A Market Approach to Forecasting: Background, Theory and Practice

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A Market Approach to Forecasting:

Background, Theory and Practice

Georgios G. Tziralis

Submitted in partial fulfillment of the

requirements for the degree

of Doctor of Philosophy

in the Sector of Industrial Management and Operational Research

SCHOOL OF MECHANICAL ENGINEERING

NATIONAL TECHNICAL UNIVERSITY OF ATHENS

June2013


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cJune2013


All Rights Reserved


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ABSTRACT

A Market Approach to Forecasting:

Background, Theory and Practice


Advisor: Prof. Ilias Tatsiopoulos

My PhD Thesis focuses on the omnipresent problem of business forecasting. In its

very core, the problem refers to estimating a variables future price, potentially being

correlated to a set of variables, or ideally following a specific pattern. There exists a variety

of statistical approaches, ranging from classical econometric techniques to recent data

mining algorithms, attempting to extract such underlying relationships from past data

and reproduce them to predict future values. However, in practice, these correlations are

highly dynamic and patterns, if any, usually evolve in chaotic ways. Therefore even highly

adjustable machine learning techniques tend to perform poorly, suffering from inherent

systemic impotencies.

Human experts, on the other hand, seem to be better suited to address this request.

A single expert can process a big number of variables and quickly adjust predictions

into varying circumstances. However, bias and shortcomings reduce the value of such

estimates. Consequently, there lies a need and opportunity to develop mechanisms that

can demonstrate a capacity to aggregate information from experts, in a dynamic, cost and

time-effective manner that negates their inherent shortcomings.

Financial markets arise as an emerging paradigm to handle such a request. Well-

established market institutions typically focus on serving either investment, hedging

or speculation purposes, while their fundamental function of information aggregation

remains latent in all but newly, and yet of limited exposure, prediction markets institutions.

I provide a market topology scheme and locate existing market institutions in terms of

usage of these ubiquitous functions. And I am studying prediction markets in this Thesis.


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I start with a survey. I provide an extensive literature review of the field and a detailed

classification scheme of the existing literature, both appearing for the first time in thebibliography.

I then move on to identify inappropriate attributes ensued from market mechanisms

typically used to serve a prediction market institution. I build on the latest advances of

market scoring rules and dynamic pari-mutuel markets to propose an analytical framework

for a coherent price function of a dynamic pari-mutuel market. I construct and validate

a function satisfying this framework, finally contributing what may be perceived as a

coherent hybrid between dynamic pari-mutuel market and market scoring rules.

I also provide an attempt to evaluate the hybrids performance, by shaping a dynamic

market model and simulating various agent behaviors and strategies to study the properties

of convergence, acquisition of recently arrived information and equilibrium, among others.

I then proceed from models and simulations to practice. To examine the behavior of the

proposed market mechanism and its forecasting capacity, I build and present a fully-scaled

web platform, AskMarkets. Taking advantage of it, I run and analyze a number of experi-

ments in a variety of academic, professional and social contexts to empirically validate the

applicability of theoretical findings. I also provide a practical deployment framework for

prediction markets implementation, leveraging on the experience accumulated due to the

extent of this study.

I also address some advanced concepts and the open question of prediction markets

efficiency. I approach it by studying the convolution of market prices and relevant

news streams. I empirically validate the approach using the most varied data set of

real-money prediction markets contracts ever examined. I finally propose a sophisticated

analytical technique for prediction markets to serve for event detection and eliciting marketinefficiencies.

Overall, I believe that this Thesis provides a unique contribution to the field of predic-

tion markets, forecasting, decision sciences and operations research at large, studying the

mechanism at full scale, ranging from an extensive literature review to a coherent market

mechanism and from market modeling to simulations and extended case studies.

The Thesis was completed in 2008, yet its defence took place in June2013.


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I would like to thank Professor Georgios Kosmetatos, who taught me to love research;

my supervisor Professor Ilias Tatsiopoulos, who gave me the chance to work on what Ilove; Athanasios Tolis, who made me stay in the place I love; fellow coworkers Athanasios

Rentizelas and Konstantinos Kirytopoulos, who instructed me to pursue excellence and

results; also, Panos Ipeirotis for his support in Chapter 6 of this Thesis. Beyond my life in

an NTUA lab, I would also like to extend this thank you note to a number of people who

happily bore my peculiar behaviour, throughout the years I spent writing this Thesis.


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Contents

Table of Contents v

List of Figures viii

List of Tables x

1 Introduction 1

1.1 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Background 7

2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Markets, in general . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Fundamentals of Prediction Markets . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4.1 Context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4.2 Research Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4.3 Terminology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4.4 Early works and evolution . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5 Classification Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.5.0.1 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.5.0.2 Theoretical work . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.5.0.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

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2.5.0.4 Law and Policy . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5.1 Classification Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.6 Conclusions and Research Implications . . . . . . . . . . . . . . . . . . . . . 23

2.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3 Theoretical Properties of Prediction Markets 29

3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2 Market Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2.1 Description & Characteristics . . . . . . . . . . . . . . . . . . . . . . . 30

3.2.2 Continuous Double Auction . . . . . . . . . . . . . . . . . . . . . . . 31

3.2.3 Market-maker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2.4 Pari-mutuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Prediction Markets Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.1 Disadvantages for use in prediction markets . . . . . . . . . . . . . . 35

3.3.2 Market Scoring Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.3.3 Dynamic Pari-Mutuel Market. . . . . . . . . . . . . . . . . . . . . . . 39

3.4 Properties for a Coherent Price Function . . . . . . . . . . . . . . . . . . . . 43

3.4.1 Simple case: n= 2 outcomes . . . . . . . . . . . . . . . . . . . . . . . 44

3.4.2 Extension forn = koutcomes. . . . . . . . . . . . . . . . . . . . . . . 47

3.5 A Coherent Price Function I . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50


3.5.2 Extension forn = koutcomes. . . . . . . . . . . . . . . . . . . . . . . 55

3.6 A Coherent Price Function II . . . . . . . . . . . . . . . . . . . . . . . . . . . 61


3.6.2 Extension forkoutcomes . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

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4 Theoretical Evaluation of Prediction Markets 77

4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774.2 Background & Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.2.1 Theoretical work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.2.2 Empirical studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.3 Design of Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.3.1 Model of markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.3.2 Information structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.3.3 Market mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

4.3.4 Agent strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.4 Convergence Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.4.1 Price Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

4.4.2 Convergence Speed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4.4.3 Best Possible Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 91

4.4.4 Convergence to the Best Prediction or Not . . . . . . . . . . . . . . . 93

4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

4.5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

4.5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5 Empirical Analysis of Prediction Markets 103

5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.2 Review of Empirical Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.3 The askmarkets platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.4 Experiment A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.4.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.4.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

5.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

5.5 Experiment B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

5.5.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

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5.5.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5.5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165.6 Experiment C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

5.6.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

5.6.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5.6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5.7 A Deployment Framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

5.7.1 Context. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

5.7.2 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

5.7.3 Stocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.7.4 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

5.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.8.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

6 Advanced Prediction Markets Concepts: Event Detection 129

6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1296.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

6.3 Background & Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

6.3.1 Text mining & stock market prediction . . . . . . . . . . . . . . . . . 132

6.3.2 Volatility analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

6.4 Time Series Analysis of Prediction Markets . . . . . . . . . . . . . . . . . . . 135

6.4.1 Machine Learning Techniques . . . . . . . . . . . . . . . . . . . . . . 135

6.4.2 Garch analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

6.4.2.1 Pre-estimation analysis . . . . . . . . . . . . . . . . . . . . . 137

6.4.2.2 Parameter estimation . . . . . . . . . . . . . . . . . . . . . . 144

6.4.2.3 Post-estimation analysis . . . . . . . . . . . . . . . . . . . . 145

6.5 Volatility Modeling for Detecting Information Flows . . . . . . . . . . . . . 149

6.5.1 Volatility Modeling and Prediction. . . . . . . . . . . . . . . . . . . . 150

6.5.2 Analyzing Variance Movements . . . . . . . . . . . . . . . . . . . . . 150

6.6 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

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6.7 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

6.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1546.8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

6.8.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

7 Conclusions 157

7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

7.2 Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Bibliography 189

A Revision 191

A.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

A.2 Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

A.3 Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

A.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

Index 199

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List of Figures

2.1 Number of articles per term used to describe the concept of PM. . . . . . . 15

2.2 Publication trend. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3 Classification of topics in prediction markets literature. . . . . . . . . . . . . 18

2.4 Classification results of PM literature. . . . . . . . . . . . . . . . . . . . . . . 21

4.1 Breakdown Structure of Prediction Market Modeling.source:Chen et al.[2006b] 84

5.1 Homepage, screenshot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

5.2 Market page, screenshot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

5.3 Market creation page, screenshot . . . . . . . . . . . . . . . . . . . . . . . . . 1105.4 Example closed market page with slow convergence, screenshot . . . . . . 112

5.5 Number of transactions per active user . . . . . . . . . . . . . . . . . . . . . 115

5.6 Market A, stock prices evolution, graph . . . . . . . . . . . . . . . . . . . . . 116

5.7 Market B, final results, screenshot . . . . . . . . . . . . . . . . . . . . . . . . 117

5.8 Example market, stock prices evolution, graph . . . . . . . . . . . . . . . . . 120

6.1 SVM predictions vs actual results, Clinton. . . . . . . . . . . . . . . . . . . . 138

6.2 Autocorrelation of prices series, Clinton,2004Q4. . . . . . . . . . . . . . . . 139

6.3 Autocorrelation of differences series, Clinton,2004Q4. . . . . . . . . . . . . 140

6.4 Autocorrelation of returns series, Clinton, 2004Q4.. . . . . . . . . . . . . . . 141

6.5 Autocorrelation of squared returns series, Clinton,2004Q4. . . . . . . . . . 142

6.6 Innovations, conditional standard deviations and returns series, Clinton,

2004Q4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

6.7 Standardized innovations of returns series, Clinton,2004Q4. . . . . . . . . . 147

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6.8 Autocorrelation of the squared standardized innovations of returns series,

Clinton,2004Q4.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

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List of Tables

1.1 Research approaches and questions . . . . . . . . . . . . . . . . . . . . . . . 5

2.1 Type of Publications & Number of Articles . . . . . . . . . . . . . . . . . . . 14

2.2 Number of Description Articles . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.3 Number of Theoretical Work Articles . . . . . . . . . . . . . . . . . . . . . . 22

2.4 Number of Application Articles. . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.5 Number of Law and Policy Articles . . . . . . . . . . . . . . . . . . . . . . . 23

2.6 Classification of Reviewed Literature, (a) Description . . . . . . . . . . . . . 24

2.7 Classification of Reviewed Literature, (b) Theoretical Work. . . . . . . . . . 24

2.8 Classification of Reviewed Literature, (c) Applications . . . . . . . . . . . . 25

2.9 Classification of Reviewed Literature, (d) Law and Policy . . . . . . . . . . 26

3.1 Properties of Market Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . 36

3.2 Examples of Proper Scoring Rules . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3 Properties of Adequate Market Mechanisms . . . . . . . . . . . . . . . . . . 43

4.1 Review of the market model. . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

5.1 Experiment A, survey & market results . . . . . . . . . . . . . . . . . . . . . 113

5.2 Experiment C, market results . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

6.1 SVMs Input Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

6.2 SVMs Output Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

6.3 SVM errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

6.4 Ljung-Box-Pierce test on returns series, Clinton 2004Q4 . . . . . . . . . . . . 144

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6.5 Ljung-Box-Pierce test on square of returns series, Clinton 2004Q4 . . . . . . 144

6.6 GARCH model estimated parameters, Clinton 2004Q4 . . . . . . . . . . . . 1456.7 Ljung-Box-Pierce test on square of standardized innovations series, Clinton

2004Q4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

6.8 Example of hits in news archive found, typical contract . . . . . . . . . . . . 153

6.9 Metrics of average results for all contracts. . . . . . . . . . . . . . . . . . . . 153

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Chapter 1

Introduction

1.1 Scope

What men have seen they know; but what shall come hereafter, no man before

the event can see. Sophocles Ajax, Chorus, lines 1417-1419, ca 450-440BC[Jebb,

1896].

Since the oracles of Pythia and before, people have always wanted to forecast the future.

Across geographies and ages, the uncertainty that future brings settles the need for its

efficient forecasting. Responding to this life fact, modern age researchers and practitioners

attempted to replace rituals with reason. Yet, this transition remains an ongoing process

and the Thesis on hand aspires to further contribute towards it.

Every decision requires a number of assumptions about the future. Predictions, in other

words, are the flip side of decisions. In this context, the importance of forecasting, namely

any statement about the future, arises as remarkable in decision sciences. Management,decision science and operations research serve as natural fields for the study and the

advancement of forecasting problems.

Forecasting is essentially a task of aggregation and processing. Ideally, one needs to

collect all potentially related information, then filter only the relevant to the target variable

parts and process the remaining data to arrive at conclusions. No matter the fascinating

progress that has been recently made in handling such problems, in fields ranging from

machine learning, to econometrics to social sciences, this expectably remains a cyclopean

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2 CHAPTER1. INTRODUCTION

task.

One may ponder though about the existence of alternative mechanisms, that organicallyperform the above core tasks of information aggregation and processing in dynamic

environments. Human intelligence seems to excel at most parts of these, yet it remains

hindered by endemic problems, such as biases. That said, a number of ideas that could

take advantage of collective intelligence and at the same time cancel out such limitations

remain underexplored, provoking for further research.

The scope of this Thesis is to study in depth such an approach, with the vision to help

making it the default choice for a number of forecasting applications.

1.2 Motivation

The core approaches to the generic problem offorecastingare essentially three [Armstrong,

2001]. The first set refers to causal methods. These focus on discovering correlations

between the target variable and other potentially related ones [Pindyck and Rubinfeld,

1981]. Then, they apply these causal relationships to predict future prices of the target

variable, assuming that such relationships do not change significantly and provided that

the prices of the correlated variables are given [Rodriguez Poo,2003]. Causal methods have

in many cases demonstrated a capacity to capture underlying relationships and provide

sufficient results. However, it is quite common for such relationships to be difficult to

discover, also for them to evolve over time, thus for causal methods to eventually lead to

mediocre results [Fildes,1985,Armstrong, 1978].

The second set of forecasting methods refers to time-seriesbased ones[Makridakis et al.,

1998,Brockwell and Davis, 1996]. Such approaches are established on the fundamental

hypothesis that past data, usually limited to the target variable itself, contain enough

information for the prediction of future prices. In other words, time-series approaches

attempt to discover underlying patterns across past data and extrapolate them in the

future, assuming that such patterns will remain valid [Lutkepohl, 1993]. As expected,

time-series approaches perform generally well in relatively static problems. However, they

typically prove to be incapable of sufficiently capturing underlying patterns in dynamic

environments, as a driving by the rear-view mirror approach suggests.


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1.2. MOTIVATION 3

The third set is comprised ofjudgmental methods[Wright and Ayton,1987, Onkal-Atay

et al.,2008]. In such approaches experts opinions and intuitive judgments are incorporated,via surveys or other similar tools, to arrive at estimates about the future. Typically

capturing the view of a single expert, such methods serve as the cheapest and most

common technique of forecasting in practice. They expectably come with some caveats

though [Lawrence et al.,2006a,Sniezek,1990]. It remains hard for an expert to formulate

her knowledge and experience in a quantitive manner. It is also difficult for a forecaster

to identify experts, then elicit and weight their various opinions [Ashton and Ashton,

1985,Batchelor and Dua, 1995]. Moreover, judgmental methods require substantial time to

complete, resulting in a poor fit for cases where time is of the essence.

Artificial intelligence (AI)approaches have more recently emerged, attempting to provide

a more decent solution to some of the above issues. A wide variety of algorithms is put into

use in both experiments and practice, mimicking among others the underlying principles

of causal and time-series methods, but also judgmental ones (e.g. neural networks [Hill

et al., 1994,Zhang et al.,1998]). Performance against some of the issues has improved, yet

others problems have been raised, while the research field remains definitely hot. Beyond

any doubt though, the holy grail of a universal forecasting algorithm remains for the

foreseeable future an elusive promise. Traditional forecasting approaches together with AI

ones seem to have converged upon a ceiling that is far from easy to transcend, no matter

the computational complexity of the algorithms being used.

In this context, breakthroughs are rather to be expected in new research directions,

challenging widely accepted assumptions while bringing together knowledge from discrete

domains. Towards such a target, research on judgmental techniques seems shallow and

less on par with the latest blossom of AI. A revisit of core judgmental practices to takefull advantage of virtues while mitigating drawbacks might provide for contribution

opportunities. More specifically, their capacity to discover, aggregate and process relevant

information, lying at the core of the forecasting problem, might be founded on a different

basis, given an alternate approach to incorporate the knowledge of the many.


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1.3 Structure

This Thesis will focus on the study of markets as a forecasting tool. The so-called prediction

markets will be investigated from four diverse perspectives. Literature review, theoretical

properties, theoretical evaluation and empirical analysis will each contribute towards

understanding and utilization of markets as an essential tool for forecasting purposes.

Table 1.1provides a closer look at the research approach to be followed, as well as to the

general and specific questions that will be attempted to address.

Literature review will attempt to study all relevant to the topic publications that

the author was able to locate by the time of writing. Theoretical properties will focus

on the algorithmic foundation of markets as a prediction tool, contributing a generic

mathematical framework and specific functions for its coherent operation. Theoretical

evaluation will attempt to provide evidence on the theoretical soundness of the proposed

mechanism. Empirical analysis will seek to expand such results into a wide set of

experiments within and beyond the laboratory, along with giving shape to a framework

for the practical implementation of markets for prediction purposes. Finally, advanced

topics will investigate the applicability of prediction markets for event detection wherecontinuous information flow is available.


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1.3. STRUCTURE 5

Research Approaches & Questions

Research Approaches General Research Ques-

tions

Specific Research Questions

Literature ReviewWhat is the literature on

prediction markets?

Why & how do prediction markets work?

What are their fundamentals of operation?

What is their evolution in recent years?

What is the existing volume of publications?

Which are the topics covered by literature?

Theoretical Properties

What is a proper

prediction market

mechanism?

What market mechanisms are used in general?

What prediction market mechanisms are used?

Which are the properties for a coherent (proper)

price function of a prediction market?

Which are coherent price functions?

Theoretical Evaluation

Does a proper

prediction market work

in theory?

Will a proper prediction market converge to a

consensus equilibrium?

If yes, how fast is the convergence process?

What is the best possible equilibrium?

Will a proper prediction market always converge

to it?

Empirical Analysis

Does a proper

prediction market work

in practice?

Is there a software platform supporting a proper

prediction market mechanism?

Is a proper prediction market able to converge to

equilibrium in practice?

What is a practical framework for prediction mar-

kets deployment?

Advanced Topics

Are prediction markets

useful for event

detection?

What is the state-of-the-art in text mining for mar-

ket prediction and volatility analysis?

What is a model of volatility for prediction mar-

kets?

How can such a model serve to detect events?

Does such a model work in practice?

Table1.1 Research approaches and questions


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Chapter 2

Background

2.1 Overview

Following Chapter1,the current Chapter provides the context within which this Thesis

will be positioned. Markets and their generic functions are studied to begin with in

Section2.2. Then, in Section2.3focus is put on those market functions that stand as the

reason detre of prediction markets. The definition and core properties of this forecasting

mechanism is then followed by an extended literature review [Tziralis and Tatsiopoulos,

2007] that follows in Section 2.4. As part of this review, a classification of literature is also

presented in Section2.5, which might be of value for beginners or experts, also researchers

and practitioners of the field. Finally, Section2.7summarizes the outcomes of the Chapter

while providing a number of suggestions for future work on top of it.

2.2 Markets, in general

[Aristotle,1972] (ca334-323BC) suggested that when diverse groups

all come together [...] they may surpass-collectively and as a body, although

not individually-the quality of the few best. [...] When there are many who

contribute to the process of deliberation, each can bring his share of goodness

and moral prudence... some appreciate one part, some another, and all together

appreciate all.

7


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8 CHAPTER2. BACKGROUND

Here, then, is a clear suggestion that many minds, deliberating together, may improve

on the quality of the few bestSunstein[2008].

Provided the mass of the people is not too slave-like, each individual will

indeed be a worse judge than the experts, but collectively they will be better,

or at any rate no worse. ([Aristotle,1972], ca334-323BC)

Fast forward twenty three centuries,Hayek[1945] takes this further:

Fundamentally, in a system in which the knowledge of the relevant facts is

dispersed among many people, prices can act to coordinate the separate actionsof different people.

[...] We must look at the price system as such a mechanism for communicating

information if we want to understand its real function.

[...] if it were the result of deliberate human design, and if the people guided by

the price changes understood that their decisions have significance far beyond

their immediate aim, this mechanism would have been acclaimed as one of the

greatest triumphs of the human mind.

Within this context, markets may be generally perceived as the best available mechanism

for gathering and aggregating dispersed information from private, self-interested economic

agents [Kagel and Roth,1995,Hahn and Tetlock, 2005b]. Indeed, yet arguably, the most

descriptive theory of market operations as of today is the efficient market hypothesis

[Fama, 1970], suggesting that an efficient market continuously reflects the sum of all

available information about future events into security prices.

On the same direction, the theory of rational expectations acknowledges the ability ofmarkets to convey information through the prices and volumes of traded assets [ Muth,

1961,Lucas,1972,Grossman, 1981]. In the more recent decades, serial research attempts

on experimental economics suggested that markets might be created specifically to collect,

aggregate and publish information, potentially requiring special market designs and

institutions to leverage on such capacity[Plott and Sunder,1982,Forsythe et al.,1982,Plott

and Sunder,1988,OBrien and Srivastava,1991,Plott,2001], also returning a Nobel prize

in Economics to the pioneer of the field, Vernon Smith [E-Museum,2003,Altman,2004].


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2.2. MARKETS, IN GENERAL 9

The above essentially laid the foundations of what matured to serve the concept of

markets for information and forecasting, also the core of the thesis on hand; however,before a closer look to the specific market function takes place, a rough depiction for

markets in general follows hereby.

Financial markets are institutions that incorporate by their very nature and facilitate, in

one way or another, all of the four fundamental functions ofinvestment,hedging,speculation

andinformation aggregation. Several institutions have been deployed during the years to

efficiently support these functions, mostly by focusing on just one of them. To name a few,

stock markets are created and operating with the primary purpose of capital allocation,

futures markets scope in hedging risks and betting markets serve as a physical destination

for wagering.

However, each one of these institutions facilitates also and in parallel the entirety of

the core market functions. For example, stock markets serve as a hospitable environment

for speculation as well, while hedging could be accomplished by holding a wide portfo-

lio of the traded assets and stock prices are unbiased estimators of firm fundamentals

[Mandelbrot,1966]. In this context, there exists significant evidence supporting the general

presence of the fourth native function of markets operation, namely their aggregative and

predictive nature. This presence is strong even in institutions that were designed with

different objectives in mind, in both investing [Admati and Pfleiderer, 1987,Chen et al.,

2007,Grossman and Stiglitz,1980b,Hellwig,1980,Holmstrom and Tirole,1993,Lo,1997],

hedging[Jackwerth and Rubinstein, 1996b,Krueger and Kuttner, 1996, Roll, 1984] and

wagering cases [Boulier and Stekler, 2003,Debnath et al., 2003,Figlewski,1979,Gandar

et al.,1998,Schmidt and Werwatz,2002,Thaler and Ziemba,1988b,Winkler,1971].

But, while there exist well-established and widely used market institutions to fullyexploit each of the first three essential features of markets operation, the fourth funda-

mental function of information aggregation was not a raison detre of a market till the

comparatively recent emergence and development of prediction markets.

A computational perspective to the information (or belief) aggregation problem is

also applicable. Such an approach describes a number of agents (or experts) holding

different and non-independent sets of information about an uncertain variable, with the

target of designing a function to extract and summarize agents information, providing a


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collective estimate which ideally equals the omniscient forecast that has direct access to all

the information available to all informants (a sensible consensus).The problem is a classic one in statistics and decision science, often studied under the

concept of opinion pools and expert weights. Pennock and Wellman[1997] attempts

to summarize a number of related references, the gist of which is well put by French[1985],

suggesting that no proposed solution avoids a certain arbitrariness, also casting doubts

for the very likelihood of a universally acceptable solution.

In this context, the idea of using markets for the purpose of belief aggregation arose

as early as forty years ago byEisenberg and Gale[1959]. The researcher experimented

with a typical pari-mutuel scheme, aggregating agents bets for a number of events, finally

resulting in a consensus probability equal to the proportion of the total bet on each event,

asPennock and Wellman[1997] mentions.

WhileEisenberg and Gale [1959] demonstrated that such a mechanism returned a

unique set of equilibrium probabilities as desirable, and Norvig [1967] figured out a

dynamic process for reaching this equilibrium through iterated bids,Genest and Zidek

[1986] observes that this approach has never enjoyed much popularity, due to the

arbitrariness of the results in the case of low number of agents and volume.

That said, however, and while the vast majority of work already performed on the

information aggregation problem has nothing to do with the concept of a market, the

inherent limitations of other solutions and references such as the above highlight the

opportunities for a market solution and suggest its further exploration and exploitation,

also from a computational perspective next to an economics and operations research one.

2.3 Fundamentals of Prediction Markets

Prediction markets emerged fairly recently as a promising forecasting mechanism, capable

of efficiently handling the dynamic aggregation of dispersed information among various

agents. The interest that this mechanism attracts seems to be growing, both in terms of

business applications and academic work [Tziralis and Tatsiopoulos,2007], while it is by

now accepted as a widely used forecasting mechanism [Armstrong, 2006]. This section

provides a definition of prediction markets, next to a summary of their fundamental


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2.3. FUNDAMENTALS OF PREDICTION MARKETS 11

properties that may serve as a short introduction to the concept.

There is no universally accepted terminology and definition of prediction markets.After performing an extensive research on the existing literature, the following definition,

based on the one given byBerg and Rietz[2003], is adopted and suggested for broader

acceptance and use.

Prediction markets are defined as markets designed and run for the primary purpose of mining

and aggregating information scattered among traders; then transforming such information into

market prices serving as predictions about specific future events. [Tziralis and Tatsiopoulos,

2007]

Due to their unique objectives and nature, serving simultaneously as a forecasting tool

and market mechanism, prediction markets feature a number of distinct characteristics,

the most important of which are summarized hereby.

First, prediction markets serve as an efficient way of arriving into consensus about

the possibility of a future event. AsHanson[1992] first observed, the mechanism may

be the only available one to enable creating a consensus about future events in a way

simultaneously open, egalitarian, honest, self-consistent and operationally cheap.

Second, as also mentioned before, prediction markets serve as a well-working frame-

work forbelief aggregation. Pennock and Wellman[1997] suggest that markets incentivize

self-motivated agents to gather all cost-effective relevant information and truthfully reveal

their private beliefs. By setting up financial securities that their return is related to future

events, belief aggregation is performed in a natural manner and market prices may be

interpreted as an aggregate probability of the participants beliefs.

Third, and similar to the previous characteristic, prediction markets serve as an efficient

mechanism with regards to information incorporationfor future events. Market participantsget informed by observing prices, thus information get spread, processed and incorporated

by everyone for prices to fluctuate as if everyone had access to all information [Pennock

et al.,2002]. As a result, it is suggested that prediction markets may be used to efficiently

incorporate all related information to a future event, with empirical results flowing in to

confirm the appeal of such an approach [Chen et al.,2006b].

Fourth, in contrast to other market institutions, prediction markets accuracy can be

assessed. Stock or future markets, for example, measure what will happen over an infinite


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period, but this is not the case with prediction markets. Being related to a specific future

event, prediction markets simultaneously suggest their probability of being mistaken butcan also be judged based on that, when the predicted event gets materialized, or not

[Einbinder,2006].

Fifth, and the final core characteristic is that, no matter how prediction markets are

modeled or not, they effectively and largely stand as an empirical science. Human agents

participation might be enough to testify that the study of prediction markets can be

attributed to strictly be a social science, and this has to be taken into account in every

approach that is not such. In this context, much of the existing literature, as it will be

documented in the next paragraph, focuses on laboratory and field experiments, testing

prediction markets behaviors in various settings. Such attempts are also provided across

this dissertation, however, and having said that, as happens with other species of markets,

economic, computational and operations research theories and practices still apply and

they will serve as the core perspective and set of tools for this thesis.

2.4 Literature Review

2.4.1 Context

This review surveys and examines the relevant existing literature and its trends, while it is

also designed to provide a unique starting point for the further study of PM literature.

Prediction markets provides an exciting field for research, partly due to its novelty and

growth. This section attempts a comprehensive review and classification of the literature

on prediction markets research, starting from its introduction and the first applications

of the PM concept in the early nineties [Hanson,1990b,1992,Kuon, 1991,Forsythe et al.,

1992] up until the writings of this section. The scheme used represents the authors view

of the focus and direction of prediction market research and reveals a rapid growth in the

number of published articles. The current state and direction of research topics should be

of interest to many and it is hoped that it will serve both academics and practitioners as a

point of reference for the study of the subject.

The literature review is organized as follows. First, the research methodology is


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2.4. LITERATURE REVIEW 13

described, followed by a commentary on the current diverse terminology existing in the

prediction market field, along with the evolution and growth of the literature and researchitself. Subsequently, a classification method is introduced and its results are analyzed.

The review finally concludes by presenting research implications and an extended list of

prediction market references.

2.4.2 Research Methodology

This survey is the outcome of an attempt to collect and list an extended volume of

prediction market related academic work. Before the publication of its results in Tziralisand Tatsiopoulos[2007], no relevant literature review was identified. Hitherto, there

existed, for example, no publication outlet dedicated exclusively to prediction research.

Therefore, the inclusion of various potential sources of academic knowledge dissemination

was essential.

As a result, all journal articles, conference proceedings papers, books or book chapters,

masters theses, doctoral dissertations or other unpublished academic working papers

and reports that are referring to the concept of prediction markets were collected, studied

and are cited herein. The search was conducted mostly through the internet, as well

as electronic libraries and academic databases. The literature review finally resulted in

identifying155articles, which are classified by type of publication as shown in Table2.1. By

its very nature, this review could therefore be characterized as extended but by no means

as exhaustive. Nevertheless, it may serve as a comprehensive basis for understanding

prediction market research and its state of art.

2.4.3 Terminology

Prediction markets is not a unique and globally adopted descriptor of the concept and

mechanism that was defined previously. On the contrary, the terminology used to address

this concept is rather wide. The literature search was based on the following five more

usual and relevant descriptors: prediction markets, information markets, decision

markets, electronic markets and virtual markets. Moreover, the references of each

article found were further examined as to identify relevant citations that use perhaps


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Type of Publications Number of Articles

Journal articles 58(37%)

Books & book chapters 22(14%)

Conference proceedings 15(10%)

Masters theses & doctoral dissertations 7(4%)

Working papers, reports & unpublished work 53(35%)

Total 155(100%)

Table2.1 Type of Publications & Number of Articles

another descriptor. The full text of each article was then reviewed to eliminate those

articles that were not actually related to prediction markets. The final selection of155

articles was then classified on the basis of the prevalent descriptor used in each article

to describe the concept of prediction markets. The distribution is depicted in Figure

2.1. Other descriptors which were identified during the research, include political stock

markets [Berlemann and Schmidt, 2001,Bohm and Sonnegard, 1999,Brueggelambert,

2004,Forsythe et al., 1994, 1992,Hansen et al., 2004,Hauser and Huber, 2005,Jacobsen

et al., 2000,Murauer, 1997,Ortner et al.,1995], election stock markets[Antweiler and

Ross,1998,Brueggelambert and Crueger,2002,Forsythe et al.,1995,1998,Kou and Sobel,

2004,Kuon,1991], artificial markets [Pennock et al.,2000,2001a,b] and idea futures

[Hanson,1990b,1992,Passmore et al.,2005b].

It becomes clear that the terminology used to describe the same concept is very diverse

and extensive. This fact could lead to the division of the prediction markets community

and its research products at a very early stage of its development and makes the agreement

on globally accepted and standardized terminology all the more important, the author

argues.

2.4.4 Early works and evolution

The series of articles that Hanson published between 1990 and 1992 [Hanson, 1990a,b,

1991, 1992] are the very first introductory texts on the topic of prediction markets. The


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Figure2.1Number of articles per term used to describe the concept of PM.


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earliest application of a prediction market mechanism, the Iowa Electronic Markets, was

initiated in1988and was originally designed to study market dynamics while predictingthe outcome of US presidential elections. Forsythe, Nelson, Neumann, and Wright[1992]

wrote the first academic article on the Iowa Electronic Markets in 1992. The early works of

the nineties focused mainly on political stock markets applications. Aside from the papers

on the most popular prediction market, the Iowa Electronic Markets [Berg et al.,1996,1997,

Forsythe et al.,1994,1992,1999], other election markets were described and analysed, like

the one founded as early as 1990in Germany [Beckmann and Werding,1996,Kuon,1991],

as well as others in Canada[Antweiler and Ross, 1998,Forsythe et al., 1995,1998], Austria

[Murauer, 1997,Ortner et al., 1995] and Sweden [Bohm and Sonnegard, 1999]. Ortner also

made important contributions to the field with his doctoral dissertation in 1996[Ortner,

1996] and the description of prediction markets first application as a business tool by

Siemens Austria in 1997[Ortner,1997,1998].

Prediction markets literature up until1998is limited to mainly those above-mentioned

articles. In the following years however, this survey witnesses a significant increase in

the volume of publications. The publication trend, as depicted in Figure 2.2, could be

roughly described as being of exponential growth: the number of relevant articles in2002

corresponded to14, increased to 22 during2004, while in the first8 months of2006there

were already 34 published articles.

Among these most recent articles it is of paramount importance to mention the pio-

neering work of Pennock on a dynamic pari-mutuel market framework [Pennock, 2004]

and of Hanson on combinatorial market design [Hanson, 2003a]. Other equally significant

contributions, both in terms of citations and implications, were made bySpann and Skiera

[2003a], Wolfers and Zitzewitz[2004a] andBerg and Rietz [2003] among others. Thissubstantial increase of literature makes the need for further classification of articles in

terms of their nature of prediction markets research indispensable. This need is addressed

in the following section.


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Figure2.2Publication trend.


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2.5 Classification Method

The classification or taxonomy framework, presented in Figure2.3,was based on the above

literature review and the nature of prediction markets research. The papers were classified

into four broad categories: (a) description, (b) theoretical work, (c) applications and (d)

law and policy; and each category is further divided into subcategories. It has to be stated,

however, that this framework is designed to be rather practical than strictly documentary,

serving as a navigation tool for researchers. Each paper was assigned to the category that

describes most accurately the core of its prediction markets relevant contents alone. The

categories breakdown is described hereafter.

Figure2.3Classification of topics in prediction markets literature.

2.5.0.1 Description

This category covers descriptive literature on prediction markets research, including

introductory texts, general description, open questions, etc.


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2.5. CLASSIFICATION METHOD 19

1. Introduction: This subcategory contains mostly short and rudimental texts on the

basics of prediction markets, which are often a subsidiary topic of the publication.

2. General description: The subcategory covers lengthy and detailed articles that usually

address the analysis of a variety of aspects on prediction markets.

3. Open problems: This consists of works that highlight issues which have yet to be

addressed by the literature in a fully satisfactory way.

4. Other descriptive issues: This subcategory contains papers that discuss other descrip-

tive issues on prediction markets, such as taxonomy, its potential use in educationand other fields.

2.5.0.2 Theoretical work

The literature in this category includes papers of theoretical nature and is divided into the

following three areas:

1. Market modeling and design: This contains various texts dealing with aspects on PM

modeling, framework design and analysis.

2. Information aggregation convergence and equilibrium: The subcategory consists of pa-

pers discussing the convergence and equilibrium properties of the information

aggregation process that is hosted by prediction markets.

3. Other theoretical issues: This includes works on other theoretical issues that could

not be assigned to the previous two subcategories, such as the interpretation of

prediction markets prices.

2.5.0.3 Applications

This broad category includes the totality of papers describing or analyzing applications of

the prediction markets concept, either of experimental or practical nature.

1. Experiments: This is comprised of various experimental applications of the PM

concept, held in academic or some other environment.


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2. Iowa Electronic Markets: The subcategory contains all the papers that focus on the

description and analysis of results of the Iowa Electronic Markets.

3. Other political markets: This covers all the literature referring to political stock markets

applications, with the exception of the Iowa Electronic Markets. The references

include political stock markets in Germany, Canada, Austria, Sweden, Netherlands,

Australia and Taiwan.

4. Markets on sport events: This subcategory comprises of articles of prediction markets

applications in various sport events. Comparisons of real-money and play-money

markets are also included in this subcategory.

5. Other applications: The subcategory contains the rest of applications that could not

be assigned to any of the previous ones and includes among others business and

entertainment web games applications.

2.5.0.4 Law and Policy

This last category consists of law and policy literature on PM research.

1. Legality and regulation: This subcategory is comprised of papers referring to aspects

on the legality of PM and provides directions for their regulation.

2. Public policy and decision making: The works of this subcategory address the potential

of prediction markets in improving policy analysis and public decision making.

3. The Policy Analysis Market: This covers all the literature describing the Policy Analysis

Market, a prediction markets application that was designed to support policy analysis

on sensitive political issues, such us international affairs and terrorism.

4. Other law and policy issues: This subcategory covers other law and policy aspects on

prediction markets.

2.5.1 Classification Results

The 155 papers found were classified according to the above mentioned model. The

distribution of articles by topics is shown in Figure 2.4. The majority of published research


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2.5. CLASSIFICATION METHOD 21

concerns prediction markets applications (72articles, 47%), whereas 36 articles (23%) were

found to be mainly of descriptive nature and27 (17%) of theoretical nature.

Figure2.4Classification results of PM literature.

Table2.2lists the number of description articles. 40% (13articles) were general descrip-

tions to the concept of prediction markets, while 30% (10articles) were of introductory

nature.

Table2.3shows the number of articles categorised as theoretical works. The majority

of them (16articles,59%) refers to market modelling issues, followed by 33% (9articles)denoting to the study of convergence and equilibrium properties.

Table2.4lists the number of articles of each PM application subcategory. 21articles

(29%) were written on other prediction markets than the Iowa political markets, 16 on Iowa

Electronic Markets (22%),15 on other applications (21%) and13 on various experiments

(18%).

Table2.5shows the number of articles in law and policy related topics. Public policy

and decision making was the dominant subcategory, as 55% (11articles) were published


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Description Number of Articles

Introduction 13(36%)

General description 13(36%)

Open problems 5(14%)

Other descriptive issues 5(14%)

Total 36(100%)

Table2.2 Number of Description Articles

Theoretical work Number of Articles

Market modelling 16(59%)

Information aggregation convergence & equilibrium 9(33%)

Other theoretical issues 2(7%)

Total 27(100%)

Table2.3 Number of Theoretical Work Articles

Applications Number of Articles

Experiments 13(18%)

Iowa Electronic Markets 16(22%)Other political markets 21(29%)

Markets on sport events 7(10%)

Other applications 15(21%)

Total 72(100%)

Table2.4 Number of Application Articles


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2.6. CONCLUSIONS AND RESEARCH IMPLICATIONS 23

Law & Policy Number of Articles

Legality & regulation 4(20%)

Public policy & decision making 11(55%)

The Policy Analysis Market 4(20%)

Other law & policy issues 1(5%)

Total 20(100%)

Table2.5 Number of Law and Policy Articles

on this topic.

Tables2.6,2.7,2.8& 2.9present a summary of all reviewed articles and assigns each

of them to their respective subcategory. This is a helpful resource for anyone looking for

prediction markets articles in a specific area.

2.6 Conclusions and Research Implications

As the nature of research on prediction markets is difficult to be limited to specific disci-

plines and the origin and growth of the literature is rather recent, the relevant material

is scattered across various sources of academic writings. As a result, the research for

this literature review was not focused exclusively on journal articles, but also extended

to conference proceedings papers, books, book chapters, masters theses, doctoral dis-

sertations and other unpublished academic working papers and reports. This literature

survey was undertaken in order to identify as many as possible prediction markets related

academic articles from various sources of prediction markets research. It resulted in theidentification of155 prediction markets articles published between 1990and 2007.

Although this review cannot claim to be exhaustive, it does provide reasonable insights

into the state of the prediction markets research. The author feels that the results presented

in this review have several important implications.

(a) Based on the data presented and the existing trends, it is expected that prediction

markets research and applications will significantly increase in the future.


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(a) Description Reference

Introduction Boyle and Videbeck[2005],Cherry and Rogers[2006],Dahan and

Hauser[2002],Hahn and Tetlock[2005a],Hahn and Tetlock[2006a],

Kambil and van Heck [2002], Passmore et al.[2005a],Spann and

Skiera[2003b],Sunstein[2006a]

General description Abramovicz[2006b],Ankenbrand and Rudzinski[2005a],MacKie-

Mason and Wellman[2006],Schrieber[2004],Soukhoroukova and

Spann[2006],Spann and Skiera[2003a],Spann and Skiera[2004],

Surowiecki [2004], Tziralis and Tatsiopoulos [2006], Wolfers andZitzewitz[2004a],Wolfers and Zitzewitz[2006a],Williams[2005],

Yang[2005]

Open problems Bubb [2005], Hanson [2006a], Sunstein [2004], Sunstein [2006b],

Wolfers and Zitzewitz[2006b]

Other descriptive issues Ankenbrand and Rudzinski[2005b],Manne[2005],Passmore et al.

[2005b],Pennock and Wellman[2001],Simkins[1999]

Table2.6 Classification of Reviewed Literature, (a) Description

(b) Theoretical Work Reference

Market modelling and

design

Bergfjord[2006],Chan[2001],Chen[2005],Chen et al.[2006b],Fang

et al.[2005],Fortnow et al.[2004],Hanson[2002b],Hanson[2003a],

Kazumori[2004], McAdams and Malone [2005], Pennock[1999],

Pennock[2004],Pennock et al.[2002],Pennock and Wellman[2001],

Tetlock and Hahn[2006],Tetlock et al.[2005]

Information aggregation

convergence & equilib-

rium

Berg et al.[2003],Feigenbaum et al.[2005],Gjerstad[2005],Hanson

[2002a],Hanson and Oprea[2004],Koessler et al.[2005],Noeth et al.

[1999],Ottaviani and Sorensen[2005],Pennock and Wellman[1997]

Other theoretical issues Manski[2006],Wolfers and Zitzewitz[2006c]

Table2.7 Classification of Reviewed Literature, (b) Theoretical Work


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2.6. CONCLUSIONS AND RESEARCH IMPLICATIONS 25

(c) Applications Reference

Experiments Barner et al.[2004],Berlemann and Forrest[2002],Chan et al.[2002],

Chan et al.[2001],Chan et al.[1999],Chen et al.[2001],Chen et al.

[2003],Chen et al.[2004],Hanson et al.[2006],Jaisingh et al.[2002],

Ledyard et al.[2005],Plott and Chen[2002],Rietz[2005]

Iowa Electronic Markets Berg et al.[2000],Berg et al.[1996],Berg et al.[1997],Berg and Rietz

[2002],Berg and Rietz[2003],Berg and Rietz[2006],Bondarenko and

Bossaerts[2000],Erikson and Wlezien[2006],Forsythe et al.[1994],

Forsythe et al.[1992],Forsythe et al.[1999],Fowler[2006],Kou and

Sobel[2004],Oliven and Rietz[2004],Pagon[2005],Rickershauser

[2006]

Other political markets Antweiler and Ross[1998],Beckmann and Werding[1996],Berle-

mann and Schmidt[2001],Bohm and Sonnegard[1999],Bruegge-

lambert[2004],Brueggelambert and Crueger[2002],Filzmaier et al.

[2003], Forsythe et al.[1995], Forsythe et al.[1998],Hansen et al.

[2004], Hauser, Hauser and Huber[2005], Jacobsen et al. [2000],

Kuon [1991], Leigh and Wolfers [2006], Murauer [1997], Ortner

[1996],Ortner et al.[1995],Rhode and Strumpf[2006],Wang et al.

[2005],Wolfers and Leigh[2002]

Markets on sports events Bean[2005],Chen et al.[2005],Debnath et al.[2003],Rosenbloom

and Notz[2006], Schmidt and Werwatz[2002], Servan-Schreiber

et al.[2004],Smith et al.[2005]

Other applications Gruca[2000], Gruca et al. [2001], Gruca et al. [2003], Gruca et al.

[2005],Gurkaynak and Wolfers[2006],Mangold et al.[2005],Ortner[1997],Ortner[1998],Pennock et al.[2000],Pennock and Wellman

[2001]. Skiera and Spann[2004],Snowberg et al.[2005],Snowberg

et al.[2006],Soukhoroukova and Spann[2005],Tetlock[2004]

Table2.8 Classification of Reviewed Literature, (c) Applications


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(d) Law & Policy Reference

Legality and regulation Abramovicz[1999],Bell[2002],Bell[2006],Hahn and Tetlock[2006b]Public policy and deci-

sion making

Abramowicz [2004], Abramovicz[2006a],Einbinder[2006],Hahn

and Tetlock[2004], Hahn and Tetlock [2005b], Hahn and Tetlock

[2005c],Hanson[2003b],Hanson[2006c],Ledyard[2006],Wolfers

and Zitzewitz[2004b]

The Policy Analysis Mar-

ket

Hanson[2005],Hanson[2006b],Meirowitz and Tucker[2004],Polk

et al.[2003]

Other law and policy is-

sues

Abramovicz[2004]

Table2.9 Classification of Reviewed Literature, (d) Law and Policy

(b) There is a strong need to standardize the terminology used to refer to the prediction

markets concept.

(c) The formation and dissemination of a fully appropriate prediction markets mecha-

nism, such as the dynamic pari-mutuel presented byPennock[2004], could lead to

the expansion of prediction markets research and applications.

2.7 Discussion

2.7.1 Summary

This Chapter provided a close look to the concept of prediction markets, serving as the

context on top of which this thesis will be deployed. Starting with a general description of

the mechanism and a short summary of the developments that led to it, it proceeded to

outline the mechanisms core characteristics and its differentiation to other existing market

tools or forecasting approaches. After that, an extensive literature review on prediction

markets was performed and deployed at full detail, followed by a classification of the

literature that may serve as a stand-alone resource for anyone willing to study a subset

of the topic. In the authors knowledge there does not exist a similar toTziralis and

Tatsiopoulos[2007] work on prediction markets literature published so far, so this might


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Chapter 3

Theoretical Properties of Prediction

Markets

3.1 Overview

Chapter 1 set the generic forecasting problem to address; Chapter 2 shaped out the

market framework towards its solution. The current Chapter proceeds to the algorithmicperspective of the problem on hand and the mechanism that orchestrates the operation of

a prediction market, standing as the theoretical core of the Thesis.

The mechanisms in use by well-established market institutions, focusing on all but

the market function of information aggregation, appear not to be suitable for that latter

one. These mechanisms typically include Continuous Double Auction, Market Maker and

Pari-mutuel Market, each one of them appearing to have at least one significant drawback

for the use under focus. The need for a more appropriate mechanism was addressed

by the recent introduction of Market Scoring Rules by [Hanson, 2003a] and Dynamic

Pari-mutuel Market by[Pennock,2004]. The current Chapter moves forward to introduce

some additions and shape a coherent framework of properties for a prediction market

mechanism, further addressed by a couple of market functions which are also deployed.

This contribution intends to provide an alternative to the core market problem, which is

thoroughly studied under various perspectives in the Chapters to follow.

The Chapter starts with Section 3.2, discussing the typical market mechanisms in use

29


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30 CHAPTER3. THEORETICAL PROPERTIES OF PREDICTION MARKETS

by well-established market institutions, next to their drawbacks for use in a prediction

market setting. Section3.3proceeds to deploy the state-of-the-art mechanisms of MarketScoring Rules and Dynamic Pari-mutuel Market, while highlighting some directions for

further improvement. Section 3.4builds on these directions to contribute a framework

of properties for a coherent price function in a DPM setting. Then, Sections 3.5 & 3.6

introduce such functions, proving their partial or fully compliance with the proposed

framework. Finally, Section3.7sums up and discusses the Chapters contributions, while

providing directions for future deployment.

3.2 Market Mechanisms

3.2.1 Description & Characteristics

During the years, a wide variety of financial and wagering mechanisms have been devel-

oped and matured to support various operations of well-defined market institutions, as

already shaped out in Section 2.2. Money allocation, speculative trading (i.e. wagering)

and hedging (i.e. insuring) against exposure to uncertain events, next to information

aggregation, all are operations facilitated in one way or another within an extended set of

institutions, which have matured over time. However, the variance of market institutions

occurs not only due to the differentiation of their scope of operations (what is being

traded, and why), but also because of the various financial and wagering mechanisms

that have been developed to efficiently serve each one of these operations (how it is

traded). This section describes and studies the operational properties of these mechanisms,

providing the needed input for assessing their suitability of usage in the case of prediction

markets.

To authors knowledge and according toPennock[2004]s sufficient review, the main

and most used market mechanisms are:

(a) CDA: Most modern financial markets use a continuous double auction mechanism

to store and match orders and facilitate trading [Smith et al.,2003]. The dominant

mechanism used in financial circles is the continuous double auction (CDA).

(b) Market-maker: The primary mechanism used for sports wagering is a bookie or


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3.2. MARKET MECHANISMS 31

bookmaker, who essentially acts exactly as a market maker. A CDA with market

maker (CDAwMM) is also common in financial circles.

(c) Pari-mutuel: Horse racing and other wagering games traditionally employ the

pari-mutuel mechanism.

The next paragraphs describe in more detail the operational properties of each one

of these market mechanisms, followed by a study on their suitability for information

aggregation purposes.

3.2.2 Continuous Double Auction

The continuous double auction (CDA) stands as a very popular and widely used market

mechanism. [Smith et al., 2003] note that it is used by most of the modern financial

markets to store and facilitate trading; examples include stock markets, option and other

derivatives markets, insurance markets and market games [Jackwerth and Rubinstein,

1996b,Roll, 1984,Forsythe et al., 1992, 1999,Hanson, 1990a,Chen et al., 2001,Pennock

et al.,2001a,b]. Moreover the CDA has been documented to effectively perform the tasks of

information aggregation and prediction of future events [Plott and Sunder,1988,Copeland

and Friedman,1987, 1991,1992,Forsythe and Lundholm,1990,Friedman,1993,Sunder,

1992, 1995,Plott,2000]. The building block of a CDA is the simple call market auction. In

such an auction, bids arrive asynchronously and are collected over time, then processed

together in large batches. Ifm the number of sellers, the clearing price is typically defined

as equal or in between of the mth andm+ 1th lowest prices.

A CDA is a continuous version of the call market, where a transaction is immediately

executed at the time that a trade is acceptable by any two bidders (usually at the bid price

of the least recent bidder). In more detail, if at any time a trader is willing to buy one unit

of an asset at a bid price pbid , while another party is willing to sell one unit of the asset

at an ask price pask, then any case that pbid > paskresults into a transaction (typically at

some price between pbid and pask). In other words, buyers can only buy as many shares as

sellers are willing to sell, and for any transaction to occur, there must be a counterpart

on the other side willing to accept the trade. In this context, the auctioneer takes on no


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risk as her only function is to match willing bidders, and this serves as another significant

advantage of the mechanism.However, in the case that the highest bid price is less than the lowest ask, nothing

occurs. As a result, it is expected for prices in a CDA-powered market either to remain

illiquid or rapidly change as new information arrives and traders reassess the value of an

asset. Illiquidity is rather typical especially when only few traders participate, turning to

be a significant drawback of CDA. Moreover, the spread between the highest bid price and

the lowest ask price may vary to arbitrarily large values, or one or both queues may be

completely empty, further discouraging trading. This chicken and egg scenario of scarce

traders do not participating at all because the expected time for a trading partner to arrive

is indefinite is also known as the thin marketproblem. This is particularly highlighted in

combinatorial markets, where the number of available stocks is extensive, further squeezing

the likelihood of a match between traders in a specific stock. The above problems result in

reduced incentives for traders to participate, and limited informativeness of the market

itself.

3.2.3 Market-maker

To address CDAs problems, the mechanism is often used with a market maker (CDAwMM)

to induce liquidity. TheMarket Maker (MM) is an agent who is nearly always ready to

trade, willing to accept a much larger volume of buy and sell orders than typical traders.

MM may be a person or automated algorithm, posting various bid and ask prices for

other traders to trade with. In other words, a single trader is enough for a transaction

to take place under the existence of a MM, who efficiently cancels out the thin market

problem and other CDAs drawbacks, resulting in a continuous and uninterrupted flow of

information.

The typicalbookmaker, the default mechanism for wagering, also functions like a MM

in a CDA. The main difference lies in the direction of the transactions available; in the

bookmaker case the market institution sets the odds only for other players to buy and

not to sell, in a take it or leave it fashion. The odds remain fixed at the time of a bet,

initially defined according to expert opinion and later updated in response to the relative


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3.2. MARKET MECHANISMS 33

level of wagering on the various outcomesPennock[2004]. Both MM and the bookmaker

have also a strong record of success in the information agreggation task [Jackwerth andRubinstein,1996b,Krueger and Kuttner,1996,Roll,1984].

It is expected though for MMs performance to come at a price; and this is exposure to

risk of losing considerable amounts of money for the MM. As the mechanism does not

only provide the matching functionality, there is an operational cost or benefit associated

with the performance of the trader that essentially a MM is, depending on the evolution of

prices in stocks that the MM comes to own. This risk is not necessarily a negative property,

providing a proper way of injecting subsidies into the market Pennock and Sami[2007]. In

this way, informed traders rationally expect to receive a profit out of their transactions.

However, it is of specific importance for the operational risk of a MM to be bounded,

next to the normal requirements of providing proper incentives for truthful information

submission by traders, and a computationally tractable way of determining the new prices

after any transaction. To achieve this, a fully systematic way for the adjustment of bid and

ask prices after every trade needs to be set up. The result is known as an automated market

maker.

3.2.4 Pari-mutuel

The third market mechanism available is the pari-mutuel market (PMM) one. Pennock

[2004] describes its usage as common at horse races [Ali,1977,Rosett,1965,Snyder,1978,

Thaler and Ziemba,1988b,Weitzman,1965]and other similar wagering games. In such a

market, people place wagers on which of two or more mutually exclusive and exhaustive

outcomes will occur at some time in the future. When the true outcome becomes known,

players who wagered on the correct outcome split the total amount of money invested in

proportion to the amount they have wagered, while those invested in all other outcomes

lose their money (in practice, the total amount redistributed is diminished, for the market

institution to receive a fee). In essence, wagers compete against each other to receive the

largest share possible out of all money invested.

In mathematical terms, letn be the mutually exclusive and exhaustive outcomes (e.g.,

n horses, exactly one of which will win), while M1,M2, . . . ,Mn euro in total are bet on


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each outcome. If outcomei occurs, then everyone who bet on an outcome j =i loses their

wager, while everyone who bet on outcomei receives an equal amount of

n

j=1Mj

/Mi

euro for every euro they wagered (in practice, the total amount redistributed is lowered

due to institutions fees). In other words, the cost of purchasing an equal share of the

winnings remain constant (for example, 1 euro) [Pennock and Sami,2007], and it is not

conditional to the time the wager was placed, or the amount of money invested in the

various outcomes, but just depends on the final amounts wagered on all outcomes when

the market closes, next to the identity of the correct outcome.

Pari-mutuel markets have a number of different operational characteristics when

compared to CDA or market-maker. Unlike a CDA, in a pari-mutuel market anyone

can place a wager of any amount at any time, without the need for a matching offer

from another bettor or a market maker, providing in a sense infinite liquidity for buying.

Moreover, unlike a CDAwMM, this liquidity comes at no risk for the market institution,

since money is only redistributed from losing wagers to winning wagers.

On the other hand, pari-mutuel markets are not suitabl

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A Market Approach to Forecasting: Background, Theory and Practice

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