Prediction Market Data Research Articles

How the American Public Perceived Electoral Competition in the States during the Pre-poll Era: A Prediction Market Data Analysis of the 1896 Presidential Election

AbstractThis study uses prediction market data from the nation’s historical election betting markets to measure electoral competition in the American states during the era before the advent of scientific polling. Betting odds data capture ex ante expectations of electoral closeness in the aggregate, and as such improve upon existing measures of competition based on election returns data. Situated in an analysis of the 1896 presidential election and its associated realignment, I argue that the market odds data show that people were able to anticipate the realignment and that expectations on the outcome in the states influenced voter turnout. Findings show that a month ahead of the election betting markets accurately forecast a McKinley victory in most states. This study further demonstrates that the market predictions identify those states where electoral competition would increase or decline that year and the consequences of these expected partisanship shifts on turnout. In places where the anticipation was for a close race voter expectations account for a turnout increase of as much as 6%. Participation dropped by 1%–6% in states perceived as becoming electorally uncompetitive. The results support the conversion and dealignment theories from the realignment literature.

Predicting replicability-Analysis of survey and prediction market data from large-scale forecasting projects.

The reproducibility of published research has become an important topic in science policy. A number of large-scale replication projects have been conducted to gauge the overall reproducibility in specific academic fields. Here, we present an analysis of data from four studies which sought to forecast the outcomes of replication projects in the social and behavioural sciences, using human experts who participated in prediction markets and answered surveys. Because the number of findings replicated and predicted in each individual study was small, pooling the data offers an opportunity to evaluate hypotheses regarding the performance of prediction markets and surveys at a higher power. In total, peer beliefs were elicited for the replication outcomes of 103 published findings. We find there is information within the scientific community about the replicability of scientific findings, and that both surveys and prediction markets can be used to elicit and aggregate this information. Our results show prediction markets can determine the outcomes of direct replications with 73% accuracy (n = 103). Both the prediction market prices, and the average survey responses are correlated with outcomes (0.581 and 0.564 respectively, both p < .001). We also found a significant relationship between p-values of the original findings and replication outcomes. The dataset is made available through the R package “pooledmaRket” and can be used to further study community beliefs towards replications outcomes as elicited in the surveys and prediction markets.

Recession Prediction Markets and Macroeconomic Risk in Asset Prices

This paper analyzes recession prediction markets from Intrade and PredictIt where individuals bet on the binary outcome of a recession occurring by a certain date. Using a time series of such historical recession prediction market data to measure macroeconomic risk in a variety of asset classes, we find a 1% increase in ex-ante recession prediction market probability is associated with a -0.29% decline in U.S. equity markets and a -0.77 basis point decrease in the 10-Year U.S. Treasury yield. Recession prediction markets also explain a significant amount of the variation in credit spreads, the U.S. Treasury yield curve, and U.S. dollar foreign exchange rates but cannot explain the value and momentum anomalies in equity markets as well as the U.S. treasury term premium.

Analyzing prediction market trading behaviour to select Delphi-experts

PurposeThe selection of experts for Delphi studies is crucial for the quality of the forecast results and the information taken into account. In the past, this has usually been done by selecting participants according to their reputation, although this approach is questionable in terms of reaching the most knowledgeable participants having new, relevant and valid information. In this context, this paper aims to propose to operate a prediction market alongside Delphi studies and select participants based on their trading behaviour in the market for the Delphi study.Design/methodology/approachBased on more than three years of historical prediction market trading data, the authors verify attributes that indicate insightful trades, as previously discussed in the finance literature, by using regression and classification trees.FindingsThe paper contributes attributes of trading behaviour that are theoretically derived from literature and potentially related to informed traders. These are tested and evaluated on historical prediction market data. Especially, the trading volume, the spread at the moment of trading and the market maker attribute seem to predict informed traders the best.Originality/valueAlgorithms based on identified attributes can be used to objectify the selection of experts for Delphi studies with potential gains in terms of the amount of information considered.

Using prediction market data for measuring the expected closeness in electoral research

This article analyzes the effect of the expected closeness on turnout for 56 direct-democratic votes held in Switzerland between 2012 and 2015. It is the first study to measure the expected closeness by using data obtained from prediction markets. It clarifies empirically the relation between the expected closeness and the levels of turnout in direct democratic votes showing that the expected closeness of the result exerts a positive effect on participation levels.

Measuring Corporate Political Sensitivity

The sensitivity of U.S. firms to domestic political uncertainty is an under-conceptualized yet critical component of nonmarket strategy. Using financial and political prediction market data, we dev...

A Behaviorally Informed Survey-Powered Market Agent

We introduce a new method for converting individual probability estimates (obtained through surveys) into market orders for use in a Continuous Double Auction prediction market. Our Survey-Powered Market Agent (SPMA) algorithm is based on actual forecaster behavior, and offers notable advantages over existing market agent algorithms such as Zero Intelligence Plus (ZIP) agents: SPMAs only require probability estimates (and not bid direction nor quantity), are more behaviorally realistic, and work well when probabilities change over time. We validate SPMA using prediction market data and probability estimates elicited through surveys from a large set of forecasters on 88 individual forecasting problems over the course of a year. SPMA outperforms simple averages of the same probability forecasts and is competitive with sophisticated opinion poll aggregation methods and prediction markets. We use a rich set of market and poll data to empirically test the assumptions behind SPMA’s operation. In addition to aggregation efficiency, SPMA provides a framework for studying how forecasters convert probability estimates into trading orders, and offers a foundation for building hybrid markets which mix market traders and individuals producing independent probability estimates.

Using Prediction Market Data to Illustrate Undergraduate Probability

Prediction markets provide a rare setting, where results of mathematical probability theory can be related to events of real-world interest and where theory can be compared to data. The paper discusses two simple mathematical results—the halftime price principle and the serious candidates principle—and corresponding data from baseball and the 2012 Republican Presidential nomination race.

Polling The Health Care Cases

Historically, our understanding of how the public perceives the Supreme Court and its decisions has been somewhat generalized. Polling firms conduct surveys on a semi-regular basis about a few common questions: do people view the Supreme Court favorably, do they think the Court is too conservative or too liberal, what do they think about certain hot-button issues like abortion or affirmative action, and do they support or oppose a recent controversial decision (such as Bush v. Gore or Citizens United v. FEC). While these surveys allow researchers to discern broad trends in how the Supreme Court is perceived, the data are somewhat unhelpful to break down public perception of a particular case with specificity.However, the attention surrounding NFIB v. Sebelius, commonly known as The Health Care Cases, is quite unprecedented in this regard. The data analyzing popular opinion about this case is voluminous, and presents a treasure trove for a quantitative analysis. Our article aggregates all of the polls, surveys, and prediction market data about the case to compile a detailed empirical analysis of how key demographics of the American populace viewed the challenge to Affordable Care Act from early 2010, when the challenges began until June 2012, when the case was decided, and beyond.

How Prediction Markets Can Save Event Studies

This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions from the economic effects of party control of government to the importance of complex rules in congressional committees. However, the results of event studies are notoriously sensitive to both choices made by researchers and external events. Specifically, event studies will generally produce different results depending on three interrelated things: which event window is chosen, the prior probability assigned to an event at the beginning of the event window, and the presence or absence of other events during the event window. In this paper we show how each of these may bias the results of event studies, and how prediction markets can mitigate these biases.

How Prediction Markets Can Save Event Studies

This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions from the economic effects of party control of government to the importance of complex rules in congressional committees. However, the results of event studies are notoriously sensitive to both choices made by researchers and external events. Specifically, event studies will generally produce different results depending on three interrelated things: which event window is chosen, the prior probability assigned to an event at the beginning of the event window, and the presence or absence of other events during the event window. In this paper we show how each of these may bias the results of event studies, and how prediction markets can mitigate these biases.