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.
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
Research Objectives and Themes
This thesis investigates whether the forecasting performance of online prediction markets is influenced by the type of financial incentive—specifically comparing real-money versus play-money schemes. The study aims to determine if real-money contracts consistently achieve higher accuracy and examines how variables such as trading volume and time remaining until contract expiry affect forecast reliability across different market environments.
- Comparative analysis of real-money versus play-money incentive structures.
- Evaluation of forecasting accuracy in online prediction markets.
- Impact of trading volumes and days-to-expiry on market efficiency.
- Influence of contract design (binary vs. indexed) on forecast outcomes.
- Investigation of event-specific dynamics in market information aggregation.
Excerpt from the Book
1.1 Introduction
Sir Francis Galton discovered the phenomenon of crowd wisdom by studying submitted guesses from a public wager in 1906. A monetary price was awarded to the individual who most accurately estimated the weight of an exposed ox. By computing the mean and median of all 800 submitted guesses, he found that the mean showed a spread of mere 1 pound to the ex-post determined slaughtering weight (Surowiecki, 2004). Although no guess equalled the determined weight, crowds collectively predicted at much higher accuracy than the individual.
This simple principle of providing monetary incentives for truthful revelation and aggregation of dispersed knowledge still constitutes the underlying concept of modern prediction markets (they are also referred to as virtual stock markets, information markets, idea futures or forecasting markets). In recent year, political, economic, and academic interest in such market platforms has risen tremendously:
The United States' Defense Advanced Research Projects Agency launched a prediction market (Policy Analysis Market) in 2003 on political and economic events in the middle-east (Polk et al., 2003) to gain knowledge on future events. The project became politically instrumentalized and therefore was abandoned after a short period of time. Since then, private companies have utilized the economic potential and generate billions in trading volume (betfair.com) despite legal bans in many jurisdictions. Table 1 provides an overview of today’s large-scale international prediction markets. Platforms on which virtual money instead of real-money is traded have likewise grown in number and scale in the form of corporate planning tools as well as skill-based online gaming applications.
Academic interest focuses on the market prices of such platforms, which can be interpreted as probability of occurrence for underlying events. Implicit predictions have proven to yield accurate results on all kinds of future events (particularly political elections) under both incentive schemes.
This thesis aims to analyse whether forecasting performance in online prediction markets differs between real- and play-money: Do contracts on real-money predict better on a systematic level (irrelevant of underlying events) and how do equal contracts compare? What other factors influence forecasting accuracy?
Summary of Chapters
1. Introduction and Theoretical Fundamentals: This chapter provides an overview of the concept of crowd wisdom and introduces the foundational theories of prediction markets, including market mechanisms for aggregating information.
2. Literature Overview: This section reviews existing academic research regarding prediction markets, focusing on previous studies that compare real-money and play-money incentives and other factors influencing forecast accuracy.
3. Data: This chapter details the dataset used in this study, including information about the data provider, ipredict, and the specific variables defined for the empirical analysis.
4. Results: This chapter presents the empirical findings, including portfolio comparisons, direct contract comparisons, and regression analysis of factors that influence forecasting performance.
Keywords
Prediction Markets, Forecasting Accuracy, Real-Money Incentives, Play-Money Incentives, Market Efficiency, Information Aggregation, Trading Volume, Days-to-Expiry, Binary Contracts, Indexed Contracts, Crowd Wisdom, iPredict, Behavioral Finance, Economic Events, Market Maker
Frequently Asked Questions
What is the primary objective of this research?
The research aims to analyze whether the forecasting performance of online prediction markets differs significantly between real-money and play-money incentive structures using a unique dataset.
What are the core thematic areas of this study?
The study centers on the impact of financial incentives, the role of market participants, the influence of trading volume, and the impact of the time-to-expiry on forecasting accuracy.
What research methodology is employed?
The author uses empirical statistical analysis on 44,169 trading observations from the platform ipredict to compare performance across different contract types and incentive schemes.
What does the main body of the work cover?
The main body examines the theoretical framework, reviews relevant literature, describes the data collection process, and provides a detailed analysis of results comparing various contract types and influencing factors.
Which criteria define the success of a prediction market in this study?
Success is primarily defined by high forecasting accuracy, measured by the spread between market prices and the actual outcome, and the effective aggregation of dispersed information.
What characterizes the key terminology of this thesis?
The work is defined by concepts such as market efficiency, behavioral incentives, and the specific dynamics of automated market makers in online trading environments.
How does the role of the "market maker" impact the data analysis?
The presence of an automated market maker at ipredict ensures liquidity and provides a structured environment for trades, which the author notes prevents the direct comparison of these results with markets that operate under continuous double auctions.
What conclusion does the author reach regarding the effectiveness of incentives?
The author concludes that while play-money markets perform well in broader portfolios, real-money contracts exhibit significantly higher forecasting accuracy when comparing identical contracts, suggesting that real-money provides a stronger incentive for information revelation.
What is the significance of the "days-to-expiry" variable?
The author analyzes this to see if forecasting accuracy increases as the event approaches; however, the study finds that the expected linear improvement in accuracy is less significant and more inconsistent than previously hypothesized.
- 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
