a market approach to forecasting: background, theory and practice
TRANSCRIPT
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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
Georgios G. Tziralis
All Rights Reserved
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ABSTRACT
A Market Approach to Forecasting:
Background, Theory and Practice
Georgios G. Tziralis
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
3.5.1 Simple case: n= 2 outcomes . . . . . . . . . . . . . . . . . . . . . . . 50
3.5.2 Extension forn = koutcomes. . . . . . . . . . . . . . . . . . . . . . . 55
3.6 A Coherent Price Function II . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.6.1 Simple case: n= 2 outcomes . . . . . . . . . . . . . . . . . . . . . . . 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
1
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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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4 CHAPTER1. INTRODUCTION
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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6 CHAPTER1. INTRODUCTION
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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.
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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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10 CHAPTER2. BACKGROUND
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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12 CHAPTER2. BACKGROUND
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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14 CHAPTER2. BACKGROUND
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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2.4. LITERATURE REVIEW 15
Figure2.1Number of articles per term used to describe the concept of PM.
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16 CHAPTER2. BACKGROUND
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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2.4. LITERATURE REVIEW 17
Figure2.2Publication trend.
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18 CHAPTER2. BACKGROUND
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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20 CHAPTER2. BACKGROUND
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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22 CHAPTER2. BACKGROUND
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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24 CHAPTER2. BACKGROUND
(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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26 CHAPTER2. BACKGROUND
(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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28 CHAPTER2. BACKGROUND
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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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32 CHAPTER3. THEORETICAL PROPERTIES OF PREDICTION MARKETS
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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34 CHAPTER3. THEORETICAL PROPERTIES OF PREDICTION MARKETS
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