Identifying Experts in Virtual Forecasting Communities

Macroeconomic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Over the last couple of years prediction markets as a community forecasting method have gained interest in the scientific world and in industry. An arising question is how to detect valuable user input and identify experts in such online communities. Detecting such input would possibly enable us to improve the information aggregation mechanism and the forecast performance of such systems. We design a prediction market for economic derivatives that aggregates macro-economic information. Using market-based measures we find that user input can be evaluated ad-hoc. Further analysis shows that aggregated measures outperform established methods -such as reputation- in identifying forecasting experts. Moreover, using data from a two year field-experiment we find that expertise is stable for longer time horizons

REVIEW OF PREDICTION MARKET RESEARCH: GUIDELINES FOR INFORMATION SYSTEMS RESEARCH

This paper presents an analysis of prediction market (PM) research relevant to information systems. Prediction markets are (online) markets are usually not traded on existing exchanges but on future events. As an emerging research area, prediction markets have received considerable attention from several disciplines, including economics, politics, marketing, computer science, electronic commerce and etc. In information systems research, however, they have been largely ignored. This study reviewed 93 academic articles concerning prediction markets. The analysis reveals that an increasing volume of PM research has been conducted, and that research themes of these studies can be categorized into three groups, namely general introduction, theoretical work, and PM applications. Building upon this work, we argue for the importance of future prediction market research and suggest potential research targets for IS researchers

Participation, Feedback & Incentives in a Competitive Forecasting Community

Macro-economic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Over the last couple of years prediction markets as a community forecasting method have gained interest. An arising question is how to design incentive schemes and feedback mechanisms to motivate participants to contribute to such an information exchange. We design a prediction market for economic derivatives that aggregates macro-economic information. We show that the level of participation is mainly driven by a weekly newsletter which acts as a reminder. In public goods projects participation feedback has been found to increase participants\u27 contributions. We find that the induced competitiveness of market environments seem to superpose classical feedback mechanisms. We show that forecast errors fall over the prediction horizon. The market generated forecasts compare well to the Bloomberg-survey forecasts, the industry standard. Additionally we can predict community forecast error by using an implicit market measure

The role of monetary incentives in prediction markets: a time series approach

Prediction markets serve as popular devices to aggregate beliefs and to assess market estimated probabilities. By looking at the interaction between real- and play-money prediction markets, this paper shows that traded volume has a significant positive effect on the probability of real- and play-money market cointegration. This indicates that the information aggregation process, eliminating individual traders'' biases, operates even when not inducing truthful belief revelation with monetary incentives. The study is based on data from four markets covering the 2008 presidential election in the United States of Americafinancial economics and financial management ;

Comparing Face-to-Face Meetings, Nominal Groups, Delphi and Prediction Markets on an Estimation Task

We conducted laboratory experiments to analyze the accuracy of three structured approaches (nominal groups, Delphi, and prediction markets) compared to traditional face-to-face meetings (FTF). We recruited 227 participants (11 groups per method) that had to solve a quantitative judgment task that did not involve distributed knowledge. This task consisted of ten factual questions, which required percentage estimates. While, overall, we did not find statistically significant differences in accuracy between the four methods, the results differed somewhat at the individual question level. Delphi was as accurate as FTF for eight questions and outperformed FTF for two questions. By comparison, prediction markets were unable to outperform FTF for any of the ten questions but were inferior for three questions. The relative performance of nominal groups and FTF was mixed and differences were small. We also compared the results from the three structured approaches to prior individual estimates and staticized groups. The three structured approaches were more accurate than participants’ prior individual estimates. Delphi was also more accurate than staticized groups. Nominal groups and prediction markets provided little additional value compared to a simple average of forecast. In addition, we examined participants’ perceptions of the group and the group process. Participants rated personal communication more favorable than computer-mediated interaction. Group interaction in FTF and nominal groups was perceived as highly cooperative and effective. Prediction markets were rated least favorable. Prediction market participants were least satisfied with the group process and perceived their method as most difficult

Trgi kot orodje za napovedovanje

Trgi kot orodje za napovedovanje: primer slovenske volilne borz

Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings