Select bibliography of prediction market literature

Kim Kaivanto
January 2022 Prediction markets

By author. Updated periodically.

Abernethy, J., Chen, Y. and Vaughan, J.W. (2013a) ‘Efficient Market Making via Convex Optimization, and a Connection to Online Learning’, ACM Transactions on Economics and Computation, 1(2), pp. 1–39. Available at: https://doi.org/10.1145/2465769.2465777.
Abernethy, J., Chen, Y. and Vaughan, J.W. (2013b) ‘Efficient Market Making via Convex Optimization, and a Connection to Online Learning’, May. Available at: https://dl.acm.org/doi/10.1145/2465769.2465777 (Accessed: 1 May 2024).
Abramowicz, M.B. (2007) ‘The Hidden Beauty of the Quadratic Market Scoring Rule: A Uniform Liquidity Market Maker, with Variations’, GW Law Faculty Publications & Other Works, 244.
Agrawal, S., Megiddo, N. and Armbruster, B. (2010) ‘Equilibrium in prediction markets with buyers and sellers’, Economics Letters, 109(1), pp. 46–49. Available at: https://doi.org/10.1016/j.econlet.2010.08.017.
Aliakbari, E. and McKitrick, R. (2018) ‘Information aggregation in a prediction market for climate outcomes’, Energy Economics, 74, pp. 97–106. Available at: https://doi.org/10.1016/j.eneco.2018.06.002.
Almenberg, J., Kittlitz, K. and Pfeiffer, T. (2009) ‘An Experiment on Prediction Markets in Science’, PLoS ONE. Edited by J.M. Schnur, 4(12), p. e8500. Available at: https://doi.org/10.1371/journal.pone.0008500.
Armstrong, J.S. (2008) ‘Methods to Elicit Forecasts from Groups: Delphi and Prediction Markets Compared’, SSRN Electronic Journal [Preprint]. Available at: https://doi.org/10.2139/ssrn.1153124.
Arrow, K.J. et al. (2008) ‘The Promise of Prediction Markets’, Science, 320(5878), pp. 877–878. Available at: https://doi.org/10.1126/science.1157679.
Atanasov, P. et al. (2017) ‘Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls’, Management Science, 63(3), pp. 691–706. Available at: https://doi.org/10.1287/mnsc.2015.2374.
Atanasov, P. et al. (2024) ‘Crowd Prediction Systems: Markets, Polls, and Elite Forecasters’, International Journal of Forecasting [Preprint].
Barbu, A. and Lay, N. (2013) ‘Artificial prediction markets for lymph node detection’, in 2013 E-Health and Bioengineering Conference (EHB). 2013 E-Health and Bioengineering Conference (EHB), IASI, Romania: IEEE, pp. 1–7. Available at: https://doi.org/10.1109/EHB.2013.6707376.
Berg, H.G. and Proebsting, T.A. (2012) ‘Patent: Combined estimate contest and prediction market’.
Berg, J.E., Nelson, F.D. and Rietz, T.A. (2008) ‘Prediction market accuracy in the long run’, International Journal of Forecasting, 24(2), pp. 285–300. Available at: https://doi.org/10.1016/j.ijforecast.2008.03.007.
Berg, J.E. and Rietz, T.A. (2014) ‘Market Design, Manipulation, and Accuracy in Political Prediction Markets: Lessons from the Iowa Electronic Markets’, PS: Political Science & Politics, 47(02), pp. 293–296. Available at: https://doi.org/10.1017/S1049096514000043.
Bernanke, B.S. and Woodford, M. (1997) ‘Inflation Forecasts and Monetary Policy’, Journal of Money, Credit and Banking, 29(4), pp. 653–684.
Beygelzimer, A., Langford, J. and Pennock, D. (2012) ‘Learning Performance of Prediction Markets with Kelly Bettors’. arXiv. Available at: http://arxiv.org/abs/1201.6655 (Accessed: 1 May 2024).
Bossaerts, F. et al. (2022) ‘Price Formation in Field Prediction Markets: the Wisdom in the Crowd’. arXiv. Available at: http://arxiv.org/abs/2209.08778 (Accessed: 1 May 2024).
Bottazzi, G. and Giachini, D. (2019) ‘Far from the madding crowd: collective wisdom in prediction markets’, Quantitative Finance, 19(9), pp. 1461–1471. Available at: https://doi.org/10.1080/14697688.2019.1622285.
Botvinik-Nezer, R. et al. (2020) ‘Variability in the analysis of a single neuroimaging dataset by many teams’, Nature, 582(7810), pp. 84–88. Available at: https://doi.org/10.1038/s41586-020-2314-9.
Brahma, A. et al. (2012) ‘A bayesian market maker’, in Proceedings of the 13th ACM Conference on Electronic Commerce. EC ’12: ACM Conference on Electronic Commerce, Valencia Spain: ACM, pp. 215–232. Available at: https://doi.org/10.1145/2229012.2229031.
Brown, A., Reade, J.J. and Vaughan Williams, L. (2019) ‘When are prediction market prices most informative?’, International Journal of Forecasting, 35(1), pp. 420–428. Available at: https://doi.org/10.1016/j.ijforecast.2018.05.005.
Buckley, P., Garvey, J. and McGrath, F. (2011) ‘A case study on using prediction markets as a rich environment for active learning’, Computers & Education, 56(2), pp. 418–428. Available at: https://doi.org/10.1016/j.compedu.2010.09.001.
Calhoun, G. (2022) ‘Prediction Markets Failed The Midterm (Election) Exams’, Forbes, 21 October. Available at: https://www.forbes.com/sites/georgecalhoun/2022/11/14/the-un-wisdom-of-crowds-prediction-markets-failed-their-midterm-exams/.
Calhoun, G. (2024) ‘Prediction Markets: How Reliable Are They Really? (Part 1)’, Forbes, 23 October. Available at: https://www.forbes.com/sites/georgecalhoun/2024/10/23/prediction-markets-how-reliable-are-they-really-part-1/.
Carvalho, A.V.C. et al. (2023) ‘A logarithmic market scoring rule agent-based model to evaluate prediction markets’, Journal of Evolutionary Economics, 33(4), pp. 1303–1343. Available at: https://doi.org/10.1007/s00191-023-00822-w.
Cerf, M., Matz, S.C. and MacIver, M.A. (2023a) ‘Participating in a climate futures market increases support for costly climate policies’, Nature Climate Change, 13(6), pp. 511–512. Available at: https://doi.org/10.1038/s41558-023-01677-6.
Cerf, M., Matz, S.C. and MacIver, M.A. (2023b) ‘Participating in a climate prediction market increases concern about global warming’, Nature Climate Change, 13(6), pp. 523–531. Available at: https://doi.org/10.1038/s41558-023-01679-4.
Chakraborty, M. et al. (2013) ‘Instructor Rating Markets’, in Proc. of 27th AAAI Conference on Artificial Intelligence (AAAI), pp. 159–165.
Chakraborty, M. and Das, S. (2015) ‘Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents’, NIPS’15: Proceedings of the 28th International Conference on Neural Information Processing Systems, 2. Available at: https://dl.acm.org/doi/10.5555/2969442.2969503.
Chakraborty, M. and Das, S. (2016) ‘Trading on a Rigged Game: Outcome Manipulation In Prediction Markets’, in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 158–164.
Chen, Y. et al. (2008) ‘Complexity of combinatorial market makers’, in Proceedings of the 9th ACM conference on Electronic commerce. EC ’08: ACM Conference on Electronic Commerce, Chicago Il USA: ACM, pp. 190–199. Available at: https://doi.org/10.1145/1386790.1386822.
Chen, Y. et al. (2010) ‘Gaming Prediction Markets: Equilibrium Strategies with a Market Maker’, Algorithmica, 58(4), pp. 930–969. Available at: https://doi.org/10.1007/s00453-009-9323-2.
Chen, Y. et al. (2012) ‘Eliciting Predictions for Discrete Decision Making’.
Chen, Y. and Pennock, D.M. (2007) ‘A Utility Framework for Bounded-Loss Market Makers’.
Chen, Y. and Pennock, D.M. (2010) ‘Designing Markets for Prediction’, AI Magazine, 31(4), pp. 42–52. Available at: https://doi.org/10.1609/aimag.v31i4.2313.
Chen, Y. and Vaughan, J.W. (2010) ‘A New Understanding of Prediction Markets Via No-Regret Learning’. arXiv. Available at: http://arxiv.org/abs/1003.0034 (Accessed: 1 May 2024).
Choo, L., Kaplan, T.R. and Zultan, R. (2022) ‘Manipulation and (Mis)trust in Prediction Markets’, Management Science, 68(9), pp. 6716–6732. Available at: https://doi.org/10.1287/mnsc.2021.4213.
Christiansen, J.D. (2012) ‘PREDICTION MARKETS: PRACTICAL EXPERIMENTS IN SMALL MARKETS AND BEHAVIOURS OBSERVED’, The Journal of Prediction Markets, 1(1), pp. 17–41. Available at: https://doi.org/10.5750/jpm.v1i1.418.
Ciabaton, J. (2022) Cost Function Based Prediction Markets Aggregate Risk-Averse Experts’ Beliefs as Opinion Pools.
Cipriano, M. and Gruca, T.S. (2015) ‘The power of priors: How confirmation bias impacts market prices’, The Journal of Prediction Markets, 8(3), pp. 34–56. Available at: https://doi.org/10.5750/jpm.v8i3.974.
Coles, P.A., Lakhani, K.R. and Mcafee, A.P. (2007) ‘Prediction Markets at Google’, HBS Case Study [Preprint].
Conitzer (2017a) ‘Market Scoring Rules’.
Conitzer (2017b) ‘Proper Scoring Rules’.
Cover, T. (2005) ‘Gambling and Data Compression’, in Elements of Information Theory.
Cowgill, B. and Zitzewitz, E. (2015) ‘Corporate Prediction Markets: Evidence from Google, Ford, and Firm X’, The Review of Economic Studies, 82(4), pp. 1309–1341. Available at: https://doi.org/10.1093/restud/rdv014.
Czado, C., Gneiting, T. and Held, L. (2009) ‘Predictive Model Assessment for Count Data’, Biometrics, 65(4), pp. 1254–1261. Available at: https://doi.org/10.1111/j.1541-0420.2009.01191.x.
Dai, M., Jia, Y. and Kou, S. (2021) ‘The wisdom of the crowd and prediction markets’, Journal of Econometrics, 222(1), pp. 561–578. Available at: https://doi.org/10.1016/j.jeconom.2020.07.016.
Dana, J. et al. (2019) ‘Are markets more accurate than polls? The surprising informational value of “just asking”’, Judgment and Decision Making, 14(2), pp. 135–147. Available at: https://doi.org/10.1017/S1930297500003375.
De Pablo, N. (2021) ‘Zeitgeist’s Unique AMM Model: The “Rikiddo” Scoring Rule’, 25 June. Available at: https://blog.zeitgeist.pm/introducint-zeitgeists-rikiddo-scoring-rule/.
Deck, C., Lin, S. and Porter, D. (2013) ‘Affecting policy by manipulating prediction markets: Experimental evidence’, Journal of Economic Behavior & Organization, 85, pp. 48–62. Available at: https://doi.org/10.1016/j.jebo.2012.10.017.
Dianat, A. and Siemroth, C. (2021) ‘Improving decisions with market information: an experiment on corporate prediction markets’, Experimental Economics, 24(1), pp. 143–176. Available at: https://doi.org/10.1007/s10683-020-09654-y.
Diebold, F.X., Shin, M. and Zhang, B. (2022) ‘On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates’. arXiv. Available at: http://arxiv.org/abs/2012.11649 (Accessed: 1 May 2024).
Diemer, S. and Poblete, J. (2012) ‘REAL-MONEY VS. PLAY-MONEY FORECASTING ACCURACY IN ONLINE PREDICTION MARKETS – EMPIRICAL INSIGHTS FROM IPREDICT’, The Journal of Prediction Markets, 4(3), pp. 21–58. Available at: https://doi.org/10.5750/jpm.v4i3.479.
Dreber, A. et al. (2015) ‘Using prediction markets to estimate the reproducibility of scientific research’, Proceedings of the National Academy of Sciences, 112(50), pp. 15343–15347. Available at: https://doi.org/10.1073/pnas.1516179112.
Dreber, A. et al. (2019) Prediction markets analyses in the NARPS project. Registered Report: planned analysis in NARP project.
Filippin, A. and Mantovani, M. (2023) ‘Risk aversion and information aggregation in binary‐asset markets’, Quantitative Economics, 14(2), pp. 753–798. Available at: https://doi.org/10.3982/QE1981.
Forestal, R.L. et al. (2020) ‘Prediction Markets: A Systematic Review and Meta-Analysis’.
Forsell, E. et al. (2019) ‘Predicting replication outcomes in the Many Labs 2 study’, Journal of Economic Psychology, 75, p. 102117. Available at: https://doi.org/10.1016/j.joep.2018.10.009.
Fountain, J. and Harrison, G.W. (2011) ‘What do prediction markets predict?’, Applied Economics Letters, 18(3), pp. 267–272. Available at: https://doi.org/10.1080/13504850903559575.
Freeman, R. et al. (2021) ‘Towards a Theory of Confidence in Market-Based Predictions’.
Gjerstad, S. and Hall, M. (2005) ‘Risk Aversion, Beliefs, and Prediction Market Equilibrium’.
Goel, S. et al. (2010) ‘Prediction without markets’, in Proceedings of the 11th ACM conference on Electronic commerce. EC ’10: ACM Conference on Electronic Commerce, Cambridge Massachusetts USA: ACM, pp. 357–366. Available at: https://doi.org/10.1145/1807342.1807400.
Gordon, M. et al. (2020) ‘Are replication rates the same across academic fields? Community forecasts from the DARPA SCORE programme’, Royal Society Open Science, 7(7), p. 200566. Available at: https://doi.org/10.1098/rsos.200566.
Gordon, M. et al. (2021) ‘Predicting replicability—Analysis of survey and prediction market data from large-scale forecasting projects’, PLOS ONE. Edited by M. Vianello, 16(4), p. e0248780. Available at: https://doi.org/10.1371/journal.pone.0248780.
Graefe, A. (2009) Prediction Markets versus Alternative Methods. Empirical Tests of Accuracy and Acceptability. Karlsruhe Institute of Technology.
Grainger, D.A. (2017) The application of prediction markets to project prioritization in the not-for-profit sector. James Cook University Australia.
Hahn, R.W. and Tetlock, P.C. (eds) (2006) Information Markets: A New Way of Making Decisions. Washington, DC (AEI-Brookings Joint Center for Regulatory Studies). Available at: http://www.ssrn.com/abstract=1385778 (Accessed: 1 May 2024).
Hanania, R. (2021) ‘Just Trust the Experts, We’re Told. We Shouldn’t’, NYT, 20 September. Available at: https://www.nytimes.com/2021/09/20/opinion/afghanistan-experts-expertise.html.
Hansen, J., Schmidt, C. and Strobel, M. (2004) ‘Manipulation in political stock markets – preconditions and evidence’, Applied Economics Letters, 11(7), pp. 459–463. Available at: https://doi.org/10.1080/1350485042000191700.
Hanson, R. (2003) ‘Combinatorial Information Market Design’, Information Systems Frontiers, 5(1).
Hanson, R. (2005) ‘The Informed Press Favored the Policy Analysis Market’.
Hanson, R. (2006) ‘Foul Play in Information Markets’, in Information Markets: A New Way of Making Decisions.
Hanson, R. (2012) ‘LOGARITHMIC MARKETS CORING RULES FOR MODULAR COMBINATORIAL INFORMATION AGGREGATION’, The Journal of Prediction Markets, 1(1), pp. 3–15. Available at: https://doi.org/10.5750/jpm.v1i1.417.
Hanson, R. (2014a) ‘Eliciting Objective Probabilities via Lottery Insurance Games’.
Hanson, R. (2014b) ‘Market Scoring Rules’.
Hanson, R. and Oprea, R. (2009) ‘A Manipulator Can Aid Prediction Market Accuracy’, Economica, 76(302), pp. 304–314. Available at: https://doi.org/10.1111/j.1468-0335.2008.00734.x.
Hanson, R., Oprea, R. and Porter, D. (2006) ‘Information aggregation and manipulation in an experimental market’, Journal of Economic Behavior & Organization, 60(4), pp. 449–459. Available at: https://doi.org/10.1016/j.jebo.2004.09.011.
He, Xue-Zhong, T., Nicolas (2017) ‘Prediction Market Prices Under Risk Aversion and Heterogeneous Beliefs’, Journal of Mathematical Economics, 70, pp. 105–114.
Hirshleifer, J. (1983) ‘From weakest-link to best-shot: The voluntary provision of public goods’, Public Choice, 41(3), pp. 371–386. Available at: https://doi.org/10.1007/BF00141070.
Horowitz, M.C. et al. (2021) Keeping Score: A New Approach to Geopolitical Forecasting. University of Pennsylvania.
‘How does the LMSR work’ (no date). Available at: https://www.cultivatelabs.com/crowdsourced-forecasting-guide/how-does-logarithmic-market-scoring-rule-lmsr-work.
Hubáček, O. and Šír, G. (2023) ‘Beating the market with a bad predictive model’, International Journal of Forecasting, 39(2), pp. 691–719. Available at: https://doi.org/10.1016/j.ijforecast.2022.02.001.
Inchauspe, J. et al. (2014) ‘A Behaviorally Informed Survey-Powered Market Agent’, The Journal of Prediction Markets, 8(2), pp. 1–28. Available at: https://doi.org/10.5750/jpm.v8i2.867.
Jian, L. and Sami, R. (2012) ‘Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution’, Management Science, 58(1), pp. 123–140. Available at: https://doi.org/10.1287/mnsc.1110.1404.
Johnstone, D. et al. (2021) ‘Scoring Probability Forecasts by a User’s Bets Against a Market Consensus’, Decision Analysis, p. deca.2020.0424. Available at: https://doi.org/10.1287/deca.2020.0424.
Karimi, M. (2017) Essays in Corporate Prediction Markets. PhD dissertation. University of Waterloo.
Karimi, M. and Dimitrov, S. (2018) ‘On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules’, Decision Analysis, 15(2), pp. 72–89. Available at: https://doi.org/10.1287/deca.2017.0362.
Karimi, M. and Dimitrov, S. (2024) ‘To Subsidize Or Not to Subsidize: A Comparison of Market Scoring Rules and Continuous Double Auctions for Price Discovery’, Information Systems Frontiers, 26(2), pp. 801–823. Available at: https://doi.org/10.1007/s10796-023-10384-8.
Karimi, M. and Zaerpour, N. (2022) ‘Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets’, European Journal of Operational Research, 300(3), pp. 1035–1049. Available at: https://doi.org/10.1016/j.ejor.2021.09.013.
Kelly, D.L. et al. (2012) ‘Evolution of subjective hurricane risk perceptions: A Bayesian approach’, Journal of Economic Behavior & Organization, 81(2), pp. 644–663. Available at: https://doi.org/10.1016/j.jebo.2011.10.004.
Laskey, K.B., Hanson, R. and Twardy, C. (2015) ‘Combinatorial prediction markets for fusing information from distributed experts and models’.
Ledyard, J., Hanson, R. and Ishikida, T. (2009) ‘An experimental test of combinatorial information markets’, Journal of Economic Behavior & Organization, 69(2), pp. 182–189. Available at: https://doi.org/10.1016/j.jebo.2008.04.010.
Lorenz, J. et al. (2011) ‘How social influence can undermine the wisdom of crowd effect’, Proceedings of the National Academy of Sciences, 108(22), pp. 9020–9025. Available at: https://doi.org/10.1073/pnas.1008636108.
Lucas, G.M. and Mormann, F. (2019) ‘Betting on Climate Policy: Using Prediction Markets to Address Global Warming’, UC Davis Law Review, 52, pp. 1429–1486.
Luckner, S. et al. (2012) Prediction markets: fundamentals, designs and applications. 1st. edition. Wiesbaden: Gabler (Gabler research).
Mann, R.P. and Helbing, D. (2017) ‘Optimal incentives for collective intelligence’, Proceedings of the National Academy of Sciences, 114(20), pp. 5077–5082. Available at: https://doi.org/10.1073/pnas.1618722114.
Manski, C.F. (2006) ‘Interpreting the predictions of prediction markets’, Economics Letters, 91(3), pp. 425–429. Available at: https://doi.org/10.1016/j.econlet.2006.01.004.
Marinovic, I., Ottaviani, M. and Sorensen, P. (2013) ‘Forecasters’ Objectives and Strategies’, in Handbook of Economic Forecasting. Elsevier, pp. 690–720. Available at: https://doi.org/10.1016/B978-0-444-62731-5.00012-9.
Martin, B., Chakraborty, M. and Kutty, S. (2021) ‘Timing is money: the impact of arrival order in beta-bernoulli prediction markets’, in Proceedings of the Second ACM International Conference on AI in Finance. ICAIF’21: 2nd ACM International Conference on AI in Finance, Virtual Event: ACM, pp. 1–9. Available at: https://doi.org/10.1145/3490354.3494406.
McGee, Z. and Hall, P. (2023) ‘Using Prediction Markets as a Tool for Classroom and Civic Engagement’. Available at: https://doi.org/10.33774/apsa-2023-1n84b.
Meron, R. (2012) ‘Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation (a presentation on Hanson 2002)’. Seminar in Information Markets, Tel Aviv University, 11 July.
Munafo, M.R. et al. (2015) ‘Using prediction markets to forecast research evaluations’, Royal Society Open Science, 2(10), p. 150287. Available at: https://doi.org/10.1098/rsos.150287.
Nau, R.F. (2001) ‘De Finetti Was Right:  Probability Does Not Exist’, Theory and Decision, 51, pp. 89–124.
Newman, A.L. (2012) ‘Manipulation in Political Prediction Markets’, The Journal of Business, Entrepreneurship & the Law, 3(2).
Nguyen, A. et al. (2021) ‘Dynamic Automated Market Making’.
O’Leary, D.E. (2011) ‘Prediction Markets as a Forecasting Tool’, in K.D. Lawrence and R.K. Klimberg (eds) Advances in Business and Management Forecasting. Emerald Group Publishing Limited, pp. 169–184. Available at: https://doi.org/10.1108/S1477-4070(2011)0000008014.
Othman, A. (2012) Automated Market Making: Theory and Practice. CMU.
Othman, A. et al. (2013) ‘A Practical Liquidity-Sensitive Automated Market Maker’, ACM Transactions on Economics and Computation, 1(3), pp. 1–25. Available at: https://doi.org/10.1145/2509413.2509414.
Othman, A. and Sandholm, T. (2013) ‘Automated Market-Making in the Large: The Gates Hillman prediction market’, Review of Economic Design, 17(2), pp. 95–128. Available at: https://doi.org/10.1007/s10058-013-0144-z.
Ottaviani, M. and Sørensen, P.N. (2005) ‘Aggregation of Information and Beliefs in Prediction Markets’.
Ottaviani, M. and Sørensen, P.N. (2006) ‘Aggregation of Information and Beliefs in Prediction Markets’.
Ottaviani, M. and Sørensen, P.N. (2007) ‘Outcome Manipulation in Corporate Prediction Markets’, Journal of the European Economic Association, 5(2–3), pp. 554–563. Available at: https://doi.org/10.1162/jeea.2007.5.2-3.554.
Ottaviani, M. and Sorensen, P.N. (2009) ‘Aggregation of Information and Beliefs: Asset Pricing Lessons from Prediction Markets’, SSRN Electronic Journal [Preprint]. Available at: https://doi.org/10.2139/ssrn.1447369.
Ottaviani, M. and Sørensen, P.N. (2015) ‘Price Reaction to Information with Heterogeneous Beliefs and Wealth Effects: Underreaction, Momentum, and Reversal’, American Economic Review, 105(1), pp. 01–34. Available at: https://doi.org/10.1257/aer.20120881.
Page, L. and Clemen, R.T. (2013) ‘Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?’, The Economic Journal, 123(568), pp. 491–513. Available at: https://doi.org/10.1111/j.1468-0297.2012.02561.x.
Page, L. and Siemroth, C. (2017) ‘An experimental analysis of information acquisition in prediction markets’, Games and Economic Behavior, 101, pp. 354–378. Available at: https://doi.org/10.1016/j.geb.2015.11.002.
Pathak, D., Rothschild, D. and Dudik, M. (2015) ‘A comparison of forecasting methods: fundamentals, polling, prediction markets, and experts’, The Journal of Prediction Markets, 9(2), pp. 1–31. Available at: https://doi.org/10.5750/jpm.v9i2.1048.
Pennock, D. (2006) ‘Implementing Hanson’s Market Maker’, 30 October. Available at: http://blog.oddhead.com/2006/10/30/implementing-hansons-market-maker/.
Pennock, D.M. (2007) ‘Computational Aspects of Prediction Market’. Available at: https://www.dcs.warwick.ac.uk/~agt2007/SLIDES/DP.pdf.
Qiu, L., Cheng, H.K. and Pu, J. (2017) ‘Hidden Profiles in Corporate Prediction Markets: The Impact of Public Information Precision and Social Interactions’, MIS Quarterly, 41(4), pp. 1249–1273. Available at: https://doi.org/10.25300/MISQ/2017/41.4.11.
Raja, A.A. et al. (2024) ‘A market for trading forecasts: A wagering mechanism’, International Journal of Forecasting, 40(1), pp. 142–159. Available at: https://doi.org/10.1016/j.ijforecast.2023.01.007.
Rajtmajer, S. et al. (2022) ‘A Synthetic Prediction Market for Estimating Confidence in Published Work’, Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), pp. 13218–13220. Available at: https://doi.org/10.1609/aaai.v36i11.21733.
Reade, J.J. and Vaughan Williams, L. (2019) ‘Polls to probabilities: Comparing prediction markets and opinion polls’, International Journal of Forecasting, 35(1), pp. 336–350. Available at: https://doi.org/10.1016/j.ijforecast.2018.04.001.
Restocchi, V. et al. (2017) ‘The impact of transaction costs on state-contingent claims mispricing’, Finance Research Letters, 23, pp. 174–178. Available at: https://doi.org/10.1016/j.frl.2017.02.006.
Restocchi, V. et al. (2018) ‘It takes all sorts: A heterogeneous agent explanation for prediction market mispricing’, European Journal of Operational Research, 270(2), pp. 556–569. Available at: https://doi.org/10.1016/j.ejor.2018.04.011.
Restocchi, V., McGroarty, F. and Gerding, E. (2019a) ‘Statistical properties of volume and calendar effects in prediction markets’, Physica A: Statistical Mechanics and its Applications, 523, pp. 1150–1160. Available at: https://doi.org/10.1016/j.physa.2019.03.096.
Restocchi, V., McGroarty, F. and Gerding, E. (2019b) ‘The stylized facts of prediction markets: Analysis of price changes’, Physica A: Statistical Mechanics and its Applications, 515, pp. 159–170. Available at: https://doi.org/10.1016/j.physa.2018.09.183.
Restocchi, V., McGroarty, F. and Gerding, E. (2019c) ‘The temporal evolution of mispricing in prediction markets’, Finance Research Letters, 29, pp. 303–307. Available at: https://doi.org/10.1016/j.frl.2018.08.003.
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