Prediction markets are online trading platforms where contracts on future events are traded with payoffs being exclusively linked to event occurrence. Scientific research has shown that market prices of such contracts imply high forecasting accuracy through effective information aggregation of dispersed knowledge. This phenomenon is related to incentives for truthful aggregation in the form of real-money or play-money rewards. The question whether real- or play-money incentives enhance higher relative forecast accuracy has been addressed by previous works with diverse findings. The current state of empirical research in his field is subject to two inherent deficiencies. First, inter-market studies suffer from market disparities and differences in the definition of underlying events. Comparisons between two different platforms (one for play-money contracts, one for real-money contracts) are potentially biased by different trading behaviour. Second, the majority of studies are based upon identical datasets of market platforms (IOWA stock exchange, Tradesports/Intrade, NewsFutures).
This thesis contributes new insights by analysing 44,169 trading observations on ipredict, where real-money and play-money contracts are traded on a variety of events. Forecasting accuracy is analysed on overall trading activity as well as comparison of equal contracts under different monetary incentive schemes. Statistical models are built to analyse the influence of order volumes and days to expiry under both incentive schemes. Ignoring different events in underlying trading activity, play-money contracts imply statistically insignificant excess accuracy. In direct comparison of equal events, real-money contracts, however, real-money contracts predict at significantly higher accuracy. This thesis finds a relationship between order volumes and forecasting accuracy whereas the influence of days to expiry and aggregated volumes showed lower R² than was expected by formed hypotheses.
At a glance
- Grade: 1.0 (1.0 is the best grade)
- Original analysis of 44,169 iPredict trades across 18 selected contracts
- Direct comparison examines four indexed currency-exchange contracts
- 30 sources in the bibliography
- 10 figures and 16 tables
Table of Contents
1. Introduction and Theoretical Fundamentals
1.1 Introduction
1.2 Definition of Prediction Markets
1.3 Theoretical Framework
2. Literature Overview
2.1 Real-money vs. Play-money
2.2 Other Factors With Influence on Forecasting Accuracy
2.3 Closed Prediction Markets
3. Data
3.1 Data Provider
3.2 Definition of Variables
4. Results
4.1 Overall Data
4.2 Real-Money vs. Play-Money: Portfolio Comparison
4.3 Real-Money vs. Play-Money: Direct Contract Comparison
4.4 Real-Money /Play-Money: Influencing Factors
4.5 Conclusion
Objectives and Topics
This thesis investigates whether real-money incentives produce more accurate prediction-market forecasts than play-money incentives, both across portfolios and in comparisons of contracts with similar underlying events. It also examines how trading volumes and time to expiry relate to forecasting accuracy.
- Information aggregation and prediction-market theory
- Real-money and play-money incentive structures
- Open and closed market participation
- Forecast errors in iPredict trading data
- Currency-exchange contracts and direct comparisons
- Trading volumes and days to expiry
Excerpt from the Book
1.2 Definition of Prediction Markets
Prediction Markets can be defined as markets for future events. Such platforms make it possible to obtain, aggregate and process information of dispersed knowledge. Two different contract categories require differentiation: binary (reveal probabilities of occurrence) and indexed (reveal mean values of the underlying index). Both payoff types are present in the analysed dataset of this study.
Binary contracts are stocks tied to events that either occur or do not occur at a specific date or time interval. Contracts pay off $1 (or $10, $100, respectively) in case an event happens, zero otherwise.A hypothetical example for a contract is whether or not the United Nations will impose additional sanctions against Iran before 31.12.2010, paying off $1 if the event occurs according to predefined sources within this interval; zero if it does not. A market price of $0.67 suggests that the last trading occurrence between traders implies a 67% probability of occurrence. Market prices are sometimes misinterpreted, as market prices above 0.50 (e.g. 0.67) are falsely construed to forecast that the event will definitely occur. The sound interpretation, however, is that for 100 repetitions of the event, the event occurs 67 times. Section 1.2 explains the mechanics behind the interpretation of market prices as probabilities.
Indexed contracts pay out the corresponding value of an underlying indicator (e.g. stock prices, interest rates, exchange rates, GDP, industry-specific data, etc.) at a specific date or the mean over a specific range, respectively. A contract paying off $1 for each rounded thousand-digit of the Dow Jones Industrial Index at 31.12.2010 is an example for an indexedcontract/event. Assuming a value of 12,230 at expiry date this contract would pay off $12. The ex-ante market value (e.g. $8.71) reveals the market’s mean value of what the market believes that value will be upon expiry.
Chapter Summaries
1. Introduction and Theoretical Fundamentals: Introduces the research questions, explains binary and indexed prediction contracts, and develops a theoretical account of how markets aggregate beliefs.
2. Literature Overview: Reviews research on monetary incentives, forecasting accuracy and closed prediction markets, identifying questions for further empirical investigation.
3. Data: Describes the iPredict markets and the selected observations, then defines forecast error, days to expiry and trading-volume measures.
4. Results: Presents descriptive statistics, portfolio and direct-contract comparisons, and regression analyses of factors associated with forecast accuracy. The chapter concludes by discussing the findings and directions for further research.
Keywords
prediction markets, forecasting accuracy, real-money incentives, play-money incentives, information aggregation, iPredict, forecast error, trading volume, days to expiry, currency exchange rates, market makers, regression analysis, closed markets
Frequently Asked Questions
What is the thesis about?
It examines forecasting accuracy in online prediction markets under real-money and play-money incentives.
What are its central themes?
The main themes include information aggregation, incentive design, forecast errors, trading volumes and the time remaining before contracts expire.
What is the primary research question?
The thesis asks whether real-money contracts forecast more accurately than play-money contracts across portfolios and in direct contract comparisons, and what other factors influence accuracy.
What research method does the author use?
The author analyses trading observations quantitatively using descriptive statistics, comparisons of forecast errors over time intervals, direct contract comparisons and regression models.
What does the main analysis cover?
It examines trading activity across selected contracts, compares portfolios under the two incentive schemes and investigates currency-exchange contracts and potential influences on forecast errors.
Which keywords characterise the work?
Key terms include prediction markets, real-money incentives, play-money incentives, forecast error, information aggregation, trading volume and days to expiry.
Where do the trading observations come from?
They come from iPredict’s public real-money market and a closed play-money market for finance students.
How are forecasting errors examined over time?
The analysis calculates forecast errors for trading observations and compares average errors over ten-day intervals before contract expiry.
What role does the market maker play?
The automated market maker provides buy and sell offers to support liquidity and operates under both incentive schemes.
- Quote paper
- Bsc. Sebastian Diemer (Author), 2010, Real-Money vs. Play-Money Forecasting Accuracy in Online Prediction Markets, Munich, GRIN Verlag, https://www.grin.com/document/212468
