The DOI matches its Crossref record; 17 of the 89 references it cites were found in a registry or an index, and 72 are not checked yet.
Prediction Markets
Open accessAbstract
We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market's expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market's beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Prediction markets have also been successfully used in several other fields such as sports, entertainment, and politics ( 25 , 59-62 ).”
From Evaluating replicability of laboratory experiments in economics (2016) · cites this paper as [59]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Sample Selection Bias as a Specification Error
Heckman, J. J. (1979) · Econometrica · doi:10.2307/1912352 · Cited by 28,779
Shleifer, A., & Vishny, R. W. (1997) · The Journal of Finance · doi:10.1111/j.1540-6261.1997.tb03807.x · Cited by 5,291
RUBINSTEIN, M. (1994) · The Journal of Finance · doi:10.1111/j.1540-6261.1994.tb00079.x · Cited by 1,752
Forecasting: theory and practice
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babaï, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., et al. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.11.001 · Cited by 1,187
Anomalies: Parimutuel Betting Markets: Racetracks and Lotteries
Thaler, R. H., & Ziemba, W. T. (1988) · Journal of Economic Perspectives · doi:10.1257/jep.2.2.161 · Cited by 621
Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets
Plott, C. R., & Sunder, S. (1988) · Econometrica · doi:10.2307/1911360 · Cited by 608
Repugnance as a Constraint on Markets
Roth, A. E. (2007) · The Journal of Economic Perspectives · doi:10.1257/jep.21.3.37 · Cited by 580
Plott, C. R., & Sunder, S. (1982) · Journal of Political Economy · doi:10.1086/261084 · Cited by 565
Combinatorial Information Market Design
Hanson, R. D. (2003) · Inf. Syst. Frontiers · doi:10.1023/a:1022058209073 · Cited by 554
Wishes, expectations and actions: a survey on price formation in election stock markets
Forsythe, R. S., Rietz, T. A., & Ross, T. W. (1999) · Journal of Economic Behavior & Organization · doi:10.1016/s0167-2681(99)00027-x · Cited by 283
Research works printed but not linked: 1
- Athanasoulis Stefano, 1999, Economic Policy Review., 5, 21
Cited by
Sorted by citations
Items 801–889 of 889
Shiller, R. J. (2008) · The New Palgrave Dictionary of Economics · doi:10.1007/978-1-349-58802-2_524
Balzano, W., & Miranda, P. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-10344-4_19
Jin, Z., Kang, J., Qian, L., & Qin, B. (2025) · The British Accounting Review · doi:10.1016/j.bar.2025.101812
Verification of the Effect of Introducing an Agent in a Prediction Market
Yamamoto, T., & Itō, T. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-16098-1_4
Aligning Smart Education With Smart Economy
Marinosyan, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-7922-7.ch008
Behavioral Limits to Complete MarketsPreprint
Magnani, J., & Wang, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5898883
Mitigating manipulation in committees: Just let them talk!Preprint
Albrecht, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5994274
‘Expertise’ shouldn’t be a bad word – expert consensus guides science and society
Altman, M., & Cohen, P. N. (2026) · doi:10.64628/aai.jwn3yj4fu
Market Expectations of Narrative Disclosure: Initial Evidence from Prediction MarketsPreprint
Glaze, J. L., López-Vilaró, J., & Stephan, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6076807
DeSci and Tokenized Science Economies
Shilina, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3371-7.ch007
Who Profits from Prediction Markets? Execution, not InformationPreprint
Vedova, J. D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6191618
Tsang, K. P., & Yang, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6248859
The Anatomy of a Blockchain Prediction Market: Polymarket in the 2024 U.S. Presidential ElectionPreprint
Yang, Z., & Tsang, K. P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6336679
Skilled Liquidity Provision in Prediction Markets: Evidence from 150 Million TradesPreprint
Yang, H.-C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6396698
(2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49002-6.00012-7
Aktuğ, E., & Torul, O. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6516491
Effects of information quantity and quality on collective decisions in human groups
Jayles, B. (2017) · theses.fr (ABES) · doi:10.70675/84c2d42dzd993z4751z9395z6e892ab73438
The AI Lemons Problem in the Prediction MarketsPreprint
He, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6467679
Bendezu, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6434079
Skilled Liquidity Provision in Prediction Markets: Evidence from 150 Million TradesPreprint
Yang, H.-C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6556613
Abraham Mathews, T., & Komera, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6591911
Nunes, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6446502
Adverse Selection in Prediction Markets: Evidence from KalshiPreprint
Bartlett, R. P., & O’Hara, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6615739
Unknown Rational Expectations: Evidence from NFL Prediction MarketsPreprint
Jha, A., Kemper, J., Abarca, A., & Papazyan, F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6642738
Beating the Earnings Game: Why Do Prediction Markets Outperform Professional Analysts?Preprint
Rabetti, D., shao, J., & Zhang, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6649938
Beyond the Wisdom of the Crowd: Concentrated Informed Trading in Earnings Prediction MarketsPreprint
Cheong, W. C., & Tamayo, A. M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6685139
When Resolution Isn't Settlement: Payoff Measurement in Prediction MarketsPreprint
Du, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6746923
Nechepurenko, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6748278
Nechepurenko, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6751284
Geographies of Prediction Markets: Gamifying the Future
Mills, S. (2026) · Geographical Journal · doi:10.1111/geoj.70093
A Taxonomy of Event-Linked Perpetual Futures: Variant Designs Beyond the Single-Market Binary CasePreprint
Nechepurenko, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6748298
Privacy, Voting, and the Wisdom of Crowds
Astashkina, E., Momot, R., & Salikhov, M. (2026) · Operations Research · doi:10.1287/opre.2023.0671
Gill, B. S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6809881
Optimal regulation of insider trading in prediction marketsPreprint
Gill, B. S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6868059
Lê, A. Q. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6933527
Gruener, L. P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6863546
Informational inertia in a decentralized prediction market: Evidence from the May 2024 CPI leak
Aktuğ, E., & Torul, O. (2026) · Finance research letters · doi:10.1016/j.frl.2026.110378
LLM-Based Merger Withdrawal Prediction and Model–Market Disagreement Around Climate Risk
Shohrab, M., Ye, Z., Liu, Y., & Yu, D. (2026) · Journal of risk and financial management · doi:10.3390/jrfm19070488
Aktuğ, E., & Torul, O. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7051166
Hadsell, L. (2026) · Springer eBooks · doi:10.1007/978-3-032-21916-9_2
Election Probabilities and the Treasury Yield CurvePreprint
Aktuğ, E., & Torul, O. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6972078
Prices, Probabilities, and Parlays: Systematic Bias in Sports Prediction Markets
Moshrefi, N. (2026) · IEEE Conference on Computational Intelligence for Financial Engineering & Economics · doi:10.1109/cifer67845.2026.11692409
Cao, D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7049119
Protectionist U.S. trade policies and the cross-section of emerging market currency returns
de Boer, J., & Eichler, S. (2026) · Emerging Markets Review · doi:10.1016/j.ememar.2026.101527
О сходимости прогнозов на рынках предсказаний
Badulina, N., Zhitlukhin, M. V., & Shatilovich, D. V. (2026) · Теория вероятностей и ее применения · doi:10.4213/tvp5950
Sandjaja, T. A., Heykal, M., & Wiputra, R. (2026) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict71158.2026.11620848
Gill, B. S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6809849
Mouynes, E. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7114978
Ando, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7151878
Learning from Prediction Markets: The Transmission of Information and Noise to Traditional AssetsPreprint
Itay Goldstein, B., Li, Y., & Wang, C. M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7198379
Axient: Debt-free Finality for Leveraged Binary Event MarketsPreprint
Nechepurenko, M. O. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7216080
Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles
Douven, I. (2026) · ACM Transactions on Social Computing · doi:10.1145/3839337
Trading the Jump in Prediction MarketsPreprint
Gill, B. S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7245879
Visotsky, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7327845
Price Formation and Information Flow in Earnings Prediction MarketsPreprint
Li, S. Z., & Luan, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7324239
Aktuğ, E., & Torul, O. (2026) · Finance research letters · doi:10.1016/j.frl.2026.110664
Singh, S., Mittal, A., Kumar, S., Chandra, C., & Singh, S. (2026) · International Journal of Modelling and Simulation · doi:10.1080/02286203.2026.2727072
Prediction markets in dermatology: Ethical considerations for physicians
Jonnalagadda, A., Desai, A. D., Zhou, A. E., & Grant-Kels, J. M. (2026) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2026.09.001
Elishar-Malka, V., Saks, D. W., & Ariel, Y. (2026) · Social Science Quarterly · doi:10.1111/ssqu.70198
The Event Clock: Identifying Physical Information from State PricesPreprint
Hanke, M., Schadner, W., Stöckl, S., & Weissensteiner, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7412359
Lei, S., & Huang, Q. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7420398
Skill-Driven Information Discovery: Evidence from Earnings Prediction MarketsPreprint
Diaz, Z., & You, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7454941
Skovgaard, S. S., Junge, N. W., & Ibragimov, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7461299
NIL compensation and corruption risk: evidence from low-profile NCAA programmes
Ashby, N. J. (2026) · Applied Economics Letters · doi:10.1080/13504851.2026.2729278
Lee, S., Lee, Y., & Lee, J. (2026) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2026.101259
Contracting for Decision-Relevant BeliefsPreprint
Cowgill, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7491398
Contracting for Decision-Relevant BeliefsPreprint
Cowgill, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7496540
Market Response to Racial Uprisings
Ba, B., Rivera, R., & Whitefield, A. (2023) · National Bureau of Economic Research · doi:10.3386/w31606
Crowdsourcing and Policymaking
Ramos, J., & García, G. (2025) · Springer eBooks · doi:10.1007/978-3-319-31816-5_4446-1
Can Prediction Markets Price Reality? Insights into the Political Economy of Post-Truth
Ugalde, E. G. (2026) · International Critical Thought · doi:10.1080/21598282.2026.2733694
Prediction Markets in Clinical Trials and Drug Approval
Kanter, G. P. (2026) · JAMA Health Forum · doi:10.1001/jamahealthforum.2026.2953
Prediction markets as a collective expectation signal for disease surveillance
Neto, O. L. (2026) · npj Digital Public Health · doi:10.1038/s44482-026-00042-6
Reichenbach, F., & Walther, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7552098
Cryptocurrency prediction markets through the derivatives lens: Evidence from Kalshi and Polymarket
Lee, S., Lee, Y., & Lee, J. (2026) · International Review of Financial Analysis · doi:10.1016/j.irfa.2026.105433
Limits to Arbitrage and Prediction Market Efficiency: the Case of PredictIt
Zitzewitz, E. W. (2026) · National Bureau of Economic Research · doi:10.3386/w35845
Self-citationAlbiez, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7563559
Clinical Trial Prediction Markets — Information, at What Cost?
Vogel, M., Rona, Y., & Wouters, O. J. (2026) · New England Journal of Medicine · doi:10.1056/nejmp2610547
Combinatorial Arbitrage in Prediction MarketsPreprint
Zhai, D., & Shi, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7587019
Dickerson, J., Shinitzky, H., Peer, M., Ben‐Shachar, M. S., & Gilead, M. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2615627123
Learning Before Scheduled Events: Prediction Markets, State Prices, and Option ValuationPreprint
Hanke, M., Schadner, W., Stöckl, S., & Weissensteiner, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7647658
How Much Is an Ace Pitcher Worth? Evidence From Major League Baseball Betting Markets
Lamb, J. M. (2026) · Southern Economic Journal · doi:10.1002/soej.70072
Learning and information diffusion in decentralized markets: experiments and a computational model
Hanaki, N., Iori, G., & Vassallo, P. (2026) · Journal of Economic Interaction and Coordination · doi:10.1007/s11403-026-00504-3
Calibrated agents as market makers: automated liquidity provision for long-tail prediction markets
Shah, N. (2026) · Digital Finance · doi:10.1007/s42521-026-00233-z
Wagering on Unfinished Experiments—Prediction Markets and Clinical Trial Integrity
Lusk, J. B., Califf, R. M., Návar, A. M., & O'Brien, E. C. (2026) · JAMA · doi:10.1001/jama.2026.19361
Metadata
- Authors
- Wolfers, J., & Zitzewitz, E.
- Affiliations
- National Bureau of Economic Research (US)
- Stanford University (US)
- ORCID
- Wolfers, Justin
- Venue
- Journal of Economic Perspectives
- Publisher
- American Economic Association
- Published
- 2004-05-01
- Volume
- 18
- Issue
- 2
- Pages
- 107-126
- ISSN
- 0895-3309 (ISSN-L)
- OpenAlex
- W4256232447
- Cited by
- 889
- References
- 17
- Citations by year
- 2026: 85 · 2025: 36 · 2024: 33 · 2023: 31 · 2022: 44 · 2021: 31 · 2020: 28 · 2019: 33 · 2018: 42 · 2017: 39 · 2016: 25 · 2015: 44 · 2014: 42 · 2013: 56 · 2012: 67
- FWCI
- 43.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Economic Perspectives (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- prediction markets, information aggregation, forecast accuracy, market design, regression coefficients, correlation versus causation, probability elicitation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review