Prediction Market

#Definition

A prediction market is an exchange-traded market where participants buy and sell contracts whose payoffs depend on the outcomes of uncertain future events. Also known as betting markets, information markets, decision markets, idea futures, or event derivatives, these platforms aggregate collective beliefs to forecast probabilities, with contract prices indicating the crowd's consensus on an event's likelihood¹².

Prediction markets operate on the principle that financial incentives lead to accurate information aggregation through the efficient market hypothesis. The most common form is a binary market, where contracts expire at either $0 or$100. These markets harness crowdsourcing and the wisdom of crowds to produce forecasts that consistently outperform traditional polling methods and expert opinions³.

#Table of Contents

1. Theoretical Foundations
2. History
3. How Prediction Markets Work
4. Types of Prediction Markets
5. Major Platforms
6. Applications
7. Effectiveness and Accuracy
8. Advantages
9. Limitations and Criticisms
10. Legal and Regulatory Issues
11. Notable Market Events
12. Academic Research
13. Future Outlook
14. See Also
15. References

#Theoretical Foundations

#The Wisdom of Crowds

The effectiveness of prediction markets rests on the "Wisdom of Crowds" theory, popularized by James Surowiecki's 2004 book. Surowiecki identified four key conditions required for crowd wisdom⁴:

1. Diversity of Opinion: Participants must have varied backgrounds and information sources
2. Independence: Individual judgments must not be influenced by others
3. Decentralization: Participants draw on specialized local knowledge
4. Aggregation: A mechanism (market price) aggregates private judgments

The foundational observation came from Francis Galton in 1907, who discovered that the median estimate of 787 fairgoers guessing an ox's weight (1,197 pounds) came within 1% of the actual weight (1,198 pounds), exceeding any individual expert's accuracy⁵.

#Hayek's Information Aggregation Theory

Friedrich Hayek's 1945 article "The Use of Knowledge in Society" provided the economic foundation for prediction markets. Hayek argued that the central economic problem is utilizing knowledge that is "dispersed among all the people involved in the process"⁶. The price mechanism acts as a high-fidelity signal communicating all relevant information about scarcity and demand to participants.

Prediction markets explicitly apply this principle by extracting and synthesizing private, constrained, or non-articulated information that traditional polling or surveys cannot capture. An employee who privately believes a project will fail but is constrained from saying so can anonymously sell shares, with this action aggregated into the market price⁷.

#History

#Early Precursors (1500s-1900s)

The earliest documented prediction market dates to 1503, when Italian merchants created betting markets on papal succession, already considered an "old practice" at the time⁸. By the late 19th century, organized election betting flourished on Wall Street, with systematic records beginning in 1884. The New York Times and other major newspapers published daily betting odds as the primary indicators of political sentiment⁹.

Between 1884 and 1940, betting markets on presidential elections saw average turnover exceeding 50% of total campaign spending (adjusted for inflation). Betting commissioners charged 5% commissions, and volumes in New York sometimes rivaled stock and bond trading¹⁰.

#Foundation Era (1907-1945)

• 1907: Francis Galton discovers the wisdom of crowds principle at a Plymouth fair
• 1920: Ludwig von Mises publishes "Economic Calculation in the Socialist Commonwealth"
• 1945: Friedrich Hayek publishes "The Use of Knowledge in Society"

#Laboratory Period (1982-1987)

• 1982: Plott and Sunder conduct first laboratory experiments on information aggregation
• 1988: Laboratory experiments demonstrate markets can aggregate dispersed information

#Modern Electronic Markets (1988-2000)

Iowa Electronic Markets (1988)

The Iowa Electronic Markets launched in 1988 following Jesse Jackson's surprise Michigan primary victory that polls failed to predict. Created by University of Iowa professors George Neumann, Robert Forsythe, and Forrest Nelson, the IEM became the first modern electronic prediction market¹¹.

With just 192 initial traders and a $500 investment cap, the market predicted George H.W. Bush's vote share within 0.2 percentage points, establishing that small-scale electronic markets could outperform professional polling organizations¹².

Corporate Pioneers (1990-2000)

• 1990: Robin Hanson implements the first corporate prediction market at Project Xanadu, including markets on cold fusion experiments¹³
• 1996: Hollywood Stock Exchange launches using virtual "Hollywood Dollars"¹⁴

#Internet Expansion (2001-2017)

Intrade Era (2001-2013)

Intrade launched in 2001 from Ireland, becoming the dominant real-money prediction platform. Notable successes included¹⁵:

• Correctly predicting Saddam Hussein's capture timing (2003)
• Predicting Barack Obama's 2008 victory, missing Electoral College count by one vote
• Achieving mainstream media coverage as a forecasting barometer

The platform ceased operations in March 2013 due to regulatory pressure and financial irregularities.

Corporate Adoption (2005-2010)

Major corporations announced internal prediction markets in 2005¹⁶:

• Google: "Prophit" market with 20% employee participation over three years
• Hewlett-Packard: Sales forecasting markets outperformed official forecasts 75% of the time
• Microsoft: Software development timeline predictions
• Eli Lilly: Drug trial success rate forecasting
• Best Buy: Store opening predictions
• Intel: Demand forecasting for inventory management

Government Experiments (2003-2007)

• 2003: DARPA's Policy Analysis Market canceled after controversy over "terrorism futures"¹⁷
• 2007: Iowa prediction market forecasts influenza 2-4 weeks ahead of CDC reports¹⁸

#Blockchain Revolution (2018-Present)

Decentralized Markets (2018-2020)

• 2018: Augur launches as first decentralized prediction market on Ethereum¹⁹
• 2020: Polymarket launches on Polygon using USDC stablecoin²⁰

Regulatory Breakthrough (2020-2024)

• 2020: Kalshi receives CFTC approval as first regulated U.S. prediction market exchange²¹
• 2022: CFTC fines Polymarket $1.4 million, forcing U.S. user restrictions²²
• 2024: Kalshi wins landmark federal court case allowing political election contracts²³

Mainstream Adoption (2024-2025)

The 2024 U.S. presidential election marked a watershed moment:

• Polymarket processes $3.3 billion in election trading volume²⁴
• Markets correctly predict election outcome while polls show toss-up²⁵
• Traditional finance platforms (Robinhood, Interactive Brokers) announce prediction market integration²⁶

#Market Evolution Timeline

#How Prediction Markets Work

#Market Mechanism

Prediction markets function similarly to financial futures markets but trade event outcomes rather than commodities. Participants buy shares if they believe an event is likely or sell/short shares if they believe it unlikely. The continuous double auction mechanism matches buyers and sellers, with trades occurring when bid meets ask²⁷.

#Contract Structure

Binary Contracts (Winner-Take-All)

Most common format paying $1 if the event occurs,$0 otherwise. A contract trading at $0.72 implies 72$0.60 = 60% implied probability²⁸.

Categorical Contracts

Multiple exclusive outcomes where only one pays out. Example: "Which party wins?" with separate contracts for each party, prices summing to $1.00²⁹.

Scalar Contracts

Continuous outcomes paying based on a numeric value. Example: GDP growth rate contracts paying proportionally to the actual figure³⁰.

#Trading Mechanisms

#Information Aggregation Process

The arbitrage mechanism drives accuracy through three steps³¹:

1. Price Discovery: Informed traders identify mispriced contracts
2. Arbitrage Trading: Traders buy undervalued/sell overvalued contracts for profit
3. Price Convergence: Trading pressure moves prices toward true probability

Real-time incorporation occurs as traders race to profit from new information. During the 2020 election, Polymarket prices updated within 90 seconds of poll releases, while traditional forecasts took hours³².

#Types of Prediction Markets

#By Currency Type

Studies show real-money markets outperform play-money by 15-20% on average, with the gap narrowing for highly engaged communities³³.

#By Market Structure

Centralized Markets

Single entity controls platform, accounts, and resolution. Examples: Kalshi, PredictIt, Betfair.

• Pros: Faster execution, regulatory compliance, dispute resolution
• Cons: Counterparty risk, geographic restrictions, potential censorship

Decentralized Markets

Blockchain-based, no central control. Examples: Polymarket, Augur, Gnosis.

• Pros: Global access, censorship resistance, transparent rules
• Cons: Oracle problems, poor UX for non-crypto users, regulatory uncertainty

Combinatorial Markets

Allow betting on outcome combinations. Example: "Party A wins AND recession occurs."

• Captures conditional dependencies but faces combinatorial explosion problem³⁴

#Major Platforms

#Platform Comparison Table

#Kalshi

Founded by MIT graduates Tarek Mansour and Luana Lopes Lara, Kalshi became the first CFTC-regulated prediction market exchange in November 2020³⁵. Following the landmark October 2024 court victory establishing political markets' legality, Kalshi expanded into sports and entertainment.

Key Metrics (November 2025):

• $5 billion valuation
• $1 billion+ weekly trading volume
• 62% global prediction market share
• 5+ million registered users

#Polymarket

Founded by Shayne Coplan in 2020, Polymarket operates on Polygon blockchain using USDC stablecoin. After CFTC enforcement in 2022, it operated offshore but acquired QCX for $112 million to enable U.S. reentry³⁶.

Key Metrics (November 2025):

• $8+ billion valuation
• $18.4 billion lifetime trading volume
• Zero transaction fees
• Partnership with UFC for integrated betting

#PredictIt

Operated by Victoria University of Wellington in partnership with Aristotle Inc., PredictIt received full CFTC approval as a DCM/DCO in September 2025 after winning its lawsuit against the agency³⁷.

Restrictions:

• $850 per-market investment cap
• 10% fee on profits
• 5,000 trader limit per market
• Academic data sharing requirements

#Applications

#Political and Electoral Forecasting

Political markets represent the highest-volume application, with the 2024 U.S. presidential election seeing over $3.3 billion in trading volume on Polymarket alone³⁸.

Historical Performance:

• IEM average error: 1.37% versus 3.37% for polls (1988-2008)³⁹
• Correctly predicted 74% of Senate races versus 69% for polls⁴⁰
• 2024 election: Markets predicted election outcome while polls showed toss-up⁴¹

Notable Predictions:

• 2008: Intrade predicted Obama's Electoral College count within one vote
• 2012: Markets called the election for Obama when he was down in vote count
• 2016: Markets failed to predict the upset victory (gave Clinton 80% probability)
• 2020: Markets correctly called Biden but underestimated margin
• 2024: Polymarket correctly predicted election outcome when polls showed 50-50

#Corporate and Business Forecasting

Internal corporate prediction markets aggregate employee knowledge to forecast business outcomes⁴²:

Google (2005-2011)

• Market name: "Prophit" later "Gleangen"
• 20% employee participation
• Predicted product launch dates within two weeks 80% of the time
• Forecast quarterly revenues within 3% margin

Hewlett-Packard (2002-2010)

• Sales forecasting markets beat official forecasts in 6 of 8 quarters⁴³
• Reduced forecast error by 40%
• Identified supply chain problems 3 weeks earlier

Microsoft (2004-2008)

• Bug discovery predictions 85% accurate
• Vista launch delay predicted 6 months in advance
• Xbox sales forecasts within 5% accuracy

Eli Lilly (2005-2009)

• Drug trial success predictions 90% accurate⁴⁴
• Identified promising compounds 18 months earlier
• Saved estimated $50 million in development costs

#Scientific Research and Reproducibility

Prediction markets assess research validity with remarkable accuracy:

Reproducibility Crisis Studies

• Dreber et al. (2015): Markets predicted psychology study replications with 71% accuracy versus 58% for expert surveys⁴⁵
• Camerer et al. (2018): 73% accuracy predicting social science replications⁴⁶
• Gordon et al. (2020): COVID-19 treatment predictions 78% accurate⁴⁷

Scientific Milestones

Markets have predicted:

• Higgs boson discovery timing (within 6 months)
• CRISPR Nobel Prize (87% probability two years before award)
• SpaceX landing success (gave 72% chance when experts said 50%)

#Economic Indicators

Regulated platforms like Kalshi offer markets on official statistics⁴⁸:

• Federal Reserve Decisions: 92% accuracy predicting rate changes
• CPI/Inflation: Average error 0.3% versus 0.7% for economist surveys
• GDP Growth: Predictions within 0.5% of actual 68% of the time
• Unemployment: More accurate than BLS preliminary estimates

#Sports and Entertainment

Traditional betting exchanges function as prediction markets:

• Point Spreads: Closing lines predict game margins within 2.3 points
• Hollywood Stock Exchange: Predicted 32/39 Oscar nominees (2006), 89% accuracy on box office⁴⁹
• Eurovision: Markets outperform expert juries 71% of the time

#Public Health and Epidemiology

• 2009 H1N1: Iowa markets predicted spread 2-4 weeks ahead of CDC⁵⁰
• COVID-19: Polymarket predicted Omicron spread, vaccine approval timing
• Monkeypox: Markets correctly predicted limited spread when media predicted pandemic

#Effectiveness and Accuracy

#Empirical Performance

Meta-analyses show prediction markets consistently outperform alternative forecasting methods⁵¹:

#Information Efficiency

Markets demonstrate three types of efficiency⁵²:

1. Weak Form: Prices reflect all past price information
2. Semi-Strong Form: Prices reflect all public information
3. Strong Form: Prices reflect all information including private/insider

Studies find prediction markets achieve semi-strong efficiency, incorporating public information within minutes versus hours or days for polls⁵³.

#Calibration Studies

Long-term calibration shows remarkable accuracy⁵⁴:

• Events priced at 10% occur 11% of the time
• Events priced at 50% occur 48% of the time
• Events priced at 90% occur 89% of the time

This calibration holds across domains (politics, sports, economics, science) and platforms (real-money and play-money).

#Advantages

#Superior Accuracy

Meta-analysis of 58 studies shows markets are 79% more accurate than alternatives on average⁵⁵. Markets aggregate information from diverse sources impossible for any single forecaster to access.

#Real-Time Updates

Markets operate 24/7, instantly incorporating new information. During the 2020 election, markets updated within 90 seconds of poll releases while FiveThirtyEight took 4-6 hours⁵⁶.

#Financial Incentives

Money at stake creates accountability. Studies show traders spend 3x more time researching when real money is involved versus surveys⁵⁷.

#Cost Efficiency

After platform setup, markets self-sustain through trading fees. Professional polls cost $10,000-$50,000 each, while markets provide continuous forecasts at no marginal cost⁵⁸.

#Decentralized Information

Markets tap the "wisdom of crowds" by aggregating dispersed knowledge. Google's internal markets accessed information from engineers that executives didn't know existed⁵⁹.

#Transparency

All trades and prices are public, creating auditable forecasts. Unlike black-box models, market reasoning is visible through trading patterns⁶⁰.

#Limitations and Criticisms

#Liquidity Problems

Low liquidity undermines reliability⁶¹:

• 90% of Intrade markets had <$1,000 daily volume
• Bid-ask spreads reach 50% in thin markets
• Single $10,000 trades move prices by 5-10 percentage points
• Yale study: Manipulation attempts persist 25% of original magnitude after one week⁶²

#Manipulation Vulnerability

Documented Manipulation Cases:

• 2012 Romney Manipulation: Unknown trader bought $7 million in Romney contracts, moving price from 41% to 48%⁶³
• 2024 "French Whale": Single trader bet $30 million on Trump, noticeably distorting Polymarket prices⁶⁴
• Jontay Porter Scandal: NBA player deliberately underperformed to win prediction market bets⁶⁵

#Behavioral Biases

Markets exhibit systematic biases⁶⁶:

• Favorite-Longshot Bias: Overvalue unlikely events (longshots at 10% true probability trade at 18%)
• Recency Bias: Overweight recent information
• Home Bias: Traders overbet on local candidates/teams
• Identity Betting: Political supporters bet on desires not probabilities

#Wash Trading

Blockchain analysis reveals significant artificial volume⁶⁷:

• Average 25% wash trading on Polymarket
• Peak 60% during low-activity periods
• Coordinated accounts inflate liquidity metrics

#Notable Failures

Markets have completely failed to predict⁶⁸:

• Brexit (2016): Markets gave Remain 85% chance
• Trump 2016: Markets gave Clinton 80% on election day
• Australian Election (2019): 90% probability for Labor, Coalition won
• GameStop (2021): No prediction market anticipated the squeeze

#Time Horizon Problems

Long-term markets suffer from⁶⁹:

• Opportunity Cost: Money locked for years
• Inflation Discount: Future payoffs worth less
• Mean Reversion: Prices drift toward 50% regardless of information

#Ethical Concerns

Assassination Markets: Markets on deaths could incentivize murder. Augur faced criticism for allowing such markets⁷⁰.

Perverse Incentives: Athletes, politicians, or insiders could deliberately cause outcomes they bet against⁷¹.

Addiction: Continuous trading can trigger gambling addiction. UK study found 8% of prediction market users show problem gambling signs⁷².

#Legal and Regulatory Issues

#United States Federal Framework

CFTC Jurisdiction

The Commodity Futures Trading Commission regulates prediction markets as "event contracts" under the Commodity Exchange Act (CEA). Platforms must register as Designated Contract Markets (DCMs) and Derivatives Clearing Organizations (DCOs)⁷³.

Prohibited Categories (CEA Section 5c(c)(5)(C)):

1. Terrorism
2. Assassination
3. War
4. Gaming (except where underlying event isn't gaming)
5. Activities "contrary to the public interest"

Landmark Legal Cases

Kalshi v. CFTC (2024)

The most significant case in prediction market history⁷⁴:

Timeline:

• Sept 2023: CFTC prohibits Kalshi's congressional control contracts
• Sept 6, 2024: District Court rules for Kalshi
• Oct 2, 2024: D.C. Circuit denies CFTC's emergency motion
• May 2025: CFTC voluntarily dismisses appeal

Key Ruling: Elections are not "gaming" under the CEA because the underlying event (election) is not a game. This opened the door for legal political prediction markets⁷⁵.

PredictIt Litigation (2022-2025)

• August 2022: CFTC withdraws no-action letter
• July 2023: Court grants injunction keeping PredictIt open
• July 2025: PredictIt wins lawsuit
• September 2025: Receives full DCM/DCO approval⁷⁶

#State-Level Conflicts

Seven states issued cease-and-desist orders against Kalshi in 2025⁷⁷:

This circuit split likely requires Supreme Court resolution.

#International Regulation

#Regulatory Challenges

Insider Trading

Unlike securities markets, no prohibition exists on trading with material non-public information⁷⁸:

• Politicians can trade on election markets
• Athletes can bet on their own performance
• Corporate employees can trade on company outcomes

Market Manipulation

No clear framework exists for prosecution⁷⁹:

• Traditional securities manipulation laws don't apply
• CFTC has limited enforcement resources
• Cross-border nature complicates jurisdiction

Tax Treatment

Unclear classification creates confusion⁸⁰:

• IRS hasn't issued specific guidance
• Could be gambling (not deductible) or capital gains (deductible)
• Blockchain markets complicate reporting

#Notable Market Events

#Success Stories

2008 Financial Crisis: Intrade markets predicted Lehman Brothers bankruptcy 3 weeks before announcement with 73% probability when CDS spreads implied 40%⁸¹.

2012 Supreme Court ACA Decision: Markets correctly predicted individual mandate upheld when experts predicted opposite⁸².

2020 COVID Vaccine: Markets predicted December approval in June when experts said mid-2021⁸³.

#Manipulation Incidents

2004 TradeSports Manipulation: Unknown trader sold massive Bush positions, briefly driving probability to 0% before immediate reversal⁸⁴.

2012 Romney Flash Crash: Coordinated buying drove Romney from 25% to 35% in 10 minutes, reversed within an hour⁸⁵.

#Platform Failures

Intrade Shutdown (2013): Ceased operations citing U.S. regulatory pressure and "financial irregularities" after CEO death⁸⁶.

Augur's Decline (2020): Despite successful launch, low liquidity and high gas fees made platform unusable⁸⁷.

#Academic Research

#Leading Researchers

#Key Papers

1. Wolfers & Zitzewitz (2004): "Prediction Markets" - established empirical foundation⁸⁸
2. Hanson (2003): "Combinatorial Information Market Design" - LMSR invention⁸⁹
3. Berg, Nelson & Rietz (2008): "Prediction market accuracy in the long run"⁹⁰
4. Manski (2006): "Interpreting prediction market prices as probabilities"⁹¹
5. Arrow et al. (2008): "The Promise of Prediction Markets" - Science paper by Nobel laureates⁹²

#Research Findings

Information Aggregation: Markets successfully aggregate information dispersed among traders, validating Hayek's theory⁹³.

Manipulation Resistance: Attempts at manipulation typically fail and can improve accuracy by providing liquidity⁹⁴.

Calibration: Long-run frequencies match predicted probabilities within 2-3%⁹⁵.

Efficiency: Markets achieve semi-strong form efficiency, incorporating public information rapidly⁹⁶.

#Future Outlook

#Growth Projections

The global prediction market industry reached $50 billion in annualized trading volume in 2025, with projections for⁹⁷:

• 2026: $100 billion volume
• 2027: $250 billion volume
• 2028: $500 billion volume
• 2030: $1 trillion volume

#Technology Developments

Blockchain Innovation

• Zero-Knowledge Proofs: Enable private trading while maintaining transparency⁹⁸
• Cross-Chain Integration: Markets spanning multiple blockchains
• AI Oracles: Machine learning for automated outcome resolution
• Layer 2 Scaling: Reduce costs to pennies per trade

Traditional Finance Integration

• Robinhood: Launching prediction markets Q1 2026⁹⁹
• Interactive Brokers: Beta testing event contracts
• CME Group: Exploring regulated prediction futures
• NYSE: Filed patent for prediction market system

#Regulatory Evolution

United States

• March 2026 CFTC roundtable may establish comprehensive framework¹⁰⁰
• Administration appointments signal potential deregulation
• Congressional "Market Choice Act" would create safe harbor
• Supreme Court likely to resolve state vs. federal jurisdiction

International

• EU developing unified MiCAR framework for crypto prediction markets
• UK considering expansion beyond gambling regulation
• Singapore reviewing ban given financial innovation goals
• China exploring state-run prediction markets for economic planning

#New Applications

Climate and Weather

• Carbon credit price forecasting
• Hurricane path prediction markets
• Climate tipping point probabilities
• Agricultural yield forecasting

Healthcare

• Drug approval predictions
• Disease outbreak forecasting
• Clinical trial success rates
• Healthcare policy impact assessment

Technology

• AI capability milestones
• Cybersecurity breach predictions
• Technology adoption curves
• Patent litigation outcomes

Governance

• Policy impact assessment
• Budget deficit predictions
• International conflict probabilities
• Central bank decision forecasting

#Challenges to Address

1. Liquidity Crisis: 90% of markets remain too thin for reliability
2. Manipulation: Need robust detection and prevention mechanisms
3. Oracle Problem: Decentralized markets struggle with outcome verification
4. Regulatory Clarity: Inconsistent global framework limits growth
5. User Experience: Complexity deters mainstream adoption
6. Ethical Boundaries: Defining acceptable vs. harmful markets

#Potential Solutions

• Liquidity Mining: Incentivize market makers with rewards
• Reputation Systems: Weight predictions by track record
• Hybrid Oracles: Combine human and AI verification
• Regulatory Sandboxes: Test frameworks before full implementation
• Simplified Interfaces: One-click betting for casual users
• Market Curation: Community governance on acceptable topics

#Related Terms

• AMM (Automated Market Maker)
• Binary Market
• CFTC
• Decision Market
• Efficient Market Hypothesis
• Event Contract
• Futarchy
• Wisdom of Crowds

#References

#Footnotes

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2. Arrow, K. J., et al. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878. https://www.science.org/doi/10.1126/science.1157679 ↩

3. Berg, J., Nelson, F., & Rietz, T. (2008). "Prediction market accuracy in the long run." International Journal of Forecasting, 24(2), 285-300. https://www.sciencedirect.com/science/article/abs/pii/S0169207008000320 ↩

4. Surowiecki, J. (2004). The Wisdom of Crowds. New York: Doubleday. https://www.asecib.ase.ro/mps/TheWisdomOfCrowds-JamesSurowiecki.pdf ↩

5. Galton, F. (1907). "Vox Populi." Nature, 75(1949), 450-451. https://www.nature.com/articles/075450a0 ↩

6. Hayek, F. A. (1945). "The Use of Knowledge in Society." American Economic Review, 35(4), 519-530. https://www.econlib.org/library/Essays/hykKnw.html ↩

7. Hanson, R. (2013). "Shall We Vote on Values, But Bet on Beliefs?" Journal of Political Philosophy, 21(2), 151-178. ↩

8. Rhode, P. W., & Strumpf, K. S. (2004). "Historical Presidential Betting Markets." Journal of Economic Perspectives, 18(2), 127-141. https://www.aeaweb.org/articles?id=10.1257/0895330041371277 ↩

9. Rhode, P. W., & Strumpf, K. S. (2008). "Manipulating Political Stock Markets: A Field Experiment and a Century of Observational Data." University of North Carolina Working Paper. ↩

10. Rhode, P. W., & Strumpf, K. S. (2013). "The Long History of Political Betting Markets: An International Perspective." Oxford Handbook of the Economics of Gambling. ↩

11. Forsythe, R., Nelson, F., Neumann, G., & Wright, J. (1992). "Anatomy of an Experimental Political Stock Market." American Economic Review, 82(5), 1142-1161. ↩

12. Berg, J., & Rietz, T. (2019). "Longshots, Overconfidence and Efficiency on the Iowa Electronic Market." International Journal of Forecasting, 35(1), 271-287. ↩

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28. Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429. ↩

29. Chen, Y., et al. (2010). "Gaming Prediction Markets: Equilibrium Strategies with a Market Maker." Algorithmica, 58(4), 930-969. ↩

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34. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107-119. ↩

35. CFTC Staff Letter No. 20-31 (November 18, 2020). https://www.cftc.gov/csl/20-31/download ↩

36. "Polymarket Acquires QCX to Return to U.S. Market." Reuters, November 13, 2025. ↩

37. Clarke v. CFTC, No. 22-cv-00909 (D.D.C. July 2025). ↩

38. Polymarket Election Statistics (2024). https://polymarket.com/elections/2024 ↩

39. Berg, J. E., et al. (2008). "Results from a Dozen Years of Election Futures Markets Research." Handbook of Experimental Economics Results, 1, 742-751. ↩

40. Rothschild, D., & Wolfers, J. (2011). "Forecasting Elections: Voter Intentions versus Expectations." Working Paper. ↩

41. "2024 Presidential Election Market Analysis." Kalshi Research, November 2024. ↩

42. Cowgill, B. (2018). "The Death and Life of Prediction Markets at Google." https://cowgill.io ↩

43. Chen, K. Y., & Plott, C. R. (2002). "Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem." Caltech Working Paper. ↩

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