously hampered by the nuances of human language. This improved matching capability allows for a more comprehensive aggregation of market sentiment, resulting in prices that more efficiently reflect the collective wisdom of participants. Consequently, opportunities for risk-free profit – arbitrage – are diminished as discrepancies between related predictions are quickly identified and exploited, driving prices toward equilibrium and fostering a more robust and informative market environment. The pipeline’s architecture is designed for scalability, offering a foundational tool not only for immediate improvements in prediction market efficiency but also for adaptation to other decentralized and complex financial systems.

The semantic matching pipeline developed for analyzing prediction markets possesses a versatility that extends far beyond its initial application. This methodology, capable of identifying and quantifying the fragmentation of information across diverse textual sources, is readily adaptable to the rapidly evolving landscape of decentralized finance (DeFi). DeFi platforms, characterized by fragmented liquidity and information dispersed across various protocols and exchanges, often suffer from similar inefficiencies that the pipeline addresses. Moreover, the technique’s core principles – identifying semantic equivalence despite superficial differences – are relevant to any complex trading environment where information is distributed and requires aggregation, including traditional financial instruments, commodity markets, and even algorithmic trading strategies. By providing a robust framework for consolidating and interpreting dispersed data, this approach promises to enhance price discovery, reduce informational asymmetries, and ultimately improve market efficiency across a broad spectrum of financial applications.

Ongoing research prioritizes the development of automated arbitrage execution strategies, aiming to capitalize on the identified semantic inefficiencies in real-time. This involves creating algorithms capable of not only detecting price discrepancies stemming from fragmented semantics, but also of automatically enacting trades to profit from these opportunities. Furthermore, investigations are underway to incorporate dynamic adjustments into the methodology; recognizing that market conditions and semantic landscapes are constantly evolving, the system will adapt its parameters to maintain optimal performance and responsiveness. This includes exploring machine learning techniques to predict shifts in semantic fragmentation and proactively adjust arbitrage thresholds, ultimately enhancing the robustness and profitability of the system in fluctuating environments.

This chord diagram illustrates the relationships between prediction markets, revealing that event equivalence varies significantly across platforms-with ribbon thickness indicating the number of matched pairs and color denoting listing priority-and demonstrating that a substantial proportion of events on each platform ([1] PredictIt, [2] Seer, [3] Omen, [4] Augur) have equivalent counterparts elsewhere.
This chord diagram illustrates the relationships between prediction markets, revealing that event equivalence varies significantly across platforms-with ribbon thickness indicating the number of matched pairs and color denoting listing priority-and demonstrating that a substantial proportion of events on each platform ([1] PredictIt, [2] Seer, [3] Omen, [4] Augur) have equivalent counterparts elsewhere.

The pursuit of seamless information aggregation, as explored in this study of prediction markets, feels remarkably familiar. The paper highlights how a lack of standardized event identity creates price divergence – essentially, everyone’s betting on slightly different things. It’s a modern echo of age-old data integration problems. Donald Knuth observed, “Premature optimization is the root of all evil,” and it’s tempting to see this fragmented landscape as a consequence of rushing toward complex platforms before establishing fundamental semantic interoperability. The elegant theory of efficient markets runs aground on the messy reality of poorly defined events. Everything new is just the old thing with worse docs.

What’s Next?

The demonstrated fragility of event identity across prediction markets feels less like a surprising discovery and more like watching a carefully constructed house of cards succumb to a predictable breeze. The paper highlights a fundamental tension: the desire for open, decentralized information aggregation colliding with the messy reality of semantic interoperability. Future work will undoubtedly focus on technical solutions – standardized event schemas, robust oracle semantics, perhaps even automated event canonicalization. These are worthwhile endeavors, yet feel inherently palliative.

It is difficult to escape the conclusion that every attempted abstraction will eventually leak. A perfectly defined event, resistant to cross-platform arbitrage, will simply attract new forms of exploitation, new edge cases, and the inevitable drift of interpretation. The core problem isn’t merely defining an event, but the inherent subjectivity in how humans perceive and react to it.

The field will likely cycle through increasingly complex protocols, each promising a more ‘truthful’ aggregation of information, only to reveal new avenues for manipulation and divergence. At least it dies beautifully. The real question isn’t whether these systems can achieve perfect price convergence, but whether the cost of pursuing that illusion outweighs the benefits of simply acknowledging the inherent noise.

Original article: https://arxiv.org/pdf/2601.01706.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-06 21:55