Real-Money vs. Play-Money Forecasting Accuracy in Online Prediction Markets

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

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.