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We investigate which of the two most common prediction market mechanisms – market scoring rules and continuous double auctions – leads to better price discovery. The relative contribution of a particular prediction market to price discovery also depends on the total number of trades observed in that market. We use real-world prediction market price data to estimate price discovery shares of each market and their relationship to the number of trades. We find that when the number of trades is low, prediction markets that use market scoring rules have a higher speed of incorporating information into prices. When the number of trade is high, however, the continuous double auctions have a higher price discovery share. As market scoring rules require a variable cost – a subsidy – to operate, and continuous double auctions are costless; our results provide important implications for the design of prediction markets. By combining the methods for measuring price discovery and information aggregation, we introduce a new data-driven approach that can be used by researchers and practitioners to gain further insight into the exact values of the number of trades favoring the use of market scoring rules instead of continuous double auctions or vice versa.

Prediction Markets (PMs) are markets in which agents trade event contingent assets. Enterprises use PMs to forecast revenues and project deadlines. This paper presents an Agent-based model, called Logarithmic Market Scoring Rule-Automated Market Maker (LMSR-ASM), to evaluate Prediction Markets. Our model is capable of testing different types of Automated Market Makers (AMMs), which are mathematical functions or computational mechanisms needed to provide liquidity in Prediction Markets. The model offers insights into how to set parameters in a PM and how profits react to contrasting settings and AMMs. In addition, we simulate different probability processes, distinct AMMs, and agent behaviors. This paper also utilizes the LMSR-ASM to evaluate the impact of choosing initial prices in profits and revenue opportunities regarding AMM computational implementation. We show that we can use the LMSR-ASM to find optimal parameters for maximizing profits in PMs and how different AMMs affect market results under a variety of settings.

In response to the persistent failures of traditional election polling, this study introduces the Decentralized Prediction Market Voter Framework (DPMVF), a novel tool to empirically test and quantify the predictive capabilities of Decentralized Prediction Markets (DPMs). We apply the DPMVF to Polymarket, analysing over 11 million on-chain transactions from 1 September to 5 November 2024 against aggregated polling in the 2024 U.S. Presidential Election across seven key swing states. By employing Cross-Correlation Function (CCF) for linear analysis and Dynamic Time Warping (DTW) for non-linear pattern similarity, the framework provides a robust, multi-faceted measure of the lead-lag relationship between market sentiment and public opinion. Results reveal a striking divergence in predictive clarity across different electoral contexts. In highly contested states like Arizona, Nevada, and Pennsylvania, the DPMVF identified statistically significant early signals. Using a non-parametric Permutation Test to validate the observed alignments, we found that Polymarket’s price trends preceded polling shifts by up to 14 days, a finding confirmed as non-spurious with a high confidence (p < 0.01) and with an exceptionally high correlation (up to 0.988) and shape similarity. At the same time, in states with low polling volatility like North Carolina, the framework correctly diagnosed a weak signal, identifying a “low-signal environment” where the market had no significant polling trend to predict. This study’s primary contribution is a validated, descriptive tool for contextualizing DPM signals. The DPMVF moves beyond a simple “pass/fail” verdict on prediction markets, offering a systematic approach to differentiate between genuine early signals and market noise. It provides a foundational tool for researchers, journalists, and campaigns to understand not only if DPMs are predictive but when and why, thereby offering a more nuanced and reliable path forward in the future of election analysis.

This paper examines the implications of the market selection hypothesis on the accuracy of the probabilities implied by equilibrium prices and on the “learning” mechanism of markets. I use the standard machinery of dynamic general equilibrium models to generate a rich class of probabilities, price probabilities, and discuss their properties. This class includes the Bayes’ rule and known non-Bayesian rules. If the prior support is well-specified, I prove that all members of this class perform as well as Bayes’ rule in terms of likelihood. If the prior support is misspecified in that the Bayesian prior does not converge, I demonstrate that some members of price probabilities significantly outperform Bayes’. Because these members are never worse and sometimes better than Bayes, my result challenges the prevailing opinion that Bayes’ rule is the only rational way to learn.

Prediction markets provide a promising approach for future event prediction. Most existing prediction market approaches are based on auction mechanisms. Despite their theoretical appeal and success in various application settings, these mechanisms suffer from several major drawbacks. First, opinions from experts and amateurs are treated equally. Second, continuous attention from participants is assumed. Third, such mechanisms are subject to various forms of market manipulation. To alleviate these limitations, we propose to employ the classic fixed odds betting as an alternative prediction market mechanism. We build a structural model based on a belief–decision framework as the event probability estimator. This belief–decision framework models bettors’ beliefs with mixed beta distributions and bettors’ decisions with prospect theory. A maximum likelihood approach is applied to estimate the model parameters. We conducted experiments on three real-world betting datasets to evaluate our proposed approach. Experimental results show that fixed odds betting based prediction outperforms the reduced form models based on odds and betting results, and achieves a comparable performance with auction-based prediction markets. The results suggest the possibility of employing fixed odds betting as a prediction market in a variety of application contexts where the assumptions made by auction-based approaches do not hold.

We conduct a lab experiment to investigate an important corporate prediction market setting: A manager needs information about the state of a project, which workers have, in order to make a state-dependent decision. Workers can potentially reveal this information by trading in a corporate prediction market. We test two different market designs to determine which provides more information to the manager and leads to better decisions. We also investigate the effect of top-down advice from the market designer to participants on how the prediction market is intended to function. Our results show that the theoretically superior market design performs worse in the lab—in terms of manager decisions—without top-down advice. With advice, manager decisions improve and both market designs perform similarly well, although the theoretically superior market design features less mis-pricing. We provide a behavioral explanation for the failure of the theoretical predictions and discuss implications for corporate prediction markets in the field.

Stock market movement prediction remains challenging due to random walk characteristics. Yet through a potent blend of input parameters, a prediction model can learn sequential features more intelligently. In this paper, a multi-channel news-oriented prediction system is developed to capture intricate moving patterns of the stock market index. Specifically, the system adopts the temporal causal convolution to process historical index values due to its capability in learning long-term dependencies. Concurrently, it employs the Transformer Encoder for qualitative information extraction from financial news headlines and corresponding preview texts. A notable configuration to our multi-channel system is an integration of cross-residual learning between different channels, thereby allowing an earlier and closer information fusion. The proposed architecture is validated to be more efficient in trend forecasting compared to independent learning, by which channels are trained separately. Furthermore, we also demonstrate the effectiveness of involving news content previews, improving the prediction accuracy by as much as 3.39%.

Prediction markets are run to extract information from its participants through financial incentive. The market scoring rule mechanism represents a way of organizing markets in order to foster agents to make sincere predictions. Market scoring rules are usually presented in a context of asset trading, but they also boil down to a sequential probability report process analyzed here. If the future state space is binary (i.e., composed of only two possible states) and only two agents participate alternatively in the market, it is proven that for strictly proper market scoring rules, the report sequences of each agent converge toward limit probability reports which are closer to each other than the subjective probabilities of the agents.

This paper summarizes an empirical comparison of the accuracy of forecasts included in analysis reports developed by professional intelligence analysts to comparable forecasts in a prediction market that has broad participation from across an intelligence community. To compare forecast accuracy, 99 event forecasts were extracted from qualitative descriptions found in 41 analysis reports and posted on the prediction market. Quantitative probabilities were imputed from the qualitative forecasts by asking seasoned professional analysts, who did not participate in the prediction market, to read the reports and to infer a quantitative probability based on what was written. These readers were also asked to provide their personal probabilities before and after reading the reports. There were two statistically significant results of particular interest. First, the primary result is that the prediction market forecasts were more accurate than the analysis reports. On average prediction market probabilities were 0.114 closer to ground truth than the analysis report probabilities. Second, in cases where analysts (readers) updated their personal probabilities in a direction opposite to what the reports implied, analysts tended to update their probabilities in the correct direction. This occurred even though, on average, reading the reports did not make readers more accurate.

Financial market-based analysis of the expected effects of policy changes has traditionally been exclusively retrospective. In this paper, we demonstrate by example how prediction markets make it possible to use markets to prospectively estimate policy effects. We exploit data from a market trading in contracts tied to the ouster of Saddam Hussein as leader of Iraq to learn about financial market participants' expectations of the consequences of the 2003 Iraq war. We conducted an ex-ante analysis, which we disseminated before the war, finding that a 10% increase in the probability of war was accompanied by a $1 increase in spot oil prices that futures markets suggested was expected to dissipate quickly. Equity price movements implied that the same shock led to a 1.5% decline in the S&P 500. Further, the existence of widely-traded equity index options allows us to back out the entire distribution of market expectations of the war's near-term effects, finding that these large effects reflected a negatively skewed distribution, with a substantial probability of an extremely adverse outcome. The flow of war-related news through our sample explains a large proportion of daily oil and equity price movements. Subsequent analysis suggests that these relationships continued to hold out of sample. Our analysis also allows us to characterize which industries and countries were most sensitive to war news and when the immediate consequences of the war were better than ex-ante expectations, these sectors recovered, confirming these cross-sectional implications. We highlight the features of this case study that make it particularly amenable to this style of policy analysis and discuss some of the issues in applying this method to other policy contexts.