A study of efficiency in New Zealand election prediction markers

<p>The development of prediction markets has naturally given rise to studies of their efficiency. Most studies of efficiency in prediction markets have focused on the speed with which they incorporate information. A necessary (but not sufficient) condition of efficiency is that arbitrage opportunities must non-existent or transitory in nature so that the systematic generation of abnormal profits is not possible. Using data from New Zealand’s first prediction market, iPredict, I examine the potential for arbitrage in the contracts for the party vote for the 2011 General Election. Relative to the risk-free interest rate, the returns from arbitrage are generally low, consistent with an efficient market. Regression analysis requires that the data not be subject to the possibility of spurious regressions - something that is not addressed in the literature. After confirming the non-stationarity of the price level and the stationarity of the price changes by the unit root test, I use the iPredict data in conjunction with opinion poll data to test whether the polls impact on market pricing behaviour. Using a number of different model types, I find that the opinion poll data has a very limited impact on market prices, suggesting that the information contained in the poll is largely already incorporated into market prices.</p>

Regulation of prediction markets under the Financial Markets Conduct Act 2013

<p>The Financial Markets Conduct Act 2013 (FMC Act) represents the most substantial overhaul of New Zealand’s securities law in recent history. The regulation of derivatives in particular featured high on the agenda as an area in need of reform and, as a result, the FMC Act is much clearer than the Securities Markets Act 1988 with respect to typical derivative agreements. The focus of this paper, however, is on the atypical: the use of derivatives in prediction markets. With a study of New Zealand-based prediction market iPredict, this paper examines whether iPredict will be regulated under the FMC Act and, if so, how it will be regulated. The conclusion reached is that iPredict can operate under the FMC Act only if the Financial Markets Authority (FMA) declares that its contracts are derivatives and grants substantial exemptions from regulatory compliance. This paper then makes recommendations for a more coherent approach to the regulation of prediction markets under the FMC Act.</p>

Regulation of prediction markets under the Financial Markets Conduct Act 2013

<p>The Financial Markets Conduct Act 2013 (FMC Act) represents the most substantial overhaul of New Zealand’s securities law in recent history. The regulation of derivatives in particular featured high on the agenda as an area in need of reform and, as a result, the FMC Act is much clearer than the Securities Markets Act 1988 with respect to typical derivative agreements. The focus of this paper, however, is on the atypical: the use of derivatives in prediction markets. With a study of New Zealand-based prediction market iPredict, this paper examines whether iPredict will be regulated under the FMC Act and, if so, how it will be regulated. The conclusion reached is that iPredict can operate under the FMC Act only if the Financial Markets Authority (FMA) declares that its contracts are derivatives and grants substantial exemptions from regulatory compliance. This paper then makes recommendations for a more coherent approach to the regulation of prediction markets under the FMC Act.</p>

A study of efficiency in New Zealand election prediction markers

<p>The development of prediction markets has naturally given rise to studies of their efficiency. Most studies of efficiency in prediction markets have focused on the speed with which they incorporate information. A necessary (but not sufficient) condition of efficiency is that arbitrage opportunities must non-existent or transitory in nature so that the systematic generation of abnormal profits is not possible. Using data from New Zealand’s first prediction market, iPredict, I examine the potential for arbitrage in the contracts for the party vote for the 2011 General Election. Relative to the risk-free interest rate, the returns from arbitrage are generally low, consistent with an efficient market. Regression analysis requires that the data not be subject to the possibility of spurious regressions - something that is not addressed in the literature. After confirming the non-stationarity of the price level and the stationarity of the price changes by the unit root test, I use the iPredict data in conjunction with opinion poll data to test whether the polls impact on market pricing behaviour. Using a number of different model types, I find that the opinion poll data has a very limited impact on market prices, suggesting that the information contained in the poll is largely already incorporated into market prices.</p>

Crowd-sourced Manipulation and Fraud Detection

Prediction markets are a common tool of companies for idea management and evaluation during the innovation process, which enables them to include expectations and opinions of stakeholders across organizational boundaries. However, prediction markets are also known for their susceptibility to manipulation in theory and practice. The irregular and multifaceted occurrence of these phenomena, with sometimes very creative strategies, makes it difficult to detect manipulation and fraud based on algorithms. To ensure robust and reliable forecasts, which are of utmost importance for a focused and successful digital innovation process, there is a need for a monitoring approach capable of dealing with these specific problems. In an Action Design Research project, we address this problem by developing a crowd-sourced manipulation and fraud detection tool. The artifact enables the crowd to successfully decompose the large set of trading data and successfully find even creative strategies without guidance. The artifact is implemented and evaluated in the field in the prediction market [blinded for review]. We conclude, that a crowd-sourced approach can be suggested to monitor ambiguous and rare events with a varying character in our context and presumably other contexts as well.

Using prediction markets to predict the outcomes in the Defense Advanced Research Projects Agency's next-generation social science programme

There is evidence that prediction markets are useful tools to aggregate information on researchers' beliefs about scientific results including the outcome of replications. In this study, we use prediction markets to forecast the results of novel experimental designs that test established theories. We set up prediction markets for hypotheses tested in the Defense Advanced Research Projects Agency's (DARPA) Next Generation Social Science (NGS2) programme. Researchers were invited to bet on whether 22 hypotheses would be supported or not. We define support as a test result in the same direction as hypothesized, with a Bayes factor of at least 10 (i.e. a likelihood of the observed data being consistent with the tested hypothesis that is at least 10 times greater compared with the null hypothesis). In addition to betting on this binary outcome, we asked participants to bet on the expected effect size (in Cohen's d ) for each hypothesis. Our goal was to recruit at least 50 participants that signed up to participate in these markets. While this was the case, only 39 participants ended up actually trading. Participants also completed a survey on both the binary result and the effect size. We find that neither prediction markets nor surveys performed well in predicting outcomes for NGS2.

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.