Prediction Markets versus Alternative Methods [Elektronische Ressource] : Empirical Tests of Accuracy and Acceptability / Andreas Graefe. Betreuer: C. Weinhardt

Andreas Graefe

Informations

• Publié par: karlsruher_institut_fur_technologie
• Publié le: 01 janvier 2009
• Nombre de lectures: 14
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Prediction Markets versus Alternative Methods
Empirical Tests of Accuracy and Acceptability



Zur Erlangung des akademischen Grades eines
Doktors der Wirtschaftswissenschaften

(Dr. rer. pol.)

von der Fakultät für
Wirtschaftswissenschaften
der Universität Karlsruhe(TH)

genehmigte
DISSERTATION

von

Dipl.-Volkswirt, Dipl.-Wirtsch.Inf. Andreas Graefe



Tag der mündlichen Prüfung: 25. Mai 2009
Referent: Prof. Dr. Christof Weinhardt
Korreferent: Dr. Armin Grunwald


2009, Karlsruhe

Acknowledgments
am indebted to many people for their support and collaboration throughout this
Ph.D. project. I would like to thank my supervisor Christof Weinhardt for his strong I
support and for continuously providing valuable insight and comments. I would also
like to thank Armin Grunwald for co-advising this thesis and giving me the opportunity to
work in the highly interdisciplinary research environment at the Institute for Technology
Assessment and Systems Analysis (ITAS) at the Karlsruhe Institute of Technology. There, I
was able to discuss and exchange ideas with colleagues from different fields and was
involved in research projects that were beyond the focus of my thesis, which provided a
stimulating environment to grow and learn. Thereby, special thanks to my friend and
mentor Carsten Orwat who navigated me through my first steps in the academic world and
was always there with helpful advice. I would also like to express my gratitude to my
colleagues at the Information & Market Engineering Group at the University of Karlsruhe
for providing constructive feedback and making it a pleasure to attend the numerous
doctoral seminars. Special thanks to Stefan Luckner for proofreading the manuscript and
providing helpful hints and pointers.

I would especially like to thank J. Scott Armstrong for providing the opportunity to work
with him for the past two years as a visiting scholar at the University of Pennsylvania’s
Wharton School. Scott has been an inspiring mentor and a wonderful friend. Under his
guidance I was constantly pushed and challenged. He made my stay an exciting and fruitful IV Acknowledgments

experience and helped me discover my passion for research. Moreover, his and his family’s
warm welcome made me fall in love with the city and people of Philadelphia. Thanks, Scott.

I dedicate this thesis to my family, my parents Lotte and Günther and my sister Kerstin. I
have relied on their love and encouragement throughout my life. Finally, I would like to
thank Jamie for her love, patience, and emotional support – and for reminding me of the life
beyond research.

Abstract
he success of prediction markets in the field of election forecasting made them T increasingly appealing to organizations and a number of companies started to
experiment with them. However, despite widespread initial interest and years of
experimental use, there are no major organizations known to use prediction markets as an
integral part of their forecasting activities. Prediction markets have not become an
established forecasting method yet.

The reasons for this are manifold. Aside from election and sports forecasting, the number of
empirical studies that analyze prediction markets’ performance is limited. In addition, the
studies are often small scale or compare the method to weak benchmarks. Since the
emergence of the field, no meta-analysis has been published to analyze prediction markets’
accuracy. Furthermore, practical experience indicates that cognitive and organizational
barriers thwart the implementation of prediction markets within organizations.

This work provides further empirical evidence on the performance of prediction markets
and analyzes the method’s acceptability among participants. Results from a field experiment
showed that prediction markets performed equally well as the Delphi method for long-term
forecasting problems. Similar results were derived from a laboratory experiment on a
quantitative judgment task: overall, prediction markets performed equally to Delphi,
nominal groups, and meetings. Furthermore, they appeared to be particularly valuable for
problems where multiple group members had valid information. VI Abstract

However, laboratory experiment participants had comparably unfavorable perceptions of
prediction markets, particularly in terms of difficulty of participation, which may lead to
low confidence in market results. This seemed to be confirmed by the fact that market
results were discounted more often than results from the three benchmark approaches. Yet,
in discounting the market results, participants did not improve accuracy; they harmed it.
The results suggested that prediction market participants were unable to judge the quality of
market results and, thus, should have refrained from revising them.

The empirical evidence from this work supports the value of prediction markets for
forecasting. However, it also revealed that prediction markets are afflicted with unfavorable
perceptions of participants. This conformed to practical experience indicating barriers to
the method’s implementation within organizations. Future research aimed at identifying
and overcoming these barriers is of crucial importance. There is a need for further empirical
studies that analyze performance of prediction markets for different types of problems and
in different settings. This should involve the use of prediction markets in conjunction with
traditional means of forecasting. In addition, market engineering should search for ways to
make market platforms more accessible, particularly to non-experience participants.




Contents
CONTENTS......................................................................................................................VII
FIGURES .......................................................................................................................... XI
TABLES ..........................................................................................................................XIII
ABBREVIATIONS ............................................................................................................XV
1 INTRODUCTION...................................................................................................1
1.1 Defining the Scope of the Work ................................................................................. 4
1.2 Motivation.......................................................................................................................... 6
1.2.1 Lack of empirical evidence..................................................................................................7
1.2.2 Cognitive barriers...................................................................................................................8
1.2.3 Organizational barriers.........................................................................................................9
1.3 Research Questions ......................................................................................................11
1.4 Overview and Structure ..............................................................................................16
1.5 Related Presentations and Publications................................................................18
2 PREDICTION MARKETS...................................................................................... 21
2.1 The Price System as Information Aggregator......................................................21
2.2 The Concept of Prediction Markets.........................................................................23
2.3 Evidence on Accuracy..................................................................................................26
2.3.1 Election forecasting ............................................................................................................26
2.3.2 Sports forecasting ............................................................................................................... 27
2.3.3 Business forecasting 28
2.3.4 Other applications.. 29
2.3.5 Summary ................................................................................................................................ 30 VIII Contents

2.4 Promising Features .......................................................................................................30
2.4.1 Enhancing quantitative forecasting methods........................................................... 30
2.4.2 Continuous and real-time information aggregation............................................... 31
2.4.3 Motivating information revelation................................................................................ 31
2.4.4 ting participation ................................................................................................... 32
2.4.5 Scalability and cost-efficiency......................................................................................... 33
2.4.6 Participatory regulation .................................................................................................... 33
3 RESEARCH METHODOLOGY.....