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>

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.

Comparing trading behaviour and profit composition in prediction markets

Prediction markets have established itself as forecasting technique, especially within the IT industry. While the majority of existing studies focuses either on the output of such markets or its design settings, the traders who actually produce the forecasts got only little attention yet. Within this work, we develop a classification scheme for traders of a prediction market that is grounded on both, financial and prediction market literature. Over a period of three years, 127 prediction markets have been observed and its 4.329 traders are separated into seven subgroups (beginners, noise traders, average traders, experts, donkey traders, market makers and superior traders), based on their knowledge, experience and selectivity. We find empirical evidence for the existence of these subgroups and thus for the heterogeneity among the traders. For each of these subgroups, we analyze the trading behaviour and the profit composition.

On the Observational Implications of Knightian Uncertainty

We develop a model of a prediction market with ambiguity and derive testable implications of the presence of Knightian uncertainty. Our model can also explain two commonly observed empirical regularities in betting markets: the tendency for longshots to win less often than odds would indicate and the tendency for favorites to win more often. Using historical data from Intrade, we further present empirical evidence that is consistent with the predicted presence of Knightian uncertainty. Our evidence also suggests that, even with information acquisition, the Knightian uncertainty of the world may be not “learnable” to the traders in prediction markets.

A Prediction Market-Based Gamified Approach to Enhance Knowledge Sharing in Organizations

Background. Knowledge sharing among the members of an organization is crucial for enhancing the organization’s performance. However, knowing how to motivate and direct members to effectively and efficiently share their relevant private knowledge concerning the organization’s activities is not entirely a straightforward matter. Aim. This study aims to propose a gamified approach not only for motivating truthful sharing and collective evaluation of knowledge among the members of an organization but also for properly directing those actions so as to maximize the usefulness of the shared knowledge. A case study is also conducted to understand how the proposed approach works in a live business scenario. Method. A prediction market game on a binary event on whether the specified activity will be completed successfully is devised. The game utilizes an original comment aggregation and evaluation system through which relevant knowledge can be shared verbally and evaluated collectively by the players themselves. Players’ behavior is driven toward a desirable direction with the associated incentive framework realized by three game scores. Results. The proposed gamified approach was implemented as a web application and verified with a laboratory experiment. The game was also played by four participants who deliberated on an actual sales proposal in a real company. It was observed that the various valuable knowledge elements that were successfully collected from the participants could be utilized for refining the sales proposal. Conclusions. The game induced motivation through gamification, and some of the designed game scores worked in directing the players’ behavior as desired. The players learned from others’ comments, which brought about a snowball effect and enriched collective knowledge. Future research directions include how to transform this knowledge into an easy-to-comprehend representation.