Miroslav Tepavac

Prediction Markets – A Review

Master’s Thesis

to be awarded the degree of Master of Science in Business Administration at the University of Graz, Austria

supervised by Assoc. Prof. Mag. Dr. Stefan Palan ([email protected]) Institute of Banking and Finance

Graz, October 2018

Author´s Declaration

Unless otherwise indicated in the text or references, or acknowledged above, this thesis is entirely the product of my own scholarly work. Any inaccuracies of fact or faults in reasoning are my own and accordingly I take full responsibility. This thesis has not been submitted either in whole or part, for a degree at this or any other university or institution. This is to certify that the printed version is equivalent to the submitted electronic one.

Table of Contents Table of figures ...... II List of tables ...... III List of Abbreviations ...... IV 1 Introduction ...... 1 1.1 Research Methodology ...... 3 1.2 Relevance of the topic & Research question ...... 6 1.3 Motivation...... 7 2 Theoretical backgrounds ...... 8 2.1 Judgmental biases as integral part of economic behavior ...... 10 3 Mechanics of prediction markets ...... 12 3.1 Contract design...... 12 3.2 Trading mechanism ...... 14 3.3 Real-money vs. play-money dilemma ...... 18 3.4 Manipulation of prediction markets ...... 20 3.5 Modes of prediction markets ...... 23 4 Application fields of prediction markets ...... 25 4.1 Prediction markets in politics ...... 25 4.1.1 The Iowa Electronic Markets (IEM) ...... 25 4.1.2 European election prediction markets - lessons from Norway ...... 32 4.1.3 The rest of Europe ...... 35 4.1.4 The PollyVote approach – The example from Germany ...... 39 4.2 Prediction markets in sports ...... 43 4.2.1 Sports betting markets with the bookmaker ...... 44 4.2.2 Sports betting markets without the bookmaker ...... 48 4.3 Corporate prediction markets ...... 52 4.3.1 Prediction markets at Google ...... 53 4.3.2 Google’s prediction markets vs. other corporate prediction markets...... 59 4.3.3 Corporate prediction markets in Austria: Siemens Austria ...... 65 4.3.4 Corporate prediction markets in Austria: Mobile phone companies ...... 70 4.4 Prediction markets as an idea generation tool ...... 72 4.4.1 Empirical studies ...... 76 4.4.2 The Tech Buzz Game ...... 79 4.4.3 Customers involvement in idea prediction markets ...... 82 4.5 Other utilization possibilities of prediction markets ...... 86 4.5.1 Negotiations ...... 87 4.5.2 Infectious diseases ...... 89 4.5.3 Scientific progress ...... 92 4.6 Legality of prediction markets ...... 95 4.6.1 Gambling activities ...... 96 4.6.2 Futures markets ...... 97 4.6.3 Lex specialis ...... 99 5 Conclusio ...... 101 5.1 Critical assessment ...... 102 References ...... 103 Internet sources ...... 109

I Table of figures

Figure 1: Classification of prediction markets research material ...... 4 Figure 2: Cumulative abnormal returns before takeover attempts ...... 9 Figure 3: Prediction markets in direct comparison with election polls ...... 29 Figure 4: Iowa Electronic Markets and poll accuracy in 2008 presidential race ...... 29 Figure 5: The price function of the logarithmic market scoring rule in the 2009 Norwegian election prediction market ...... 33 Figure 6: Actual versus predicted outcomes in European prediction markets ...... 37 Figure 7: Absolute percentage errors of predictions under 50%...... 37 Figure 8: Absolute percentage errors of predictions above 50% ...... 38 Figure 9: Search frequencies on Google ...... 51 Figure 10: Price-Payoff relationship of two and five-outcome Google prediction markets ....56 Figure 11: Payoff rules of the securities in the “Verzug” market ...... 67 Figure 12: Trades at a specific time of the day ...... 69 Figure 13: Idea markets concept ...... 75 Figure 14: From the Fuzzy Front End until the commercial exploitation ...... 79 Figure 15: Stages of the innovation process ...... 83 Figure 16: Evaluation of the hypothesis process ...... 94

II List of tables

Table 1: Contract types in prediction markets ...... 13 Table 2: Prediction markets trading mechanisms ...... 17 Table 3: Synthetic bids & asks in the Iowa Electronic Markets ...... 21 Table 4: Types of prediction markets ...... 23 Table 5: Overview of prediction markets contracts ...... 36 Table 6: Accuracy of the PollyVote component methods in Germany 2013 ...... 42 Table 7: Win rate of the first pregame trade ...... 49 Table 8: Contract win rates ...... 50 Table 9: Prediction markets at Google ...... 54 Table 10: Optimistic bias in the Google prediction markets ...... 58 Table 11: Direct comparison of the prediction markets in all three companies - summary statistics ...... 60 Table 12: Prediction markets and experts in direct comparison ...... 64

III List of Abbreviations

ATS : ……………………………………………………………………... .. Austrian Schilling BMDW : ………………………………. ... Federal Ministry for Digital and Economic Affairs CDA :…………………………………………………………... .. Continuous Double Auction CDAwMM : ……………………………...... Continuous Double Auction with Market Maker CEA : ……………………………………………………….. . The Commodity Exchange Act CEO : ………………………………………………………………. .. Chief Executive Officer CER : ……………………………………….….. ... Certified Emissions Reductions Certificate CFTC : ……………………………………….. .. Commodity Trading Exchange Commission CH : …………………………………………………………………………….. ... Switzerland DARPA : …………………………………… ... Defence Advanced Research Projects Agency DE : ……………………………………………………………………………….. ... Germany DFN : ………………………………… ... German National Research and Education Network DPM : ………………………………………………………………… .. Dynamic Pari-Mutuel EMH : …………………………………………………… .... The Efficient Market Hypothesis FMA : ………………………………………………………...... Financial Market Supervision F-test : …………………………...... Snedecor & Cochran Test for Equality of Two Variances IAM : ………………………………………………… .. Information Aggregation Mechanism IBM : …………………………………… ... The International Business Machines Corporation IEM : …………………………………………………………. .. The Iowa Electronic Markets IGM : …………………………………………………………….. .. Interactive Group Method LMSR : ……………………………………………... .. The Logarithmic Market Scoring Rule MiFID : ………………………………………...... Markets in Financial Instruments Directive MOV : …………………………………………………………………… ... Margin of Victory MSR : …………………………………………………………………. ... Market Scoring Rule MTA : …………………………………………………………… ... Milestone Trend Analysis NBA : ……………………………………………………...... National Basketball Association NCAA : ……………………………………………...... National College Athletic Association NFL : …………………………………………………………….. ... National Football League NOK : …………………………………………………………………….. ... Norwegian Krone OKR : ………………………………………………………….. ... Objectives and Key Results P-value : …………………………………………………………………… .... Pearson's Value SUM : ………………………………………………………………………...... Sum of Scores SUR : ……………………………………………………...... Seemingly Unrelated Regression USA : ………………………………………………………...... The United States of America

IV 1 Introduction

Uncertainty is a well-known phenomenon and an integral part of the finance world. Uncertainty simultaneously represents both the main driving force and the notorious stumbling hurdle of today’s financial services as we know them. With the purpose of reducing the effects of uncertainty, or perhaps more correctly with the purpose of forecasting uncertain future events, prediction markets came into existence.

There is still no universally accepted definition of prediction markets, neither between academic researchers nor between practitioners. The most suitable definitions tend to be: “Prediction markets are special case of asset markets where the value of the traded asset is contingent upon the outcome of some uncertain event at or before some pre-specified point in time […]”1 and “Prediction markets are defined as markets that are designed and run for the primary purpose of mining and aggregating information scattered among traders and subsequently using this information in the form of market values in order to make predictions about specific future events”2. There is also no globally adopted descriptor of the concept of prediction markets. The current literature uses the following synonyms: information markets, decision markets, virtual markets, idea futures.

The driving idea behind prediction markets is the aggregation of information from market participants, followed by the expression of those aggregations in the form of market prices. Information aggregation is achieved through natural competition taking place in every marketplace. It is handy at this point to elucidate on the prime distinction between financial markets and prediction markets. The aim of the prediction market is a forecast, the aims of the financial market are the facilitation of trading, the obtainment of financial resources and the achievement of positive returns.

The main motive behind this master thesis is undoubtedly the massive potential of prediction markets as a scientific field. Prediction markets are an exciting area for conducting research, as they incorporate some unique, unusual qualities together with relatively short history. Prediction markets as such represent the only mechanism up to date, whose only goal is to make use of dispersed knowledge among various individuals in the form of prediction. Robin Hanson and some great minds who took part in the creation of the Ethereum platform3 even argue that

1 Deck/Porter (2013, p. 2) 2 Berg/Rietz (2003) 3 https://www.ethereum.org 1 prediction markets could eventually find themselves at the heart of some new, advanced social- political system.

Following previous academic work of some authors4 and ever-growing interest in the concept of prediction markets, this thesis aims at adding continuity and unification into prediction markets terminology and providing the newest insights concerning the general development of the prediction markets concept. Forecasting errors made during the last global financial crisis made explicit how traditional forecasting models based on time series are anything but perfect. Better risk management and forecasting models are to be found. The main goal of this thesis is to analyze the recent findings and the newest application fields concerning prediction markets.

Sports events, political elections, project management, product development, disease, wars, and geopolitical crisis forecasts are just some of the areas and processes that could be more precisely predicted, and better managed with the use of prediction-markets-based mechanisms. Even social media usage, social welfare, and public policies could potentially be improved with the use of prediction markets. Prediction markets could eventually offer something, none of the current political systems can – decentralization. Decentralization is continuously being neglected and marginalized by current decision makers. Theoretical democracy in the form invented by ancient Greek wisdom does not seem to be able to produce the wanted results. Britain’s vote to leave the European Union showed that 48.11 percent out of the overall turnout of 72.21 percent voted to remain in the European Union. The post-referendum survey of people who did not vote showed that 39.1 percent would have voted Remain and another 32.2 percent of the respondents ‘did not know’ how they would have voted had they have gone to their local polling station. The recent presidential elections in the USA exposed similar flaws of the prevailing electoral system. Even though Hillary Clinton received a total of 2.8 million or 2.1 percent more votes nationwide, Donald Trump won the elections. Electrical appliances and especially cellular devices are being designed and produced by neglecting the underlying customer needs56, and the financial system is already vague enough for an average citizen to understand it.

The aim of modern society should be to provide people with fairness, clarity, and participation opportunities. Crypto-currencies and prediction markets are the exact results of unavoidable technological development, but they are also a product of the above-stated people`s needs and

4 Tziralis/Tatsiopoulos (2007), Zhao/WagnerChen (2008) 5 Samsung’s battery scandal (2016) 6 Apple’s acknowledgment of deliberate slowing down of older iPhone models (2017) 2 desires. Capitalism brought much good to our civilization, but it seems that it had reached its pinnacle. According to Milton Friedman and his “Free Market Economy,” capitalism needs freedom, and it never came into existence in order to be able to work under strict market control and different monetary manipulations. National economies are becoming more mature and emerging supremacy of outsourcing and money markets has led to a state in which growth in GDP of any particular country seems to be possible only at the expense of GDP growth of another one. A realistic assessment by Carnegie Endowment for International Peace suggests that if the current risks in the form climate change, geopolitical breakdown, financial crisis, and protectionism are to be contained, China’s economy will be almost twice the size of the USA economy in purchasing power parity terms. India will be the world’s third largest economy, and no European country will be among the top eight largest economies. Our society needs something new. Artificial intelligence and machine learning are impressive evolution products, but they also bring huge question marks with themselves. The solution could be a mechanism based on prediction markets. The ability to use the knowledge scattered around in all of us could be equally impressive with the additional benefit of virtually none negative consequences. It could be beneficial to our world in many different ways, and maybe subtly remind us in that it is not all about profit. The wisdom of the crowd can be a potent tool indeed. We need to learn how to utilize it. 1.1 Research Methodology

The master thesis is a result of an extended scientific literature review, with the focus on prediction markets. Following the primary goal of the thesis resulted in an attempt to compile and methodically examine all the latest academic efforts related to prediction markets. Scientific journal articles, working papers, and book chapters were used as main sources of academic knowledge. The relevant literature search and data collection were conducted with the help of academic libraries7 and online platforms for publishing and exchanging scientific work8, mostly with the use of the World Wide Web. Such approach expectedly resulted in the Boolean model of information retrieval (BIR)9 concerning prediction markets scientific field. All the relevant sources found had been in the end classified as shown by Figure1, following the same classification method which was used by Tziralis & Tatsiopoulos (2007a). As already stated in the introduction of the thesis, the use of such a classification method was chosen

7 http://ub.uni-graz.at/, http://www.zbw.eu/ 8 https://www.researchgate.net/, https://scholar.google.at/ 9 Lancaster/Fayen (1973) 3 because it represents a plausible and reasonable classification solution, and in order to add some continuity and clarification to prediction markets scientific field.

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0 Description Theoretical work Applications Law & Policy

Figure 1: Classification of prediction markets research material

Description category:

The description category contains the following subcategories:

o Introduction – This subcategory contains mostly short, rudimental texts about the basics of prediction markets and is often a subsidiary topic of the publication. o General description – This subcategory consists of texts that go into every detail about prediction markets, including analyses of various prediction markets aspects. o Open problems – This subcategory tries to emphasize issues that are yet to be addressed in an appropriate, satisfactory way by academia and scientific literature. o Other descriptive issues – This subcategory deals with taxonomy developments, potential use in education and governance, and other descriptive issues.

Theoretical work category:

The theoretical work category includes the following subcategories:

o Market modeling – This subcategory contains various texts dealing with prediction markets modeling, framework design, and framework analysis. o Information aggregation convergence and equilibrium – This subcategory contains various papers that discuss and outline the convergence and equilibrium properties of the information aggregation process taking place in prediction markets.

4 o Other theoretical issues – This subcategory includes works on other theoretical considerations and issues that could not be assigned to the previous categories such as the interpretation of prices reached in prediction markets.

Applications category:

The applications category consists of the following subcategories:

o Experiments – This subcategory is comprised of articles illustrating various conducted experiments and the resulting findings. o Iowa Electronic Markets – This subcategory is focused solely on the description and analysis of the most famous prediction market up to date, the Iowa Electronic Markets. o Other political markets – This subcategory covers scientific articles presenting all the political stock markets around the world except Iowa Electronic Markets. Such markets were recorded in Austria, Denmark, Germany, Norway, Sweden, Taiwan, USA, and many others. o Markets on sports events – This subcategory includes prediction markets applications related to sports and findings resulting from these. This subcategory also includes real- money and play-money comparisons. o Other applications – This subcategory contains articles dealing with other application fields of prediction markets including business, entertainment, web games, public health, education.

Law and Policy category:

The law and policy category of prediction markets literature is up until now the least disseminated one. It is also since prediction markets as a mechanism still find itself at the beginning of development and potential usage. The category includes:

o Legality and regulation – This subcategory includes papers indicating potential aspects and directions of prediction markets legal regulation. o Public policy and decision making – This subcategory consists of articles which address prediction markets potential in improving public decision making and policy analysis. o The Policy Analysis Market – This subcategory represents the entirety of prediction markets applications that was implemented as support for sensitive geopolitical issues, the most common ones being international affairs, terrorism, and wars. o Other law and policy issues – This subcategory covers other law and policy aspects of prediction markets.

5 1.2 Relevance of the topic & Research question

As the introduction part of the master thesis already hinted, the aim of the whole academia working on prediction markets is to determine, which fields are suitable for prediction markets implementation and how to simplify their implementation all the way to the level of routine.

Potential breakthroughs in the field of prediction markets could enormously improve the role of every citizen concerning corporate governance, public governance, and decision making. People take part regularly in sport betting exchanges and political elections, but there is a great distance between information gathering and actual decision making in such mechanisms. That is why people should be able to predominantly predict things which have a strong influence on their lives. Prediction markets mechanism could solve such problems by offering solutions with a high level of interactive connectivity.

There is also a socio-psychological aspect of prediction markets. Brain processes, human nature, and people mental dimensions strongly influence our decision making. They are yet to be understood by science. Prediction markets offer in this respect a framework which could help draw significant conclusions from information gathered on human behavior in specific contexts.

This master thesis should serve as a theoretical foundation for further scientific research in the field of prediction markets. It should also provide some new personal insights and conclusions, which could one day prove to be an initial incentive for eventually some decisive breakthrough in the corresponding field.

The main focus of the master thesis and the focal point of the author’s interest are prediction markets application fields. The thesis will gradually go through the most interesting out of recorded prediction markets applications, without neglecting the importance of topicality of every scientific source used in the process. Such a method should provide this master thesis with the necessary credibility, reliability, and hopefully with some scientific weight.

The research question is framed as follows:

 What are the most significant prediction markets application fields, and which human behavior aspects become apparent with the use of such forecasting mechanism?

6 1.3 Motivation

Despite growing public interest, the prediction markets scientific field finds itself in the state of infancy. Only as a constant subject of relevant scientific interest, prediction markets mechanism will be able to evolve and reach the level of desired reliability gradually. Even though the field possesses a certain amount of embodied proneness to various inefficiencies and biases, results from conducted studies show an excellent forecasting potential. That enormous forecasting potential, together with some other revolutionary prospects, represent the primary motivation for drafting the master thesis.

7 2 Theoretical backgrounds

It is a rarely mentioned fact that prediction markets history goes a long way back, all the way up to the late 19푡ℎ century. Betting markets in New York were already using a sort of market mechanism to bet on the results of political elections. Most of the participants were the Wall Street traders. 10

The Iowa Electronic Markets, introduced by the faculty of Iowa in 1988, emerged as the first long-term run prediction markets considered by academia reliable enough for an educational application.11 After that, universities in other countries started running their prediction markets focusing on their home countries’ elections. Austrian Electronic Market, run by the Vienna University of Technology, and Election Stock Market, run by the University of British Columbia, are the best known. By examining these, as well as various web-based prediction markets cases, following insights about historical backgrounds, and mechanics of prediction markets were able to be gained.

There are three fundamental theoretical postulates which come under scrutiny when talking about theoretical backgrounds of predictions markets. The introduction part of the master thesis already indicated how one of the most important goals of the financial world was to fight uncertainty and predict the future. The development of computers made it possible for researches to conduct time series analysis mostly based on the behavior of stock market prices over time. One of the first such analysis conducted was the one of Maurice Kendall in 1953. He hoped that a detailed analysis of past boom and negative periods would enable people to precisely predict future ones. The results were surprising since no predictable patterns in stock prices movements could be identified. At the time, the interpretation of economists was that the markets had an erratic nature and that irrational forces drove them. It soon became apparent that this market “irrationality” actually represents a well-functioning mechanism, on the contrary. This finding paved the way for what is today known as a random walk, which represents the reasoning that in well-working markets stock price changes should be unpredictable and their returns accordingly independent and identically distributed.

Probably unconsciously, our society created a very efficient information aggregation mechanism in the form of the market price system. The Efficient Markets Hypothesis was developed and established by Professor Eugene Fama, and it is considered to be the cornerstone

10 Cf. Rhode/Strumpf (2004), qtd. in Lüdtke (2015) 11 https://tippie.biz.uiowa.edu/iem/media/summary.html, [State:22.1.2018] 8 of modern financial theory. The notion behind it is that prices reflect all the available information and move only as a reaction to new information. Current academia distinguishes between three levels of EMH:

o Weak form

Stock prices always reflect all the past available information. Since such information is usually easily obtainable, none of the usual trend analyses will provide extra-return results.

o Semi-strong form Stock prices reflect all publicly available information regarding one stock. In addition to past prices, such information includes earnings forecasts, accounting practices, and balance sheet structure. o Strong form

Stock prices reflect all available information relevant to one company including past information, current information, and insiders. This form of EMH can be put into question since there are numerous cases of company officers using insider information in order to gain extra profit. Following Figure 2. illustrates the responsiveness of stock prices to the inflow of new information in the efficient market.

Figure 2: Cumulative abnormal returns before takeover attempts12

It has to be noted that randomness in price changes and irrationality of price levels are not to be confused, even though both terms represent an integral part of EMH. If the price-levels are rational, then only new information can cause them to change. Random walk price patterns are

12 Bodie/Kane/Marcus (2014) 9 to be understood then as the natural result of prices that always incorporate all available information. However, many recorded market-anomalies such as small firm in January, neglected firm, post-earnings-announcement drift, and others cast doubt over the validity of the EMH.

Friedrich August Hayek was one of the most influential and famous economists of the twentieth century. He won the Nobel prize in 1974 as his work made fundamental contributions to the fields of economics, political theory, and psychology. He was an advocate of what is in today’s science world known as the Austrian school of economics. Friedrich Hayek and John Maynard Keynes were developing their models and theories virtually at the same time, and the differences resulting from this rivalry had a strong influence on both of them. In a similar fashion to Adam Smith, Friedrich Hayek thought that the coordination of people’s actions in one economy represents a fundamental issue. He was arguing that the price system of free markets does a great job of fulfilling such a role of coordination. Happening long before the EMH, such thoughts resulted in what is today known as the Friedrich Hayek price mechanism. Friedrich Hayek argued that practically every individual has some advantage over all others because he or she possesses unique information, of which beneficial use can be made. The mechanism of market prices acts thus to coordinate the separate actions of different people, much in the way as subjective values help every individual to coordinate different parts of his plan.

The Samuelson effect represents the third principal part of prediction markets theoretical background mosaic. Samuelson effect was first introduced in 1965 by Paul Samuelson. This scientific work was one of the reasons Paul Samuelson won the Nobel prize in 1970. The Samuelson effect represents the increase in movement of futures-contracts prices approaching the settlement date. The Samuelson effect holds therefore as a plausible explanation for the enlargement in the sensitivity of futures prices when faced with the inflow of new information. However, numerous empirical studies had been examining the Samuelson effect under a variety of circumstances since the work of Rutledge in 1976 and had produced mixed results. 13 14 15 2.1 Judgmental biases as an integral part of economic behavior

The traditional economic theory has always assumed economic and rational market participants who are in most cases aware of all the relevant aspects of their surroundings, and who always

13 Cf. Rutledge (1976), Samuelson (1976), qtd. in Chen/Zheng (2014) 14 Cf. Anderson/Danthine (1983), qtd. in Chen/Zheng (2014) 15 Cf. Hong (2000), qtd. in Chen/Zheng (2014) 10 strive to maximize the utility bearing in mind their constant personal system of preferences. Jeremy Bentham and John Stuart Mill were known as architects of the economic man theory. Economic science was always preoccupied with normative macroeconomics, often neglecting the critical role of behavioral-judgmental factors. The development in economic and business firms’ practices since the time of Adam Smith and his “Theory of Moral Sentiments” showed, however, that such consideration does not represent a stable foundation for further economic modeling.16 In order for a forecast to be produced, the processing of available information is fundamental. The processing can be done with the help of either statistical models or judgmental methods. In various everyday situations, agents generate predictions based on some combination of these two approaches.17

In his groundbreaking work from 1955 “A Behavioral Model of Rational Choice,” Herbert A. Simon introduced the well-known concept of bounded rationality. This concept provided an initial framework for inductive research of economic behavior. It pointed out that many economic models rest upon a theoretical basis and that assumptions on which they were built upon deviate from the real-world conditions. Later on, it has been determined that empirical results depart from economic man assumption in several domains, with the main one being the context of uncertainty. Such shortcomings are most prominently represented by the research work of psychologists Daniel Kahneman & Amos Tversky, in the paper “Judgements under Uncertainty: Heuristics and Biases,” (1974).

In today’s finance world it is more than clear that judgmental behavior of economic subjects is susceptible to various types of biases. Prediction markets offer a transparent and valuable tool for the research of economic behavior. Considering the potential, growing applicability and ever-growing interest tied to prediction markets it would be safe to say that prediction markets contain enough complexity to cover a lot of potential circumstances and that they are parsimonious enough to explore and detect various behavioral anomalies.

16 Simon (1955) 17 Reade (2014) 11 3 Mechanics of prediction markets

This section of the master thesis should offer a systematic overview of until now documented types of prediction markets. Along the way, prediction markets organization and framework, main properties, and consistently used terminology will be described methodically. Prediction markets are fundamentally different from financial markets. Financial markets’ goal is to provide a trading platform for all willing participants. The traders aim exclusively at a profit whereas companies listed on financial markets target raising of financial capital and future profit as their primary objectives. Prediction markets on the other side came into existence with one primary purpose – accurate forecast. Profit, in the context of prediction markets, can be considered as a wanted, natural consequence of such accurate forecasts, especially in the case of corporate prediction markets. It is however intrinsically different when compared with a profit on financial markets since it is only one of the main goals besides information revelation and taming of insecurity. The rest of the master thesis will refer to a generally accepted model of prediction markets. The model implies that there is a certain number of stocks in each prediction market, with multiple agents who multi-round trade these stocks. All that said, the mechanics of predictions markets strongly resemble one of the financial markets. They are being exemplified as follows. 3.1 Contract design

Every prediction market is strongly tied to an outcome of some specific future event. In order to elicit predictions of participants, participants of every prediction market are incentivized to engage in a trading activity. The only commodity being traded in prediction markets is a contract.

The design of these contracts plays a crucial role in 2 interconnected respects:

o The design arouses a market`s expectation of various parameters. o The design determines how the payoff of the trading activity is contingent on the outcome of some particular event.

Even though the design of prediction markets contracts looks like a straightforward task, at first sight, the design has to balance between interest and contractibility continually. Based on design characteristics, there are three basic types of contracts traded in prediction markets. Following Table 1 provides a concise overview.

12 Contract Example Details Parameter

Contract price €p. Event y: Obama wins Probability that event Winner-take-all Pays €1 only if event y the elections y occurs, p(y) occurs.

For every percentage point of the vote won Mean value of Index Contract pays € y. by Obama, contract outcome y: E[y]. pays €1

Contract price €1. Contract pays double Pays €2 if y > y*. Pays the stake if Obama Spread €0 otherwise. Bid Median value of y. wins more than y*% of according to the value the vote. of y*.

Table 1: Contract types in prediction markets18

1. Winner-take-all contracts are contracts which cost a certain amount $P and pay off, for example, $1 if and only if that event occurs. The price of the contract $P represents, in this case, the market’s expectation of the probability that an event will occur. 2. When talking about index contracts, variable or continuous types of contract payment come into focus. Dependent on the value which can rise and fall, such as the percentage of the vote won by an election candidate, the amount that contract pays changes. The price of such a contract represents the outcome mean value, assigned by the market. 3. Spread contracts usually come to mind when talking about sport, although they can also be used in political elections. Traders bid in a process on a specific cutoff, which quantitatively speaking represents nothing else than a spread. This spread value in return determines, whether a particular event takes place or not. Familiar examples are a percentage of the favorite vote won and spread betting in various sports competitions. If and only if a political candidate or a sports team win by at least a spread value does the contract owner get rewarded. When a spread contract is combined with an even-money contract, the outcome can yield the market’s expectation of the median outcome. It is because such a combination represents a fair bet only in the case, where the payoff is as likely to happen as not.

18 Wolfers/Zitzewitz (2004) 13 It can be stated that winner-take-all contracts look like the most promising ones when talking about their application. They are easy to interpret, and this could offer additional liquidity to such markets. The most important thing ultimately is who wins, in the context of politics or sports. Whether it is with 2 or 20 percentage points margin plays less of a role. It has to be noted that the price of winner-take-all securities (contracts) is primarily a state price which will equal an estimate of the probability that an event will occur under the assumption of risk neutrality.19

It is safe to say that the formulation of such contracts has to be decisive for the attractiveness and efficiency of prediction markets. “For a prediction market to work well, contracts must be clear, easily understood and easily adjudicated.”20 However, this search for clarity and simplicity can sometimes turn out to be extremely complex, as some examples from prediction markets implementation already showed. Internal prediction market of Siemens showed that there might be essential contract elements which are not exclusively under the company’s jurisdiction. In this well-known case, the client changed the deadline for the delivery of software.21

The design of contracts has, therefore, a massive influence on the type of the metric being forecasted by the market. Prediction markets could as well be used to uncover an entire probability distribution of the market’s expectations or to evaluate an uncertainty about those expectations. Non-linear payoff contracts can, for instance, be used to reveal a market expectation of standard deviation or even higher-order moments of the distribution. 3.2 Trading mechanism

In the same way as within in financial markets, trading mechanism represents the focal point of every prediction market. It determines how the trade itself is being conducted and through that fact indirectly, how reliable and efficient a prediction market is. Prediction markets represent an instrument which is still evolving. Trading mechanisms, as their integral part, follow the same path of evolution as well. The following part of the master thesis will present and elaborate the products of that evolution in the form of trading mechanisms. Even though this work tries to incorporate only the newest scientific articles and breakthroughs on the field of prediction markets, the paper called “A dynamic pari-mutuel market for hedging, wagering, and information aggregation” from the beginning of this century lies at the heart of this

19 Wolfers/Zitzewitz (2004) 20 Wolfers/Zitzewitz (2004) 21 Cf. Ortner (1998), qtd. in Lüdtke (2015) 14 section.22 The paper summarizes a wide variety of financial trading mechanisms nicely and adds to the relevant state of the science of that period with a newly developed concept of its own.

The Continuous Double Auction or (CDA) represents a trading mechanism which is dominant in circles of finance and investing. Continuous double auction mechanism tries continuously to match buy and sell orders. Every time a bid order equals or is higher than a sell order, a transaction is being carried out. If the highest bid order is, however, lower than the lowest ask order, no transaction takes place. The downside of such design immediately comes into play. Buyers can buy only as much as sellers are willing to sell, and an illiquidity state in which no trades take place is a constant threat. There always has to be an available counterparty on the other side of the market, in order for a trade to take place. The partly philosophical question, however, remains: “Is the state of constant trading a right one to be pursued by all other trading mechanisms?” The continuous double auction mechanism represents, in any case, a simple, familiar, and a widely used trading mechanism which forces bid and ask prices to change as soon as new information arrives. Since the market institution, or an auctioneer, matches only existing orders, no risk has been taken by the institution.

The Continuous Double Auction with Market Maker or (CDAwMM) represents a modification mechanism of the classic continuous double auction, which solves the problem of a constant illiquidity threat. The market maker can be introduced in the form of an automated algorithm or a form of a person. The critical thing to notice here is that no matter which form of the market maker has been established, the market maker becomes a part of the whole trading system. It means that such a system improves liquidity when compared with the standard CDA mechanism, but at a price of apparent risk. Instead of only matching orders, the whole market institution takes a risk. Such a structure could potentially, dependent on future outcomes, result in considerable losses. The market maker can also take a form of a bookmaker or oddsmaker within the continuous double auction mechanism. Such form is usually being implemented in wagering markets. The main difference between these two versions of the CDA mechanism with market maker lies in the fact that a market maker in the form of bookmaker operates via “take it or leave it” modus. It means that bettors can place their stakes solely at prices defined by the oddsmaker. However, when talking about such a modus operandi, it is necessary to mention that the same bid-ask spread defined by bookmaker represents his source of profit, as well as his main exposure to risk.

22 Pennock (2004) 15 The Dynamic Pari-Mutuel or (DPM) can be thought of as a combination of pari-mutuel mechanism and continuous double auction mechanism. It is a pari-mutuel because it continually redistributes money between traders or more accurately, from losers to winners. Continuous double auction feature comes in the foreground at the selling side of the market. In order for such a trading mechanism to work, a small pre-determined subsidy is required. Such subsidy could be obtained from market participants or the outside. Exposure to the risk of a market institution in this way always strives to be zero. Traders can purchase shares of any outcome at any time, and this represents the feature of constant liquidity and information incorporation. The market institution changes the prices according to the current state of wagering, without any risk. The more money is placed on one outcome, the bigger its price is. The prices are computed automatically using the price function. This mechanism resembles the so-called one- way liquidity since all selling orders are conducted via the CDA mechanism.

The Market Scoring Rule or (MSR) is a trading mechanism developed by a Robin Hanson, a scientist widely regarded as one of the leading proponents of prediction markets. The market scoring rule represents a trading mechanism that has been developed relatively recently. It is a trading mechanism that maintains a probability distribution over all events offered and thus can act, similarly to the dynamic pari-mutuel, as an automated market maker. If a specific trader believes that the probability distribution is wrong, he can change any part of that distribution. In order to do so, a trader has to accept a lottery ticket with an underlying scoring rule and also to pay off the most recent trader, according to the underlying scoring rule, who changed the distribution. This market mechanism maintains in such way the constant liquidity feature. The main difference between the market scoring rule and the dynamic pari-mutuel mechanism represent the fact that the market scoring rule mechanism has to be subsidized with a variable amount of money. The patron`s final loss is therefore also variable and incorporates a certain degree of risk, but the maximum loss is bound. The market scoring rule is therefore not a pari- mutuel in nature. Another significant difference in comparison with the dynamic pari-mutuel mechanism is the fact that the market scoring rule incorporates a two-sided automated market maker. Robin Hanson showed how such a trading mechanism is very well suited for allowing bets on a combination of different outcomes.

The Credit-based Trading does not represent a trading mechanism per se. It is, however, a potentially important feature of every trading mechanism previously defined. The Credit-based trading was an essential part of prediction markets since they were practically introduced.23 As

23 Cf. Forsythe (1999), qtd. in Lüdtke (2015) 16 the prediction markets domain was searching for more simplicity and broader applicability, such feature was gradually left out. Trading without an initial private capital is a possibility in every financial market developed up to date, but credit trading and short selling are concepts difficult to understand for a trading amateur. The Credit-based trading is a feature which could potentially improve information disclosure, but it could also provide a trading impetus for the wrong people to unreasonably embark on trading. Without the credit-based possibility, portfolios can at worst case end up with the value of 0, while with the feature enabled portfolios could result in negative portfolios. It means that a trading mechanism with the credit-based trading possibility could either have a hugely positive effect, or a detrimental effect on prediction markets efficiency and accuracy. Leaving the credit-based trading out of prediction markets seems to be a rational choice at this point of prediction markets’ evolution. Following Table 2 offers an excellent overview of the trading mechanisms’ characteristics concerning the three most important features of the trading mechanisms:24

o The guaranteed liquidity o The market institution risk o The continuous incorporation of information

It has to be noted, however, that the newest prediction market models add the fourth important feature of prediction markets mechanisms. It is known as the all-agents-express-relevant- information condition. These models state that this is a necessary and sufficient condition for convergence to the direct communication equilibrium in a well-working prediction market framework.25 A market which possesses this fourth feature is being referred to as the simple decision market model.

Incorporation Guaranteed Trading mechanism No risk of liquidity information

CDA ✖ ✔ ✔

CDAwMM ✔ ✖ ✔

DPM ✔ ✔ ✔

MSR ✔ ✖ ✔ Table 2: Prediction markets trading mechanisms26

24 Pennock (2004) 25 Grainger et al. (2015) 26 Own depiction based on Pennock (2004) 17 Even though the dynamic pari-mutuel mechanism exhibits all three relevant properties, it is by no means without any drawbacks. The payoff of all possible future outcomes within the CDA mechanism is fixed at the time of the trade. This is in contrast to the DPM mechanism where the payoff depends on both the price at the time of the share and the final payoff per share when the market closes. It means that the optimization problem of participants displays conspicuous complexity. The second drawback of the DPM mechanism is its one-sided nature. Meaning that the market maker is always ready to accept buy orders, whereas sell orders have to be conducted between traders. Also, concerning the guaranteed liquidity property. The importance of market liquidity is being emphasized in almost every business school and finance class today. It is beyond any doubt a fundamental feature of every conceivable trading platform. Even more so in the context of prediction markets, since every trade made represents practically a forecast of the underlying event.

However, the non-liquidity state of a market could be rationally explained by both the “no trade theorem,” and the presence of the selection bias. In the context of prediction markets such state could potentially indicate two possibilities:

o If the initial asset allocation is commonly known to be efficient, the inflow of new information cannot stimulate trade as long as participants similarly interpret information. o An example of the selection bias issue would be that participants have relevant information at their disposal but are not willing to share them even under the veil of anonymity.

It cannot be expected that all of the relevant information is allocated among different individuals. It is entirely credible that only one individual possesses the majority of the relevant knowledge about one, nonpublic matter. If and how would it be possible to motivate such individuals to put their money where their mouth is, represents a complex issue itself, which has to be addressed by a separate scientific project. 3.3 Real-money vs. play-money dilemma

Since the prediction markets field became part of the scientific focus, the dilemma has been known as an intriguing question, although somewhat little scientific work has been devoted to its solution. The real-money vs. play-money dilemma belongs to the choice-of-incentives matter as well as to the general market-design decision. It is a delicate question which demands a very comprehensive, in detail approach. The market initiator should always, therefore, aim at the alignment of incentives for participation and the ones for truthful information revelation. 18 Such a goal demands a very balanced approach since the higher market participation does not necessarily mean better market performance, the statement also addressed in the previous section of the master thesis. Up until very recently, the academic community witnessed only three significant studies which tried to reveal whether real-money markets or play-money markets prevail in the context of a prediction markets accuracy.

The first study27 compared the accuracy of real-money and play-money prediction markets in the application field of sports. 2003 NFL season revealed that both types of prediction markets exhibit an approximately equal accuracy. The study also revealed that both types of prediction markets were significantly more accurate than all but a dozen of 3000 people taking part in an online contest, meaning that both prediction markets significantly outperformed the average prediction of the online contest as well. The second study28 was conducted as a part of the same prediction markets, but over a broader range of topics and over a different time frame. The study came to the same conclusion with the addition of one critical remark. The play-money prediction market performance was strongly contingent on participation. The play-money prediction market showed equally accurate results as the real-money prediction market, however, only in the case of high participation. A hypothesis that marginal traders find enough incentives for participation only in real-money markets could explain such results. However, such a hypothesis is something which has to be further looked into in order to get more clear results. Real-money prediction markets have to be a subject to strict regulation. It is because such markets naturally possess more risk. Individuals with substantially more financial resources could prove to have significantly more influence on prediction markets performance. Such prediction markets could potentially become a place where various sorts of risk could be hedged away. Participants would consequently lose money as well, which would immediately influence the liquidity of such markets. The third study29 dealing with this never-ending prediction markets dilemma compared predictions of box office results from the Hollywood Stock Exchange with predictions from the Iowa Electronic Markets for the same nineteen movies. Contrary to the second relevant study, the third study found no real evidence for the significant difference in accuracy between the real-money and play-money markets. The year 2007 brought one more study worth mentioning related to this matter.30 Interestingly, authors found that rank tournaments led to better predictions than individual depot contests. This

27 Servan‐Schreiber et al. (2004) 28 Rosenbloom/Notz (2006) 29 Gruca/Berg/Cipriano (2008) 30 Luckner/Weinhardt (2007) 19 implied that the real-money versus play-money dilemma does not play a crucial role in the accuracy of the prediction market mechanism.

Play-money prediction markets probably offer much more flexibility. They are perceived as an enjoyable experience where participants take part only because of the thrill of pitting judgments against the ones of other participants. The only way to increase one’s wealth in play money markets is to have a history of good results. According to that, a potential superiority of play- money prediction markets when measured against real-money prediction markets could be expected over time, as the play wealth concentrates between informed traders. 3.4 Manipulation of prediction markets

Interactive group methods are common tools designed in order to improve exchange and aggregation of information. The best-known IGMs are face-to-face meetings, Delphi techniques, and prediction markets.31 Each of these methods was a result of the evolution of business practice, with its familiar pros and cons. This section of the master thesis will not go into detail about manipulation proneness of every information gathering mechanism mentioned. It will instead devote its content to the primary mechanism of this paper, namely, prediction markets. Prediction markets are a mechanism which in many ways combines the features of all aforementioned information-gathering mechanisms, including financial markets. Besides the matter of the end product precision, examining, whether or not the susceptibility to manipulation in the process of producing that end product exists, remains equally important. Prediction markets prices represent nothing else but forecasts. If some price manipulation is continuously being used, this may affect the overall performance of a particular prediction market. Now, how precisely can be manipulation defined? The most appropriate definition can be found in the act of the Securities and Exchange Commission: “To effect, alone or with 1 or more other persons, a series of transactions in any security registered on a national securities exchange, any security not so registered, or in connection with any security-based swap or security-based swap agreement with respect to such security creating actual or apparent active trading in such security, or raising or depressing the price of such security, for the purpose of inducing the purchase or sale of such security by others”.32

Such a deceptive strategy, however, does not seem to transmit efficiently into the prediction market field. One could easily imagine the motives behind such illegal strategy within the

31 Graefe (2009), Lüdtke (2015) 32 https://www.law.cornell.edu/uscode/text/15/78i, [State:23.11.2017] 20 prediction markets, but given the scope and necessary restrictions of every prediction market, such strategy would be almost impossible to pull off. In order to elaborately explain such view, analysis of the Iowa Electronic Markets political prediction market will follow.33 The Iowa Electronic Markets represent the peak of prediction markets applicability and development up until today. This prediction market, run by the University of Iowa, is the most representative in this field since it is being used on a frequent basis. Back to the manipulation issue…there are two familiar design features of IEM which make the manipulation of this prediction market a tall order:

o Unit portfolio o Account limit

All interested participants worldwide can trade in political markets of the IEM. Other markets, such as the earnings and returns markets, are open only to academic traders. In addition to that, every individual account is restricted to the maximum of $500. This restriction, of course, limits the size and power of individual traders relative to the market and undermines the possibility of manipulation. The unit portfolio feature of the IEM makes the manipulation even more difficult. The unit portfolio represents the feature of winner-take-all markets where every contract represents a separate candidate. A contract bundle consists of one unit of each contract listed in a particular market. That would mean that every one of these contracts pays $1 if its candidate wins the elections with the majority of votes. Such a feature allows traders to buy a unit portfolio from the exchange for $1 and sell at the sum of available bids at any time. Traders can also sell the unit portfolio to the market institution for $1 and buy the stocks at the sum of available asks. The important thing here, which serves as additional insurance against potential manipulation attempts, is the possibility of buying or selling of the same positions in contracts in two different ways. One way is the possibility of direct actions with the simple use of available bids and asks. The other way is the so-called synthetic position which will be explained with the help of the following Table 3.

CONTRACT BID ASK SYNTHETIC BID SYNTHETIC ASK DEM12_WTA $0,510 $0,520 $0,510 $0,520 REP12_WTA $0,480 $0,490 $0,480 $0,940 Total $0,990 $1,010 $0,990 $1,010 Table 3: Synthetic bids & asks in the Iowa Electronic Markets 34

33 Berg/Rietz (2014) 34 Berg/Rietz (2014) 21 Mathematically speaking: Synthetic bid position equals 1 - cross ask

Synthetic ask position equals 1 - cross bid

In Table 3 for instance, the DEM12_WTA represents the Obama contract. The Obama contract can be purchased directly at ask price for $0,520. Indirectly or synthetically, the whole unit portfolio can be bought for the amount of $1, and the Romney contract could be sold at the current bid price, resulting in the same net position: $1-$0,48=$0,52. There are of course similar ways of selling both contracts. This mechanism is a straightforward one but provides additional security against the manipulation of prediction markets. If there is a manipulator who wants to raise the Obama price, he could bid up the price and leave for example $0,53 bid in the queue. Such a strategy would immediately create an arbitrage opportunity => every other trader could by a unit portfolio and sell the contracts at the sum of new bids. That would result in the profit of $0,53+$0,48-$1=$0,01. Since the supply of unit portfolios is an infinite one, this would create enormous pressure on the manipulator and demand considerable resources in order to maintain such prices. The unit portfolio system does not make the arbitrage mission impossible but makes it extremely difficult instead. Manipulators cannot just bud up one contract. They also have to sell down the other one resulting in double the resources. Such a combination of the unit portfolio system and account limits makes prediction markets rational aggregate predictors on the one hand, and manipulation attempts a particularly hard and complicated task on the other hand.

At the same time, some scientist found evidence that asset market prices could be manipulated under certain conditions in the short run.35 These experiments contained both informed and uninformed traders. The type of a trader was, however, private information which was not familiar to other participants. When in such a market a robot-trader with a particular, profit- neglecting trading strategy was included, uninformed traders were not able to tell if informed traders did the actions of a robot-trader or not. It firstly led to the manipulation of the uninformed part of the traders. Consequently, this could lead to a transitory effect in the asset prices. The same authors reported, however, that a presence of a robot-trader did not significantly affect the last contract price. There were also some reported practical attempts to manipulate one political IEM through random large orders, and those attempts showed that it was not possible to influence the market on a sustainable basis.36 These researchers concluded

35 Veiga/Vorsatz (2010) 36 Rhode/Strumpf (2008) 22 that they were also not able to find significant evidence that political stock markets could be manipulated beyond short periods of time.

It can be concluded that the manipulation issue of prediction markets mechanism still represents a reasonable concern, especially when considering the prediction markets mechanism as a potential public policy tool. The well-known DARPA Project37 was dropped mostly because of such concerns. Recent studies, however, found no evidence which could justify the manipulation concern. This is nonetheless a matter which demands further, comprehensive research. 3.5 Modes of prediction markets

Opposite to application fields of prediction markets which are continually evolving, modes of prediction markets are determined by two particular, relevant characteristics:

o Purpose o Participants

Table 4 perfectly illustrates this distinct but critical section of the master thesis.

Open Closed Hollywood Prediction Stock Google Exchange Corporate Investment Wall Street Investment Table 4: Types of prediction markets 38

Primarily, the table emphasizes the well-known contrast between prediction markets and financial markets. Prediction markets aim exclusively at forecasting future events, whereas financial markets aim at pricing different assets and the facilitation of their trade. The second, but an equally important message from the table, is about the targeted auditorium of prediction markets. Some prediction markets are open to virtually all willing participants. Such are the Iowa Electronic Markets, Hollywood Stock Exchange, Betfair, and others. However, there are prediction markets which are limited only to certain groups. The best-known examples of closed prediction markets are all within company prediction markets, with Google Prediction Market, Copenhagen Prediction Market, Best Buy’s Tag Trade, as familiar examples. It would

37 Looney (2004) 38 O`Leary (2015) 23 be reasonable to think that prediction markets can only do well while forecasting the outcome of the events which are of great public interest, and in cases where the information about such events is well spread among the general public auditorium. There is, however, strong evidence of a surprising prediction markets precision on the district, micro level.39 Such prediction markets were tested in Australia (local district races), and it was concluded that those prediction markets were able to produce some extremely accurate forecasts. It is impossible to say which prediction markets mode is the most accurate one. However, such a dilemma should fade away over time. It may never be resolved. Moreover, perhaps it even does not have to be. It is much more important to state that prediction markets represent a surprisingly versatile forecasting tool, which can succeed in different ambiances.

39 Wolfers/Leigh (2002) 24 4 Application fields of prediction markets

Prediction markets history goes a long way back. As early as in 1868, prediction markets have been used for betting on the outcomes of presidential elections in the USA. Of course, these early forms of prediction markets did not focus on information aggregation…they were used as the trading vehicle through which individual gains were possible. The resulting prices, however, were often quoted by newspapers and campaign leaders as credible forecasts. In the time span between 1884 and 1940, an analysis of such markets found that these markets exhibited remarkable forecasting abilities long before scientific polling. Such “unintentional” prediction markets peaked in 1916, as spending on electoral betting was twice as large as the total spending of respective election campaigns. Legal restrictions followed, resulting in their abolition eventually.40 The prediction markets field rested in a sort of hibernation until 1988. It was the year in which the University of Iowa established the famous Iowa Electronic Markets for academic purposes. At that point in time, political elections were the predominant application field of prediction markets. Later on, application fields of prediction markets were continually gaining on their diversity. It is why this section of the master thesis represents its core and the most important part. In response to recent growing academic interest, the Journal of Prediction Markets was initiated in 2007. In 2008, the Special Interest Group on Prediction Markets was launched on behalf of the International Institute of Forecasters.41 4.1 Prediction markets in politics

Politics remain the most common application field of prediction markets. Due to the longest deployment history and resulting credibility, the Iowa Electronic Markets serve as the benchmark within this application field. The next section of the master thesis will, therefore, elaborate on the most famous prediction market in the world and its results in the field of politics. 4.1.1 The Iowa Electronic Markets (IEM)

IEM is an Internet-based teaching and research tool developed by the faculty of the University of Iowa in 1988. It allows mainly students to invest real money ($5-$500) and trade in a variety of contracts. Many different markets were a part of IEM with the best-known ones being the political markets. The remaining application fields include economic indicators, earnings, corporate stock price returns, and movie box office receipts. Since its inception in 1988, over

40 Rhode/Strumpf (2004) 41 Graefe (2009) 25 100 universities all over the world have been using the IEM, including some of the largest and best known research-oriented academic institutions such as Harvard, MIT, Northwestern and others. The IEM is being used in a wide variety of university subjects starting from finance, accounting, macroeconomics all the way up to political science. The nature of the markets is such that it offers a unique hands-on interactive environment. There are two common features of the IEM which enable the market to be such a successful educational tool:42

1. The IEM represent first and foremost the forecasting mechanism. As such, it forces its participants to simultaneously take into account many different factors and develop their analytic and creative capabilities. 2. The IEM works as a real-money platform. Since the students and other participants earn real money by trading, the market represents a strong incentive for participants to learn, make accurate predictions, and earn a profit eventually.

The twofold educational-tool statement above represents the additional declaration of intent since the prediction markets field still represents a relatively unregulated area. As such, the market is not regulated by, nor are its operators registered with the Commodity Futures Trading Commission or any other regulatory instance. The Board of Governors, composed of faculty from the Departments of Accounting, Economics, and Finance at the University of Iowa, provides a long-term oversight to the IEM. The IEM is beyond any doubt an advantageous researching mechanism which continually increases the awareness and knowledge of students interested in the fields of finance, business, technology, and politics. It is also an educational tool which can vastly improve the decision-making capabilities of all participants by providing them with some valuable and fascinating insights into the art of forecasting.

4.1.1.1 Trading mechanism

The IEM uses the WebExchange Trading System for the operation of the market, although it was initially set up as a phone call market. The system is working on a platform better known as the continuous double auction (CDA). Once a trader is being logged into the system, he can undertake the following actions:

 Place a limit order  Place a market order  Withdraw an outstanding bid or ask

42 https://tippie.biz.uiowa.edu/iem/media/summary.html, [State:28.11.2017] 26 As soon as the bid and ask orders are submitted, the system places them in Bid & Ask queues. Short selling and buying on margin are not allowed in any of the IEM markets. This means that a trader cannot sell something that he does not own, and he cannot spend more money than he has. Every attempt in doing so results in an infeasible order. Contracts in the IEM are being issued in the form of unit portfolios. A unit portfolio consists of one unit of each of the contracts available in the market and has a price equal to the guaranteed aggregate payoff of this contract set. Such a framework ensures that the IEM neither gains nor loses money by issuing contracts. These contracts are being put in the circulation by traders through the purchase of unit portfolios from the IEM. Traders can also sell their unit portfolios to the IEM anytime. The prices are therefore being determined as the sum of liquidation values of all contracts in the unit portfolio. Due to such mechanics, there will always be an equal number of each contract unit in circulation, at any point in time.

4.1.1.2 Participants

The IEM is strictly operated for research and teaching purposes. Interested participants from all over the world can trade in available election prediction markets. Earnings and returns markets, on the other side, are open only to academic traders. Instructors who wish to use the IEM for their class are only obliged to set up an IEM Class Description before their students can open their IEM trading accounts.

4.1.1.3 Incentives and payment schemes

As already hinted at, the IEM is run as a real-money mechanism, meaning that participants use their funds to buy and sell contracts. Traders, therefore, have the opportunity to make some profit, but they also have to bear the risk of losing own money. All the funds are being deposited to the University of Iowa account. When funds are being withdrawn from a traders account, the University of Iowa accounting department, which is independent of the IEM, writes a check and mails it directly to the last known address of the trader. The IEM is also a subject to university or state audit. Trading in a specific contract will cease on the expiration date as specified in the prospectus of a respective contract. At that time or shortly after, the value of the fundamental can be determined, the liquidation values are being computed, and corresponding amounts will update the cash accounts of the traders. The dividend-adjusted rates

27 of returns are being used in the return markets. The following example, taken from the IEM online site,43 explains the whole procedure:

 Assume that IBM closes at $104.75 on the third Friday of month “m,” closed at $100 of the previous month and paid a $0,25 dividend during the month. The resulting dividend-adjusted return for IBM is: R = ($104,75 + $0,25 - $100)/$100 = 5%.  If the IBM has the highest such return out of Apple, IBM, Microsoft, and the S&P500, the liquidation value of all IBMs contracts will be $1 and all other contracts will be $0.

4.1.1.4 Performance of the Iowa Electronic Markets

Prediction markets earned their current scientific attention with the establishment of the Iowa Electronic Markets. Since the inception year, the results of the IEM were attracting more and more attention until they gradually became a credible scientific forecasting source. Numerous scientific papers dealt exclusively with the IEM, and the majority of them found that the IEM was able to significantly outperform other forecasting mechanisms (pools, expert opinions) on a constant basis. In 1988, the inception year of the IEM, the prediction market as a relatively new prediction tool exhibited astonishing accuracy. On the election eve, the absolute percentage error of vote shares predictions was only 0,2 percentage points, whereas the pools as the only cited forecasting source until that moment exhibited forecasting errors of 2,5 percentage points.44 Since that first year, the Iowa Electronic Markets continued to be more accurate than other forecasting tools in the majority of cases. The existing evidence (Berg, Forsythe, Nelson & Rietz, in press) shows remarkable predictive accuracy for the election vote-share prediction markets even in the very short run (i.e., one-day-ahead forecasts using election eve prices). Berg, Nelson, and Rietz, (2008) compared on the pair-wise basis a massive number of pools with prediction market results (964 poll results to be precise) over the five USA Presidential elections since 1988. Such a comparison presented an analysis of the long-term predictive ability of prediction markets relative to polls. The researchers asked whether the best result of all available national polls or a contemporaneous IEM vote share market prediction was closer to the eventual outcome. In the last 100 days of election campaigns, prediction markets outperform the corresponding polls on 74 percent of the days.45 Such comparison method was not flawless since the sudden arrival of news just before the election could have changed everything. No forecasting method would have been able to predict such circumstances and

43 https://tippie.biz.uiowa.edu/iem/trmanual/IEMManual_2.html#Issue, [State: 28.11.2017] 44 Arnesen/Bergfjord (2014) 45 Berg/Nelson/Rietz (2008) 28 thus should not be judged by the consequences of such sudden news. It is important to emphasize that mentioned researches used raw polls rather than polls adjusted using some mathematical or historical model. The reasons were the potential applicability of the evidence to settings in which there was not a long history of forecasting mechanisms, the absence of consensus about the superiority of poll adjustment mechanisms, and the exclusive availability of raw poll results in the media.

Figure 3: Prediction markets in direct comparison with election polls 46

Figure 3 above nicely shows that prediction markets results exhibited greater accuracy than the results of the contemporary polls in most of the cases.

Figure 4: Iowa Electronic Markets and poll accuracy in 2008 presidential race 47

46 https://tippie.biz.uiowa.edu/iem/media/accuracy.html, [State: 28.11.2017] 47 https://tippie.biz.uiowa.edu/iem/media/08Pres.html, [State: 28.11.2017] 29 The reporting of raw poll results, rather than those of adjusted polls, was predominantly because of the recommendation of the Commission of Frederick Mosteller, one of the pioneers of mathematical psychology.48 This Commission was formed in 1948 in order to investigate the failure of opinion results in the Truman-Dewey election. The recommendation has been followed religiously by the opinion polling industry ever since.

There have been only several scientific works which claim that the accuracy of election polls can be higher than the one of prediction markets.49 Such works used the adjustment-pools technique which ex-post improved the accuracy of election polls to the level of being higher than the accuracy of prediction markets. The technique consists of polls, who are being used to make regression model forecasts. The forecast than take expected poll developments into account. This analysis was later being complemented by Arnesen and Bergfjord (2014). The framework used can be briefly described as follows:

 Two-party vote share was forecasted meaning that all the other candidates were ignored. After a vote share is being obtained, 50% was subtracted, resulting in a clear indication of a win. If a variable was negative, the opposition won.  The following regression equation projects the poll-based vote forecast for year y:

푉푦 = 푎푇 + 푇푃푌푇

 푃푌푇 is the actual poll number at time T in year Y.  and represent regression coefficients evaluated from historical data. They are calculated for every campaign day going back to 200 days before elections.  and results in the identical forecast for the observed result. > 0 implies that the incumbent party managed to do better historically than in the polls at time T. < 1 indicates the change of current trend.

Even though the data being used by Arnesen and Bergfjord (2014) was not wholly identical to the one of Wlezien and Erikson, 75 percent of the material was the same. Both studies focused on the 2008 and 2012 USA presidential elections. However, the former concluded that even though adjusted polls represent an improvement over regular polls, it can be by no means stated that such enhanced poll results improve upon results of the Iowa Electronic Markets. This study provided further support for the development and applicability of prediction markets, which was also in line with the majority of previous studies. In regards to the general comparison

48 http://www.nasonline.org/publications/biographical-memoirs/memoir-pdfs/mosteller- frederick.pdf, [State: 7.2.2018] 49 Wlezien/Erikson (2008), Wlezien/Erikson (2012) 30 between public polls and prediction markets mechanism, it is hard not to point out a distinct advantage of the prediction markets framework. It is familiar that public-polls methodology incorporates something known as the two-day lag period, meaning that this approximately corresponds to the time between polling the data and publishing of results. Such practice was probably logical ten or fifteen years ago, but current technological development had very likely changed the real lag time. Also, the adjusted-pools method represents a sort of historical data analysis, and there is no reminder necessary of the fact how fundamentally wrong turned out to be every quantitative technique based on historical data. On the other side, every price obtained in prediction markets represents a real-time value, with the additional advantage of being easily interpreted since it only depends on particular contract design. Prediction markets also relate to the convenience of being able to predict different metrics, simultaneously. Political prediction markets also profit from some economic arguments, the most obvious in this context being the equilibrium price. Nevertheless, public polls and prediction markets should by no means be considered as two mutually exclusive techniques. They possess, on the contrary, complementary characteristics. Arnesen and Bergfojrd (2014) state that, while survey results represent the voters, the market traders interpret the voters and anticipate their behavior. Polls can be theoretically used to improve the accuracy of prediction markets, prediction markets, on the other hand, are unlikely to have a positive influence on the accuracy of the polls. It is because the average citizen who takes part in a public survey probably does not possess the necessary financial literacy for the participation in a prediction market. Such view, however, seems to be accurate only up to a certain extent. Market traders use various sources, including polls, as an input for their trading decisions. However, they also have to use other perspective- taking methods. Prediction markets could have an impact on the results of public polls, but it is improbable that the results would be positively correlated with the accuracy. There is a famous argument that being informed about politics represents an irrationality50. “Nothing strikes the student of public opinion more forcefully than the paucity of information most people possess about politics.”51 As a combination of expressing opinions and acting upon their interpretation, prediction markets seem to be able to overcome some of the persistent flaws of modern society, even in the ambiguous field of politics.

50 Downs (1957, p.236) 51 Ferejohn (1990, p.3) 31 4.1.2 European election prediction markets - lessons from Norway

Most of the political prediction markets ran in Europe were nothing else than experiments, and as such, they do not possess the necessary historical consistency of the Iowa Electronic Markets. That was also the case with the Norwegian prediction market. The Norwegian election prediction market was motivated by a pure curiosity concerning a new, incoming polity. Sveinung Arnesen developed and established a prediction market motivated by a desire to see how well can prediction markets perform when dealing with Norwegian politics. The whole project was later the basis for his doctoral thesis, which further on serves as the primary scientific source of this particular master thesis section. The Norwegian prediction market was conducted in 2009, before the 2009 Norwegian parliamentary election. A little skepticism surrounded the project since the Dutch prediction markets trials proved to be less accurate than the ones in the USA in the past.52 Eventually, it turned out that the multi-party political system of Netherlands, a system which offers more than two political alternatives, caused the lower accuracy of Dutch prediction market trials in comparison with the two-party political system in the USA.

4.1.2.1 Trading mechanism53

The trading was conducted through the web-based program. The initial idea was to use the framework of the largest online international prediction market at the time, Intrade, together with the Norwegian financial newspaper Dagens Næringsliv. It was a pretty good idea as the Intrade was interested in gaining access to the region of Scandinavia, Dagens Næringsliv wanted to draw additional attention to the newspaper with something new, and the researchers would gain access to the data obtained. A plan was to establish a real money prediction market and that participants use private resources. The Norwegian gambling authorities Lotteritilsnet, together with the Ministry of Culture and Church Affairs, rejected the application of exemption of the experiment from the gambling laws. Eventually, the researcher used a specially customized prediction market platform for this experiment, developed by Kjetil Thuen from Evolver DA, and Kjetil Ravnås from Gnosis.

Initially, the organizers wanted to mimic the continuous double auction trading mechanism of the Iowa Electronic Markets. Their biggest concern, however, was the thin market or the low liquidity problem. Such concerns forced them to implement Hanson’s logarithmic market

52 Jacobsen et al. (2000) 53 Arnesen (2011) 32 scoring rule, developed in 2003. This automated market mechanism works as a two-sided market maker which enables traders to buy and sell contracts at their will. Even though such market mechanism obviously impedes the revealing of traders unbiased expectations of elections outcomes (in order for one trader to push the price in either direction, current price level, amount of money at disposal, and operator’s decision about how much effect at any given price level should each unit of the money have, play the decisive role), liquidity considerations were decisive in such an experiment. The researcher reported one specific vote share contract where, because of the impeding power of Hanson’s logarithmic market scoring rule, certain adjustments had to be made. As mentioned above, the logarithmic nature of this market maker can lead to a specific problem, where an increasing sum of monetary units becomes necessary to move the price in either direction, if the price is lower or higher than the neutral value, or 50 percent. Thus, the contract price was contained between 0 and 10 percent rather than between 0 and 100 percent. In such way, the traders were allowed to move the price more effortlessly since the neutral position was only 5 percent. Following Figure 5 illustrates the price function of Hanson’s logarithmic market scoring rule in 2009 Norwegian prediction markets.

Figure 5: The price function of the logarithmic market scoring rule in the 2009 Norwegian election prediction market 54

54 Arnesen (2011) 33 The aim was to make the whole user interface as simple and friendly as possible, which should in return induce the liquidity. The formulation of contracts was approached with great attention in this regard and was as a result understandable even for those participants who possessed no prior knowledge or interest concerning the trading at financial markets.

4.1.2.2 Participants

The prediction market trials lasted six weeks, whereas, the notification letter containing all the necessary information about the market, including its very own purpose, was sent out to all participants before the trials. The majority of participants were professors, students and active members of Liberal and Labor political parties. All the party members were recruited through their respective local organizations in Bergen, while the non-party members were recruited through a series of posters downtown Bergen and on the University campus. The organizers aimed to gather as much relevant, specific knowledge as possible. The recruitment period lasted approximately three months, starting at the end of the 2009 spring semester and lasting up until the trials started, meaning up until six weeks before the election day.

4.1.2.3 Incentives and payment schemes

The questions whether the real-money prediction markets provide greater accuracy than the play-money markets, and whether or not monetary incentives serve as better participation and trading motivators than non-monetary incentives do, were addressed in detail in the earlier part of this master thesis. Both dilemmas were understandably thoroughly dealt with during the design period of the Norwegian prediction market. As already stated, the uniformly accepted consensus regarding this topic still has not been reached. Some argue that non-monetary incentives provide sufficient motivation55 and that people are willing to do many things for free if they possess the necessary intrinsic motivation.56 As soon as participants receive monetary payments, the extrinsic motivation replaces their intrinsic motivation. If the extrinsic motivation is not able to compensate for the loss or absence of intrinsic motivation, the introduction of monetary incentives can potentially be counterproductive.

In the prediction market trials for the 2009 Norwegian elections, some participants got real monetary units in the form of Norwegian Krone (NOK 200), and this group consisted mostly of students and politically non-active participants. Each participant received NOK 200 directly from Mr. Arnesen. Play-money participants were all active party members, and each received

55 Cf. Read (2005) & Jacobs (2009), qtd. in Arnesen (2011) 56 Cf. Gneezy & Rustichini (2000), qtd. in Arnesen (2011) 34 10 000 play units in the market, which was NOK 200 play units equivalent. Facilitators expected that loss of monetary, extrinsic motivation within the latter group could be compensated with the intrinsic motivation of staying informed about the current election campaign. At the market closing, winnings of the non-monetary participants were paid out according to the amount of final balance of play units. The final summary of the organizers revealed that groups with monetary units showed greater trading activity than the groups having play money at their disposal.

4.1.2.4 Performance of the Norwegian Prediction Market Experiment 200957

The main goal of the facilitator was to induce as much trading as possible. Having such a goal in mind, the main question put in front of the participants was: “given the monetary units you have, how far do you want to move the prediction from its current level?”. In order to move the price, the traders have simply had to slide the bar to the right, which indicated a buy, or to the left, which indicated a sell. Before every final decision, real-time, updated information about potential new predictions, including what would such particular move cost in monetary units, was provided.

All in all, predictions produced by the Norwegian prediction market were more accurate than contemporary polls in 88 percent of the daily observations, during the 40 days of the trial period. The average mean absolute percentage error of the markets was 1.2 percentage points off the election outcome, whereas the average mean absolute percentage error of the polls was 2.1 percentage points off the election outcomes. The party members were harder to motivate than their non-party counterparts, probably because they found themselves in the middle of the campaign. The predictions of all party members were not made accessible to anyone other than the participants and the organizers. It was because of the fear that they would feel the need to alter their predictions if outsiders would be able to see them. This was not the case with non- party participants.

4.1.3 The rest of Europe

This part of the master thesis is based exclusively on the paper “Accuracy and bias in European Prediction Markets”58 from 2015 and it will in following present its main findings. The paper provides a laid out analysis of predictions of 62 vote share contracts collected from corresponding prediction markets ran in Germany, Norway, and Switzerland. The prediction

57 Arnesen (2011), Arnesen (2011) 58 Arnesen/Strijbis (2015) 35 market in Norway will be, however, intentionally left out since the detailed findings of the Norwegian prediction market occupy the former part of the thesis solely. The five-year period data of the paper includes in addition to the 2009 Norwegian parliamentary election, the 2011 Swiss federal election, the 2013 German federal election, and referendums and initiatives from Switzerland in the 2012-2014 period. Following table 5 represents a free chosen sample of the mentioned 62 contracts, with the plot of predicted and actual outcomes of all 62 vote share contracts presented with the Figure 6 following shortly after.

Country (Year) Contract Result Prediction Type CH (2011) Swiss People’s Party 26,6% 28,6% Election CH (2011) Social Democratic Party 18,7% 23,2% Election CH (2011) The Liberals 15,1% 13,7% Election CH (2012) Secondary residences 50,6% 46,7% Referendum CH (2012) Gambling games 87,1% 68,4% Referendum CH (2013) Asylum law 78,4% 66,1% Referendum DE (2013) Social Democratic Party 25,7% 28,9% Election DE (2013) CDU/CSU 41,5% 40,1% Election DE (2013) The Left 8,6% 8,9% Election Table 5: Overview of prediction markets contracts 59

Typically, between 50 and 100 traders participated in each market. Every single one of the considered 62 contracts was traded with the real money. An initial amount received by the participants in every market has varied somewhat, but it is safe to say that each trader received an average of €30 by the organizer. Every single prediction market being part of this analysis traded the vote share type of contracts solely. In regard to referendum-markets, each market consisted of contracts dealing with referendums that were held in the same calendar month. All the markets have been using the same prediction market software initially developed by Mr. Sveinung Arnesen. The whole genesis of the software was introduced in detail in the previous part of the master thesis, with the logarithmic market scoring rule (LMSR) being the chosen trading mechanism.

59 Own table based on Arnesen/Strijbis (2015) 36 Accuracy of European Prediction Markets 100%

80%

60%

40% Outcome

20%

0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Prediction

Figure 6: Actual versus predicted outcomes in European prediction markets 60

In the majority of markets presented by Figure 6 above, the price range had a minimum of zero and a maximum of 100 percent. However, there have been few exceptions. For all of the contracts in the 2003 German election market, the range was 0-60 percent. Figure 6 shows outcomes of all traded contracts within respective prediction markets. When looked at carefully, the Figure 6 above shows a general tendency to overpredict the share of yes-votes. The researcher reported that prediction-errors summed up to the amount of 172 percentage points, the tendency which was particularly strong for the contracts that were predicted in the region between 30 percent and 50 percent. When, however, the predicted vote share sizes are being split between those under and over 50 percent, the mean errors of predictions above 50 percent are negative. Two following histograms, presented by Figure 7 and Figure 8, will offer the clearest illustration of such error-making tendency.

Figure 7: Absolute percentage errors of predictions under 50%61

60 Own depiction based on Arnesen/Strijbis (2015) 61 Arnesen/Strijbis (2015) 37

Figure 8: Absolute percentage errors of predictions above 50%62

Since the detection and explanation of such error-making tendencies represent an important goal of this scientific paper, the possible causes and deductions will be closely looked at. The question arising from the observation of Figure 7 and Figure 8 is: “What makes traders and people in general, to overpredict small-sized contracts (underdogs) and underpredict the large- sized contracts within different frameworks?”. The first possible behavioral explanation would be the well-known favorite-longshot bias. This bias is a fundamental part of the prospect theory which states that people systematically overestimate low probabilities and underestimate high probabilities. The second possible explanation would be the one that takes social norms and values into consideration and imposes the groupthink or herding as a possible answer. If a person is a part of a specific group of people with whom it shares believes and opinions, and if the individuals are insulated from the outside influence, group members can begin, while stressing loyalty, to think alike. If a person prefers to look at the opinions of other people before it makes its own decision, herding could take hold. The third possible explanation could have political nature and is also mentioned by the authors of the underlying scientific paper. The horse race media coverage informs voters whether their candidate is doing well or not. According to the logic of such media, elections and referendums can only be exciting and useful in the state of close competition. Even if that is not the case, there can be a general tendency in media to overestimate and falsely report the narrowness of the corresponding political battle. Moreover, the fourth cause of the observed bias could be purely mechanical, in the mathematical sense of the word. As already stated, all of the considered prediction markets used

62 Arnesen/Strijbis (2015) 38 the logarithmic market scoring rule. Due to its logarithmic nature, such market maker works in such a way that it becomes increasingly expensive and hard to push the price further up and down from the midpoint. When combined with limited financial positions, such combination could result in overprediction of small-sized contracts and underprediction of large-sized contracts. It is important to say that none of the offered explanations and deductions are mutually exclusive as they could coexist theoretically. However, it is not possible to determine which cause for the observed bias has the more dominant role and by how much. Also, the last deduction could be interpreted as a shortcoming of this type of the market maker Even though many studies found that the accuracy of European prediction markets exceeded the accuracy of the polls, the supporting evidence is not universal 63 Poll ratings still represent the usual information source when it comes to prediction of the support for each party running in the election, referendum or initiative. The logarithmic market scoring rule (LMSR) is not the usual choice when running prediction markets with vote share contracts. Some relevant academic sources have however suggested such custom.64 The previous section of the master thesis subtly showed the potential impact of the prediction markets mechanism on our lives. It also underlined some of its most obvious deficiencies. Prediction markets are a tool in the making.

4.1.4 The PollyVote approach – The example from Germany

Prediction markets in the politics section of the master thesis will be rounded off with the PollyVote method of predicting. The PollyVote method represents the combination of average forecasts within and across four component methods (i.e., polls, expert judgments, quantitative models and prediction markets) and it has been successfully used for the forecasting of presidential elections in the USA since 2004. The Germany-based study provided further evidence that the combined PollyVote forecast was more accurate than the forecasts produced by each of the four component methods on its own.

The PollyVote method works because it combines the information gathered by all four different component methods. Since the methods are different, the gathered information is also not the same, and the aggregation of such dissimilar information results in improved forecasts.65 As implied by the quoted literature, the benefits of combined forecasting methods are known for a long time. The PollyVote66 method used for forecasting of 2013 & 2017 German election is the

63 Brüggelambert (2004) 64 Cf. the discussion in blog post by David Pennock (2006), qtd. in Arnesen/Strijbis (2015) 65 Granger/Bates (1969) 66 http://pollyvote.com/de/, [State: 30.04.2018] 39 same version used in the USA since 2004. Even though it is important to ascertain which forecasting method provides the most accurate results, the goal of even higher importance should be to combine advantages of every different forecasting technique, as they are of course not mutually exclusive. The German elections case 2013 looked as follows. o Polls

Polls typically measure public opinion at the specified point in time and usually do not provide a forecast of what will happen on a particular day. Nevertheless, they are often being interpreted as forecasts. Regarding the 2013 German election, Wahlumfrage.de published simple, unweighted averages of polls conducted by six established German pollsters, whereas Pollytix.de used weighting approach based on the robustness of samples and the recentness of their conduction. o Prediction markets

Concerning the 2013 German election prediction markets, five websites ran a total of six prediction markets. Out of these six prediction markets, one was a real-money platform, and the others were typical play-money markets in the form of ranking tournaments. Interestingly, one prediction market did not use a market mechanism in order to produce predictions as it was more of an online community. All six prediction markets were internet-based frameworks. o Experts

Five different expert studies were conducted before the 2013 German Elections. Experts had to have a specific profile as they were either political journalists or German election scholars. The selection made sure that their judgments were based exclusively on concrete knowledge. 53 journalists and 69 scholars on average took part in those five expert opinions. o Quantitative models

For more than thirty years, several well-known quantitative models have been used for prediction of election outcomes. Every single one of these models is based on what is today known as the retrospective voting. According to the retrospective voting, voters look at the past performance of the current government officials and reward or punish them based on the past performance. These established models are also known as political economy models since every one of them uses economic, political and (or) public opinion variables in order to measure the

40 performance of various parties.67 More details concerning these quantitative prediction models are available in Graefe (2015).

4.1.4.1 Mechanics of the Germanys PollyVote 2013

After shortly explaining how the forecasts produced by each component method are being obtained, this section will summarily illustrate how the combined PollyVote forecast is being produced. As already indicated, combined daily PollyVote forecasts were calculated using a two-step procedure. The first step represents the average of the individual forecasts produced within every single component method. The structure of individual forecasts within every component method looked as follows:

1) Pollsters – Allensbach, Emnid, FGW, Forsa, GMS, Infratest dimap, INSA/YouGov, TNS. 2) Prediction markets – Eix, Politikprognosen, Progosys, Spiegel Wahlwette, Wahlfieber I, Wahlfieber II. 3) Experts – Journalists, Scholars. 4) Quantitative models – Election.de, JérÔme et al., Kayser & Leininger, Norpoth & Gschwend, Selb & Munzert.

The second, final step is the production of a combined PollyVote forecast by averaging the combined forecasts across all four component methods listed above. Exponential smoothing, GARCH method and various types of simulations are usually considered to be superior to a simple method of averaging in the role of forecasting Initial, as well as the latest reviews of this topic, provided, however, no further evidence in favor of any of the aforementioned complex methods when compared to the simple technique of averaging.6869

4.1.4.2 Findings and performance

The 2013 PollyVote lasted 58 days, from 26th of July until 21st of September which was also the day of the election eve. The performance of the 2013 PollyVote method from Germany was judged according to the common category of statistical accuracy in the form of mean absolute error (MAE). A randomly picked forecast produced by polls achieved an MAE of 1,44 percentage points on average which was a surprising result bearing in mind that it was more

67 Cf. Special symposiums in Political Methodologist 5/2, American Politics Research 24/4 and PS: Political Science and Politics 34/1, 37/4, 41/4 and 45/4, qtd. in. Graefe (2015) 68 Cf. Clemen (1989), qtd. in Graefe (2015) 69 Cf. Graefe/Küchenhoff/Stierle/Riedl (2014), qtd. in Graefe (2015) 41 accurate than the same measure produced by quantitative models (1,57 percentage points on average), experts (2,13 percentage points on average) and prediction markets (2,33 percentage points on average).

Polls PMs Experts Qn.-Models Mean Absolute Error Typical 1,44 2,33 2,13 1,57 Combined 1,27 2,08 1,67 1,14 MAE ratios Combining within 0,88 0,89 0,79 0,72 Combining across 1,08 0,66 0,82 1,21 Combining within & across 0,95 0,59 0,64 0,87 Table 6: Accuracy of the PollyVote component methods in Germany 201370

Table 6 shows the effect of using the PollyVote forecasting technique in Germany 2013. There are many shreds of evidence throughout the master thesis that prediction markets provide a significant improvement in the accuracy when compared with public polls and expert forecasts. The example from Germany shows that the flawless forecasting mechanism is yet to be invented. However, this should be considered as a side-effect, as the aim of every PollyVote project is to combine the forecasts within & across all four component methods. The mean average error achieved by the PollyVote in Germany 2013 was 1,37. Division of this value with the respective MAE typical component values results in the critical value which describes the PollyVote method accuracy in comparison with the accuracy of every other individual component method. The last row of Table 6 shows that the PollyVote method achieved error reductions ranging from 5 percentage points (polls) until 41 percentage points (predictions markets). The results clearly show the additional benefit of combining all the traditional forecasting methods in the form of the PollyVote method, which is, of course, an improved accuracy. The only known affair in which the PollyVote method failed to forecast the right outcome of political elections were the 2016 USA presidential elections. Same as every other known forecasting method, the 2016 PollyVote favored Hillary Clinton as opposed to Donald Trump. That showed us that it was the wrong prediction with one small remark, however. When looking at the absolute numbers, the higher number of voters gave their voice to the Hillary Clinton. The USA electoral system took care of the rest.71 The results of the individual

70 Graefe (2015) 71 http://pollyvote.com/en/2016/11/09/a-first-post-mortem/, [State:03.05.2018] 42 forecasting methods were however somewhat surprising. All of the individual component methods were conducted as independent projects, the PollyVote method simply used the predictions produced by every single method and transformed them into the combined, PollyVote forecast. In the case of German 2013 elections, the polling method proved to be the most accurate one, whereas prediction markets provided the least accurate results. Even though this was a surprise, considering the importance of the prediction markets mechanism for the entire master thesis, the 2013 German study managed to shed some light on the PollyVote method as a prosperous forecasting mechanism. 4.2 Prediction markets in sports

The central aspect of sports betting markets, the feature that distinguishes them from the various election, corporate and other types of prediction markets, is the certainty of results. Once an event has happened, the result cannot be manipulated. The only wishful goal, therefore, should be the ability to predict the outcome of such contests in advance. This is why sports betting markets find themselves right up there at the top end of prediction markets academic, and general public interest. Gambling on various sports events and predicting their outcome is considered to be one of the very first types of prediction markets. Also, all of that was happening in the absence of the general consciousness concerning those incorporated predictive capabilities. For the general purpose of this master thesis we will distinguish between two main types of sports betting markets:

o Sports betting markets with the bookmaker o Sports betting markets without the bookmaker

Sports betting markets are a straightforward mechanism which includes publicity and inevitability of outcomes as one of its main strengths. It is a subject of great public interest and plays an important role in our lives. The rise of sports betting markets can only be looked at as a natural consequence of a matter with such high publicity. The crucial element of every sports betting market are bets. There are four basic types of bets used:

o The point spread bet – is merely the margin of victory. It is a forecast of the number of points by which a stronger team is expected to defeat a weaker one. The researches commonly refer to it as a betting line or a measure of the expected outcome of sporting events. o The Moneyline bet – represents the betting odds. It is the implied probability, the chance an event will occur.

43 o The point total bet – involves various types of betting on how many points total will two teams score during a given timespan in a game. o Proposition bet – asks a question whether or not a particular thing will happen during a game.

Sports betting markets, both with and without the bookmaker, start with the facilitation of bets that bettors wager on. The stakes from losing sides of the outcome are used to pay off the winning stakes. In sports betting markets with the bookmaker, the betting house always retains the fixed percentage of all wagers made. The purpose of betting markets with the bookmaker is a pure profit, both from bookmakers and gamblers point of view. The fun and personal interest are of course additional vital factors from the gambler's perspective. Betting markets with the bookmaker are established in order to gain a profit for the bookmaker. Gamblers take part ever more, as they see such markets as a kind of a tempting challenge to beat the system (similar phenomenon to roulette, where players engage in betting, even though they know that every casino game was designed to provide the house with a built-in edge stacking the odds in favor of casino). No one can deny that these same factors drive the participants of sports betting markets without a bookmaker mechanism. However, the mechanics of such sports trading exchanges, as sports betting markets without the bookmaker are often being referred to, is entirely different. Participants themselves determine the bets. The main consequence or result of such a trading mechanism should be a prediction since the bettors are the only ones to influence the betting lines. Also, there will be no bets taken if the estimated probabilities of the outcomes do not differ across participants. It can be safely stated, that betting lines as such should be free of at least the premeditated bias.

Moreover, there should be a clear distinction between correlation and causality within the field of sports betting markets. Correlation can indicate what is going to happen. However, the causes of the correlation have to be understood. Otherwise, the obtained predictions could be a side effect of some other goal. Causality, on the other side, could help us understand and shape the future. As the perfect starting point to address these critical relations, sports betting markets and resulting bets, their genesis, and their utility come in the forefront.

4.2.1 Sports betting markets with the bookmaker

The majority of traditional researches of sports betting markets use sports betting lines as predictions of underlying sports events72, with a vast majority of them testing the price

72 Kain/Logan (2014) 44 efficiency of closing lines. All of these studies assume a priori, that closing betting lines contain all relevant public and private information regarding the outcome of respective contests.73 There are however some severe theoretical pitfalls connected with this assumption which came under scrutiny with the increasing attention towards prediction markets. Theoretically, bookmaking is no precognition mechanism74, and more prominent betting exchanges are paying the knowledge of external bookmakers. In return, external bookmakers create betting lines which minimize the risk and secure the profit of a betting house. The betting line is considered to be a wager-weighted median of bettors' ex-ante beliefs about a specific event. In order to minimize the risk, the betting house aims to hold on to the balanced book rule. If this is the case, the betting house experiences no risk at all, since all the wagering is equally divided on both sides of the betting lines. This rule consequently ensures that a certain number of bets is always being placed on the favorite, which per se improves the predictive power of corresponding betting lines. A betting line for a favorite has to be attractive enough in order to punter places his wager on it. It could be concluded that a certain extent of betting lines’ predictive power comes therefore from the mechanics of the balanced book rule. Simplified, betting houses set their betting lines as a response to ex-ante beliefs of bettors. On the other side, sports betting markets are highly competitive. Any poor forecast in the form of offered odds would probably be either ignored entirely by bettors who could place their bets somewhere else or exploited in the sense that they could make certain returns. When examining their predictive power, the betting lines are commonly being regressed on the actual outcome, and the results usually find a strong correlation between the two75. The problem is that in the process they potentially neglect all sorts of subsets of games, where the difference between the bettors beliefs and the expected outcome is continuously present. An average punter is generally not aware of the fact that betting lines also represent a specific type of information. As such, they could be intentionally manipulated in the way that the average bettor over- or underreacts to them. The predictive power should be in such cases considered as an unintended aftereffect, meaning that any positive correlation between betting lines and outcomes of underlying events would be a coincidental byproduct of the profit maximization process.

The foundation for the analysis of the betting market with the bookmaker mechanism was the paper called “Are sports betting markets prediction markets? Evidence from a new test.”76 The

73 Cf. Vergin/Scriabin (1978), Gray/Gray (1997), Dare/Holland (2004), qtd. in Borghesi (2014) 74 Bem (2011) 75 Golec/Tanarkin (1991), Dare/Holland (2004), Sinkey (2011) 76 Kain/Logan (2014) 45 majority of researches conducted on sports betting markets used sport betting lines exclusively as predictors of underlying sports events. The introduction part of the sports section of the master thesis indicated that betting lines may prove to be a good predictor of margins of victory (MOV), but it does not necessarily follow that sports betting markets produce reliable and good forecasts of the listed sports events. The foundation paper of this section of the thesis represents a significant breakthrough in this research field. In order to examine whether sports betting markets possess predictive features, the study synthesized the common betting information used by the majority of studies of sports betting markets, namely the margin of victory, with the betting information in the form of the sum of scores. Such a combination should have resulted in a significant improvement in both the scale and the scope of data used for testing of the predictive power of the sports betting market with the bookmaker. It is logical to assume that tests which consist of both the margin of victory and the sum of scores should offer an improved characterization of the game in comparison to traditional tests in this field77.

The general test conducted by the researches was a test in the framework of the seemingly unrelated regression (SUR).78 The equations are seemingly unrelated meaning that shocks to one equation would cause natural spillovers to the other one. In a clear econometric language this meant the following:

 For the coefficients: bettingline=under/over=1

 For the intercepts: bettingline=under/over=0

The actual assessing of the predictive power of these two metrics was conducted with the method of linear regression. The generalized system of regression equations looked as follows:

 MOVi = 0 + 1SPREADi + 1i,

 SUMi = 2 + 3Over/Underi + 2i.

The first part of the test examined whether the hypothesis 0 = 2 = 0 holds. If this condition holds, predictions of the betting market can be considered free of systematic errors. The second part of the test examined whether the hypothesis 1 = 3 = 1 holds. If this condition holds, the variables chosen are an accurate predictor. If this condition holds, chosen variables can be considered to be accurate predictors. If the particular sports betting market is to be considered predictive, both hypotheses have to hold. The Brausch-Pagan test of the independence looks into the necessary mechanical relationship between the margin of victory and the sum of scores.

77 Cf. Evan/Noble (1992), qtd. in Kain/Logan (2014) 78 Cf. Zellner (1962), qtd. in Kain/Logan (2014) 46 The data used for the foundation paper were collected from the four official sports leagues in the United States of America, the National Football League (NFL), the National Basketball Association (NBA), the National College Athletic Association (NCAA) football, and the National College Athletic Association (NCAA) basketball, in the timespan from 2004 until 2010. Outcomes of the games were obtained from the Pinnacle sports book. It resulted in a pretty large sample size of 40,372 games from all sports in total. The authors found that the sports betting market generally predicts certain aspects of the games better than others. The betting lines turned out to be an accurate predictor of the margin of victory, whereas the over/under turned out to be not a precise predictor of the sum of scores. This implied that corresponding betting markets could not be considered to be predictive. Brausch-Pagan tests of the joint predictive power of the market revealed the lack of initially assumed mechanical relationship between the margin of victory and the sum of score data. This further implied that any discovered predictive capabilities of the analyzed sports betting market could not be ascribed to the necessary causality which determines a specific mechanism as predictive.

The possible explanations for such findings are numerous. It is easy to imagine that the availability of information for the sum of scores and the margin of victory metrics is not the same. On the other side, even if the availability of information would not represent a problem, the interpretation of available information almost always would. As already stated, the bettors are often not aware of the fact that betting lines are in a way the summaries of the information available before the game.79 However, those who are aware of such fact interpret the available information very rarely in an optimal way. One of the best know judgment biases concerning the interpretation of new information in the framework of economics is the confirmatory bias.80 The confirmatory bias is a cognitive type bias which is defined as the bolstering of the existing hypothesis using the weak evidence. The bias is in stark contrast to Bayesian updating, which assumes a smooth and symmetric updating of current beliefs. The confirmatory bias has been documented both in the laboratory81 and the field82 type of experiment. Moreover, it would be rational to think that the sum of scores metric is a lot more complicated to predict than the margin of victory metric. Such an assumption was substantiated with the findings from Kain/Logan (2014). In regard to the sum of scores, the findings imply that updating and incorporation of new information represent an unusually hard task. One valid plausibility could

79 Logan/Sinkey (2011), Card/Dahl (2011) 80 Cf. Rabin/Schrag (1999), qtd. in Andrews/Logan/Sinkey (2011) 81 Jones/Sudgen (2001) 82 Andrews/Logan/Sinkey (2011) 47 also be the asymmetry of the wagered money between the margin of victory and the sum of scores. Such a case would probably lead to uneven incentives for the improvement of predicting techniques of both the sum of scores and the margin of victory. Also, one last clarification would be the possibility that the mechanical relationship assumption between the sum of scores and the margin of victory could be wrong per se.

4.2.2 Sports betting markets without the bookmaker

This section of the master thesis is a result of a detailed analysis of the research paper called “Informed traders and balanced books.”83 The section should enrich this master thesis with the findings from the sports betting markets biotope, but from an opposite perspective since the underlying research paper used the sports trading platform without the bookmaker as the framework for the study. The introduction part of the sports section of the master thesis pointed out that the majority of research papers on the respective topic used the closing betting lines exclusively in order to examine their price efficiency and predictive abilities. The foundation study of this particular section of the master thesis used a radically new approach concerning the exploration of sports trading platforms. The researches emphasized the betting lines dynamics, from open to close, in order to determine whether they possess predictive abilities. The price movement after the kickoff was neglected. All of the data for the study came from the Tradesports.com, an online betting platform which is currently non-operational. The Tradesports.com is a platform which allowed traders to trade with binary-option types of contracts priced between $0 and $100 for a ten-contract lot. Such price format allowed the direct transformation of prices into probabilities84. The contract prices were determined by the likelihood of the outcome of a respective underlying event. Since there were no bookmakers in this market, the prices represented the platform’s dollar-weighted consensus formed by the supply and demand. For example, the contract titled ‘[email protected]’ meant that Green Bay Packers should beat Atlanta Falcons by at least 6.5 points in order for the contract to expire at $100. Otherwise, it would expire at $0 worth. This also meant that the particular contract priced at $60 expires at $100 sixty percent of the time. As it was the case with the previous study, the researchers used the point spread or the margin of victory type of contracts as well as the totals or the sum of score type of contracts. Totals contracts expired at $100 if the combined score of both respective teams exceeded a certain limit. The researchers based the whole analysis on a distinction between win rates and price changes of the first trade registered

83 Borghesi (2014) 84 Wolfers/Zitzewitz (2006) 48 since the contract was posted to the exchange, and price changes and win rates of the first trade occurring after the game kickoff. Since there was no bookmaker in such a trading platform, the presence of unbiased, informed traders should ensure the constant movement of contract prices towards expiry prices, from the trade start until the game kickoff. Such a mechanism should have theoretically led to the conclusion that by the time an underlying event begins, there should be no difference between the contract prices and the win rates as the contract prices would already incorporate all available information. The data set consisted of the NFL and the NBA game execution times and prices. This resulted in 13,741 observations. The total number of trades observed was 389,987 for the NFL and 383,009 for the NBA contracts. The analysis provided substantial proof in favor of the predicting power of the sports betting platform without the bookmaker. In order to offer a clear summary of such findings, the thesis will continue with their tabular exposition.

All contracts NFL sides NFL totals NBA sides NBA totals

Mean win rate 55,27% 55,19% 47,93% 58,62% 50,37%

Q1 win rate 51,54% 51,83% 41,59% 54,75% 47,84% Q2 win rate 54,53% 55,21% 50,38% 58,09% 49,02% Q3 win rate 57,61% 54,74% 50,00% 60,48% 55,18% Q4 win rate 58,54% 59,18% 50,29% 61,73% 52,28%

N 13.741 2.368 845 7.018 3.510 € Table 7: Win rate of the first pregame trade 85

Table 7 illustrates whether or not the Tradesports.com platform possessed some predictive powers. The researchers divided the sample into four quartiles based on the upward (downward) pregame price movements. They referred to such price movements as pregame drifts. The Q1 pregame drift represents the lowest quartile (the most significant downward pregame drift), whereas the Q4 pregame drift represents the highest quartile (the most significant upward pregame drift) of pregame price movements. As Table 7 indicates, the mean win rate of the whole sample is 55,27 percent. The mean opening contract price was $55.33 which implied an expected mean win rate of 55.33 percent. The principal aim of this part of the analysis was to determine whether or not there was a distinction of win rates between the contracts who experienced the most significant downward and upward price movements. Table 7 data reveal

85 Borghesi (2014) 49 the genuine predictive powers of such kind of sports trading markets. In other words, when and if the prices moved in the pregame period, they moved in the right direction. Table 7 shows that when prices of the contracts experience the most significant downward movement, the Q1 win rate in the vicinity of 51,54 percent follows. That is significantly different from the aggregate mean win rate of the whole sample which is 55,27 percent. On the other side, when prices of the contracts experience the most significant upward pregame movement, the Q4 win rate in the vicinity of 58,54 percent follows. The result which is also significantly different from the sample mean win rate of 55,27%. Based on this particular part of the analysis, it can be concluded that traders in such sports trading platform incorporate relevant information which is necessary to predict the outcomes of underlying sports events correctly.

As the supplementing part of the analysis, the researchers conducted a series of regression tests. The results are displayed in the following Table 8.

Pregame In-Game

Estimate p-value Estimate p-value

Drift Q1 0,44 0,000 0,43 0,000 Drift Q2 0,46 0,000 0,43 0,000 Drift Q3 0,50 0,000 0,44 0,000 Drift Q4 0,51 0,000 0,44 0,000 NFL 0,02 0,050 0,01 0,237 Totals 0,00 0,876 0,01 0,516 Price dummies? Yes Yes Table 8: Contract win rates 86

Table 8 represents two separate models. The first model deals with the first trade of each contract after it was posted to the exchange. The second model deals with the first in-game trade of each contract. The dependent variable in both models is the Win. It is set to 1 when the underlying contract expires at $100. Independent variables of both models are pregame drifts Quartiles which have been explained earlier. NFL is set to 1 when the contract represents a bet on an NFL event and 0 otherwise. The NFL is set to 1 when the contract represents a bet on an NFL event and 0 otherwise. The Totals variable is likewise set to 1 when the contract represents a totals wager and 0 otherwise. The researchers used ten dummy variables to control for the price of the contract.

86 Borghesi (2014) 50 The quantitative output of the respective regressions was summarized in Table 8 above. That contract win rates in the lowest (highest) quartiles are 44% and 51% respectively, which adds to the results of Table 7 and suggests that prices incorporate relevant information during the pregame period. The test on the difference between Drift Q1 and Drift Q4 resulted in a significant difference in magnitude between the two (p-value < 0.0001) which substantiated the statement above. The table also shows that win rates of the NFL contracts are far lower than the ones of the NBA contracts, an interesting fact which could indicate various causes. The second, in-game, regression model output shows that win rates of the contracts from Q1 and Q4 do not differ from each other. The test on the significance of the difference between the win rates from the two respective quartiles substantiates the finding with the p-value of 0.7493. It shows that prices managed to incorporate all available information during the pregame phase and that there is no momentum carryover from the pregame phase to the in-game trading phase. Based on the findings of this unique study, it can be stated that sports betting markets without the bookmaker exhibit good predictive abilities. The price-generating mechanism at the heart of such markets produces proper incentives for both the information revelation and the information incorporation. Further, the study interestingly implies that the skill-level of NBA bettors differs from one of NFL bettors. The intrinsically different nature of these sports categories, the availability of the respective information, and the processing of respective information could prove to be the accountable causes. The availability of the relevant information is as important as the processing of that information itself when talking about the quality of resulting predictions. The following Figure 9 explains vividly such statement.

100 20 18 80 16 14 60 12 10 40 8

6 (National League) (National

20 4

Relative Search Frequency Search Relative Relative Search Frequency Search Relative 2

(Premier League & Football League) Football & League (Premier 0 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Premier league Football league National League

Figure 9: Search frequencies on Google 87

87 Own depiction based on Reade (2014) 51 Figure 9 represents the worldwide Google search frequencies regarding different football leagues in England. England is known to be the cradle of today’s football, and its football leagues and their worldwide popularity can serve as the perfect foundation for the quick elaboration on the importance of the availability of information. Figure 9 reveals different levels of interest and accordingly different levels of available information concerning each football league depicted. It is to be expected that the popularity and the resulting coverage of England’s Premier League, combined with the skewed quality of teams, would most probably produce predictions which are considerably more accurate than the lower leagues competitions. It can also be assumed that the quality of predictions would decline in comparison to the Premier League while moving through the lower ranks, as the availability of information wanes considerably Even though the availability of information plays a crucial role, the character of sources and the resulting quality of information must not be neglected. Some relevant studies88 refer accordingly to the broken-leg cue. The term stays for a potentially detrimental effect of both the greater media coverage and the availability of information on resulting predictions. This means that the greater availability of information per se, must not necessarily result in more precise predictions. It could also be considered as the noise with some disruptive effect. The bridge between the availability of information and the processing of the respective information represents, therefore, a critical relationship. 4.3 Corporate prediction markets

The concept of prediction markets, especially within the corporate surroundings, was initially taken with a grain of salt by the general public. The main postulates of well-working markets in the form of unlimited participation and high liquidity were hardly ever going to be possible within the corporate prediction markets concept. It was only after the success of first public prediction markets at the end of the twentieth century and the beginning of the twenty-first century that the concept slowly began to get into its stride. Such developments, together with the expensive and nevertheless flawed information gathering mechanisms which were available at the time, resulted in the idea of collecting and utilizing the collective intelligence of employees within certain organizations on a regular basis. The famous book “The Wisdom of Crowds”89 provided both academic and corporate circles with an additional source of enthusiasm. The potential benefits of such integrated prediction markets were enormous, and as a consequence, prediction markets as decision support systems came into existence. More

88 Cf. Forrest (2005) & Forrest/Simmons (2000), qtd. in Reade (2014) 89 Surowiecki (2004) 52 recently, corporate prediction markets were labeled as one of the top 5 “newer techniques” with a potential to add value to corporate marketing as well as to corporate governance. In order to get the sense of the magnitude of such high potential, prediction markets were grouped with social media and big data analytics.90 When talking about corporate prediction markets and their problem-solving abilities, their role in such a context is to predict various vital goals of respective companies and institutions. Fighting the uncertainty is right up there at the top of every company’s priorities. Siemens Austria launched the first known corporate application of prediction markets in 1997. Hewlett-Packard Corporation developed internal prediction markets in order to forecast sales of their products shortly after.91 Since 2005, numerous companies decided to take their part in the development of prediction markets. Google, Ford Motor Company, Abbott Labs, Best Buy, Chrysler, Corning, Electronic Arts, Eli Lilly, Frito Lay, General Electric, Hewlett Packard, Intel, InterContinental Hotels, Masterfoods, Microsoft, Motorola, Nokia, Pfizer, Qualcomm, Siemens, TNT, are the best-known examples.92 Google prediction markets represent the most significant known corporate prediction market, regarding both the number of securities and participants, established up to date. The master thesis will accordingly continue as follows. Google’s prediction markets will be thoroughly analyzed. This will be then accompanied by the direct comparison between the Google’s prediction markets, the Ford Motor Company’s prediction markets and the prediction markets of Firm X. Firm X is a large, privately held conglomerate with headquarters in the Midwestern USA. It is a company with global operations. The company’s main activities include refinement of crude oil, manufacturing of chemicals, building materials, paper products, and synthetic fibers. The name of the company had to stay unknown out of the confidentiality reasons. The section will be than rounded off with the presentation of the Siemens prediction markets. Such an approach should result in comprehensive insight into the most significant, publicly-known corporate prediction markets up to date.

4.3.1 Prediction markets at Google

Google launched its prediction markets in the headquarters of the company in 2005. The brains behind it were Mr. Bo Cowgill and a few other employees. His name is important since he was the coauthor of all relevant scientific papers which serve as a foundation for this section of the master thesis. Google has always been known for its highly educated workforce, and as such,

90 Rydholm (2013) 91 Chen/Plott (2002) 92 Cowgill/Wolfers/Zitzewitz (2009) 53 the company was considered to be the perfect place for the launch of such a prediction market project.

4.3.1.1 Trading mechanism

Google`s prediction markets project adopted the same trading mechanism used by the Iowa Electronic Markets. The trading mechanism was the continuous double auction mechanism which was described in the first section of this master thesis. Google tried to predict whether specific objectives and key results, widely known as OKRs, would be achieved. The initial experiment became later an indispensable part of Google's culture. Since 2005, OKRs are being discussed and thoroughly analyzed within the company four times per year.93 OKRs had to be measurable and quantifiable in order for a prediction market to be able to produce relevant predictions. The logical implication was then that the respective questions had to be correctly and precisely formulated. Every specific question represented a different market and had from two to five possible mutually exclusive and exhaustive answers. Furthermore, each specific answer embodied a different security.

Type Example Share of markets Demand forecasting # of Gmail users at the end of quarter 20% Performance Google Talk quality rating 15% Company news Russia office to open 10% Industry news Will Apple release an Intel-based Mac? 19% Decision markets Will users of feature A users use feature B more? 2% Fun How many "rotten tomatoes" will Episode III get? 33% Markets run (questions) 270 Securities (answers) 1,116 Table 9: Prediction markets at Google 94

Table 9 indicates that so-called “fun” markets, such as the quality of the new Star Wars Episode, gas prices, the federal funds rate, represented about 30 percent of prediction markets at Google. This fact is one of the main differences between Google’s prediction markets and prediction markets at other contemporary companies. While other companies mostly avoided such prediction markets considering them as potentially time wasting, Google considered “fun” as an indispensable part of general prediction markets offerings. The trade volume of Google’s

93 Cf. Levy (2011), qtd. in Cowgill/Zitzewitz (2015) 94 Own depiction based on Cowgill/Wolfers/Zitzewitz (2009) 54 fun-markets correlated positively with the trade volume of all the other prediction markets, implying that “fun” markets had a positive effect on the liquidity of all the other prediction markets at the company.95 Each security was worth one unit of artificial, prediction market currency called Gooble if the answer was correct, and zero otherwise. Very similar to the unit- portfolio trading mechanics of the Iowa Electronic Markets, traders in Google’s prediction markets were able to exchange one unit of Gooble for a complete set of securities. They were also in the position to exchange a complete set of securities for one unit of Gooble. Such trading mechanics ensured a constant presence of two-way liquidity. Replicating the trading mechanism of the Iowa Electronic Markets short selling was not allowed in any of the Google’s prediction markets. Employees had at their disposal a possibility to create their robotic traders.

4.3.1.2 Participants

Participation in prediction markets at Google was open to all of the active employees of the company including some contractors and vendors. 6,425 employees had a prediction market account, 1,463 conducted at least one trade.96 In order to get some sense about what was the order of the magnitude of the initial prediction markets at Google, authors of the underlying scientific paper reported that Google had 5,680 and 10,674 registered employees in the year 2005 and 2006 respectively. Such data substantiate the credibility of the selected underlying paper since a significant number of employees was actively involved in prediction markets. The profile of the average participant corresponded highly to a modal employee of the company. They were mostly programmers who were part of the engineering and code reviewing departments at the company.

4.3.1.3 Incentives and payment schemes

Since the introduction of prediction markets, new markets were created within the company on a quarterly basis. It coincided with Google's objectives and key results (OKRs) analysis frequency. Every three months the company introduced roughly 25 new prediction markets which had to be resolved by the end of the respective quarter. Participants received a new endowment of Goobles accordingly in order to trade with new securities. The prize budget was $10,000 per quarter. Goobles were converted into raffle tickets, and the quarterly prize budget was then raffled off at the end of each respective quarter.

95 Cowgill/Wolfers/Zitzewitz (2009) 96 Cowgill/Wolfers/Zitzewitz (2009) 55 4.3.1.4 Findings and performance

The simplest way of starting the prediction markets analysis is to compare the price paid and the ultimate payoff of every registered security (contract). The previous parts of the master thesis indicated that a consensus on prediction markets price interpretation is that if the price X was paid for the contract, the contract should reach the promised payoff X percent of the time. In other words, when the price X was paid for the contract, the contract should return at least the same price X in expectation. The following Figure 10 illustrates the relationship between the prices paid and the resulting payoffs in Google`s two- and five-outcome prediction markets. These two types of markets account for the 86 percent of the total markets.

Figure 10: Price-Payoff relationship of two and five-outcome Google prediction markets 97

Red dots represent trades in two-outcome markets whereas blue dots stay for trades in five- outcome markets. The researchers analyzed 22,452 trades of two-outcome markets and 42,416 trades of five-outcome markets. All the trades were sorted into 20 different bins according to price (i.e., 0-5, 5-10, 10-15), and the average price and payoff probabilities of every single bin were plotted. Blue and red dashed lines represent regression equations lines obtained using the ordinary least squares method. Looking at the depiction above imposes the following remark. The analysis of prediction markets in politics and sports showed that participants of such prediction markets exhibit the behavioral phenomenon pattern widely known as the favorite- longshot bias, substantiating in this way the theoretical postulates of the prospect theory. Figure

97 Cowgill/Wolfers/Zitzewitz (2009) 56 10 above shows that different frameworks, different biotopes of prediction markets represent a critical factor and tend to produce some different results.

Five-outcomes markets, marked with blue dots, exhibit some positive returns for contracts priced roughly below 0.2. Two-outcomes markets, depicted with red dots, show consistent positive returns of the contracts priced all the way up to 0.5. In other words, both types of contracts are being underpriced in the price region below 0.2 and 0.5 respectively. Results show no apparent consistency in the higher price regions. Since the results of the lower price regions contradict the fundamentals of the favorite-longshot bias, the authors refer to such findings as the reverse favorite-longshot bias. There is a considerable number of scientific papers and online sports trading platforms which provided substantial proof for the presence of the favorite-longshot bias in public prediction markets.9899 It seems that the theoretical possibility of huge winnings, together with the satisfaction of correctly predicting such outcomes, drives bettors towards placing such wagers. However, the findings of the underlying paper differ fundamentally. It can be stated that one of the preeminent causes for such findings lies in the corporate environment of prediction markets. It can be stated that one of the preeminent causes for such findings lies in the corporate environment of prediction markets. Some scientist state that the possibility of taking large positions for a certain amount of downside risk and the combination of limited liquidity and risk-neutrality states should almost always result in the favorite-longshot bias.100 It is, therefore, a surprise that the reverse favorite-longshot bias emerged in the corporate framework since the risk neutrality and liquidity constraints assumptions should always hold in corporate surroundings. However, holding of the large positions for the specific amount of downside risk is not possible in the corporate surroundings as every participant gets a particular amount of starting capital. This could be regarded as the potential explanation for the reverse favorite-longshot bias findings. One other possible explanation could lie in the fact that being the part of a large successful system and different social groups within that system can result in a kind of the “underdog-aversion” which could consequently produce a herding type of behavior. Both the “underdog-aversion” as well as the herding would most probably end up in support of the favorites. Given the mentioned liquidity and account constraints, the presence of marginal traders in such a context would not be able to push the prices in the right directions.

98 https://www.pinnacle.com/en/betting-articles/Betting-Strategy/explaining-favourite- longshot-bias/VUN2U32R85PPF4YP, [State: 12.03.2018] 99 Wolfers/Zitzewitz/Leigh (2007) 100 Cf. Ali (1977), Manski (2004), qtd. in Cowgill/Wolfers/Zitzewitz (2009) 57 Trading in Google's initial prediction markets provided compelling evidence for some other behavioral patterns. Following Table 10 nicely presents this evidence.

Observations Avg. Price Avg. Payoff Return

All markets 70.706 0,357 0,342 -0,010 Google relevant 37.910 0,31 0,293 -0,017 2-outcome markets 9.023 0,509 0,492 -0,017 Best 4.556 0,456 0,199 -0,256 Worst 4.467 0,563 0,790 -0,227 5-outcome markets 26.511 0,239 0,222 -0,017 Best 5.592 0,244 0,270 0,027 2nd 5.638 0,271 0,246 -0,025 3rd 5.539 0,296 0,179 -0,118 4th 5.199 0,206 0,178 -0,028 Worst 4.543 0,162 0,236 0,074 Table 10: Optimistic bias in the Google prediction markets 101

Table 10 provides quantitative evidence for the optimistic bias or the short aversion bias. The table summarizes positive as well as negative outcomes for the Google company in general, depending on whether the security’s outcome would be good news for the company. Fun markets were excluded from the analysis since it was in some cases impossible to determine the nature of the impact the fun markets had on the company. The returns of purchasing securities were on average negative. Especially two-outcome markets display the presence of such bias since the trades earned on average minus twenty-six percentage points. This implies, in a simplified language, that the employees refused to sell particular securities for too long, were too optimistic considering specific outcomes, and were probably unable to think outside the prevailing opinions at the company. The presence of the optimism bias was further substantiated with a series of regressions in subsamples of the data. Returns to expiry were the dependent variable of the regression analysis and optimism of security, on the scale from -1 (for the worst outcome) to 1(for the best outcome), was used as the independent variable. Subsample events under the direct Google’s influence, two-outcome markets and the first month of the calendar quarter show the most significant presence of the optimism bias, measured with the negativity of coefficient. Table 10 shows one more interesting behavioral implication of Google prediction markets. When looking at the average returns of five-outcome markets, it can be

101 Cowgill/Wolfers/Zitzewitz (2009) 58 noticed that the returns of the intermediate outcome achieved the worst result. Authors of the underlying scientific paper refer to such findings as the extreme aversion bias. It can only be assumed that the subjects of such bias would most probably be new, inexperienced and risk- averse employees. Such a bias could be ascribed to the same type of biases to whom the herding bias and the conformity bias belong.

4.3.2 Google’s prediction markets vs. other corporate prediction markets

After presenting the findings of the Google prediction markets, the thesis now turns to a comparison of Google's prediction markets with prediction markets of several other companies. Policymakers and the business world are being pretty slow to adopt and regularly use the prediction markets mechanism, having in mind the recent rise in popularity of such forecasting mechanism. Correspondingly, there are only a few relevant scientific pieces of research available concerning the corporate application field. One of the leading causes for such scarcity are the companies themselves since they treat their prediction markets as a business secret and only a few individuals have access to the resulting findings.

This section of the master thesis represents a direct comparison of previously presented Google prediction markets, prediction markets of Ford Motor Company, and prediction markets of a large privately held basic materials and energy conglomerate, whose name had to stay unknown out of confidentiality reasons. The conglomerate will be further referred to as Firm X. Scientific paper “Corporate Prediction Markets: Evidence from Google, Ford and Firm X”102 serves as the primary source of information for this section of the master thesis. The three companies and their prediction markets were chosen because these prediction markets were amongst the most extensive known corporate prediction markets until the point of creating the mentioned scientific work. The diversity of the included prediction markets personifies the way in which the business world has been utilizing this forecasting tool.

Contrary to Google prediction markets, prediction markets at Ford Motor Company focused solely on two crucial issues: weekly sales volumes and car features which were (should be) popular with customers. The main activities of the Firm X include refinement of crude oil, manufacturing of chemicals, building materials, paper products, and synthetic fibers. Most of such activities are very responsive to the changes in macroeconomic developments as well as to changes in commodity prices relevant to its business. Correspondingly, such causality resulted in many different prediction markets questions (securities). For the purpose of

102 Cowgill/Zitzewitz (2015) 59 summarizing the comparison between the three companies and prediction markets deployed at each of them, following Table 11 with its clear descriptions will be used.

Google Ford X Industry Software/Internet Automobile Basic materials Ownership Public (Ticker: GOOG) Public (Ticker: F) Private Sample begin April 2005 May 2010 March 2008 Sample end September 2007 December 2010 January 2013 Markets (questions) 270 101 1.345 Securities (answers) 1.116 17 4.278 Trades 70.706 3.262 12.655 Unique traders 1.465 294 57 Market mechanism IEM-style CDA LMSR LMSR Software Internally developed Inkling Inkling Style of market (%) One continuous outcome (e.g. how many F-150s sold?) 100% 1,3% One binary outcome (e.g. Project X done by September 30?) 59% Two outcomes (e.g. Yes and No securities) 29% 0,7% 3+ outcomes (e.g. bins) 71% 39% Topic of market (%) Demand forecasting 20% 100% Project completion 15% Product quality 10% External news 19% 96% Decision 2% Fun 33% 4% Share for which optimism can be signed (%) 58% 100% 71% Table 11: Direct comparison of the prediction markets in all three companies - summary statistics 103

103 Cowgill/Zitzewitz (2015) 60 The attention will be immediately devoted to the essential remarks which can be made while looking at Table 11. The first thing that stands out is the sample period, which is different for all three companies. This should, however, have no specific impact on the comparison…these are different companies from different business sectors with prediction markets tailored to the needs of every single one of them.

4.3.2.1 Trading mechanism

The trading mechanism differs going across all three companies. Contrary to Google, Ford Motor Company and Firm X both used the same prediction markets software developed by the external company.104 The difference to the continuous double auction is that such software uses an automated market maker in the form of the logarithmic market scoring rule which ensures that traders have the possibility to place their bets continually. Such a feature enables liquidity in the case of limited participation. Other noticeable difference concerning the trading mechanisms is the design of the securities traded. As already noted in the section of the master thesis devoted to Google, the company used double bins for the discrete outcomes (e.g., deadlines) and three to five bins for the continuous outcomes (e.g., demand, sales). On the other side, both the Ford Motor Company and the Firm X used single bin security for the discrete outcomes. In addition to that, the Ford Motor Company used securities with a linear payoff for the continuous outcomes whereas the Firm X used either single binary security combined with an “over/under” threshold or multiple bins security for the continuous outcomes. A little discourse concerning the choice between single bin securities and double bins securities in the case of discrete outcomes, remark also made by the authors of the underlying scientific paper, could be now useful. The choice between single bin security and double bin security belongs to the well-known framing effect, meaning that the way a specific problem is presented has in most cases a strong influence on the decision making. One important study provided some relevant evidence that the choice of single binary securities can potentially lead to short aversion (i.e., subject prefers to take or keep the long position in a security rather than the short one).105 This can furthermore result in the occurrence of the market inefficiency. The particular study found that a typical assessor begins with an ignorance prior distribution that assigns equal probabilities to all specified outcomes. The assessor than fails to correctly adjust those probabilities according to the new information and resulting likelihoods afterward. More

104 http://inklingmarkets.com, [State: 15.03.2018] 105 Cf. Fox/Clemen (2005), qtd. in Cowgill/Zitzewitz (2015) 61 concretely, they found proof for partition dependence bias in the case of discrete outcomes which is a specific case of the more general framing effect.

4.3.2.2 Participants

Table 11 shows that the number of participants who took part in the Firm’s X prediction markets is considerably lower than the number of participants in the other two companies. The company restricted the participation in the corresponding prediction markets only to employees with relevant expertise, which was not the case at other two companies. However, the response of the participants at the Firm X was almost 100% and the participants placed on average 220 trades (48 at Google and 10 at Ford). This is an important distinction which explains the suitability of prediction markets at Firm X for this comparison. The Google company has the longest history of running prediction markets in the corporate world. The company sees the prediction markets as a tool which ensures and improves the two-way communication between the management and the lower ranked employees considerably. Prediction markets became an integral part of the company’s corporate culture, and that is the main reason for such a high response of participants. The Ford Motor Company has a long history of hiring experts for conducting various types of forecasts and market researches. Since such a method predominantly relies on the judgment of very few individuals, the company decided to implement the cloud-based tool of the Inkling company in 2009 in order to help to mitigate operational risks. According to David Needle from Ford, in 2011 more than 1.300 Ford employees in the United States and Europe have been taking part in the stock market-like trading.106

4.3.2.3 Incentives and payment schemes

All three companies provided relatively modest incentives for trading at their prediction markets, with incentives at Google being the highest. The reasons for that could be connected to the uncertain success of the markets, prestige of the traders, and legal as well as regulatory matters. Traders were endowed with equal amounts of artificial currency at the beginning of every quarter, and the end of each quarter this currency was converted into the raffle tickets for every trader who placed at least one trade linearly. The prize budget was about $25-100 per active trader, and the linear method of conversion into the raffle tickets created the possibility that even a poorly performing trader may win a prize. Awarding the prizes only to the most

106 https://www.hpcwire.com/2011/02/22/ford_motor_company_turns_to_cloud- based_prediction_market_software/, [State: 15.03.2018] 62 successful ones of traders would create convex incentives and probably make low-priced securities excessively attractive, consequently distorting the prices.107 In addition to that, Google also used league tables as reputational incentives which ranked the most successful traders. Incentives at the Ford Motor Company consisted of several $100 gift certificates. Ford also used a lottery that created incentives linear in the currency deployed in the market. Company X did not use standard monetary incentives. However, the extremely high response rate of the traders indicates that management probably emphasized the importance of prediction markets. The company publicized the most successful traders. It is possible, however, that such reputational incentives could have encouraged a risk-taking behavior and thus caused a convexity of the performance.108

4.3.2.4 Findings and performance

Cowgill and Zitzewitz (2015) conducted a serious of statistical tests in order to examine how efficient the prediction markets of these three companies were, whether they were producing predictions which were an improvement in comparison with contemporaneous experts and whether these prediction markets were free of biases. Researchers were examining whether the prediction markets yielded predictable returns (or whether forecasting errors were foreseeable at the time of the forecast) which would indicate a market inefficiency. They found that Google's and Firm X’s features markets were approximately well calibrated with both markets exhibiting apparent underpricing of securities with prices below 0.2 bin and an overpricing of securities above that level. The thesis already referred to such findings as the reverse favorite- longshot bias in the section devoted to Google's prediction markets. In the case of Ford Motor Company's prediction markets, which were all sales markets, the researchers found that they were well calibrated with maybe a small indication of the optimistic bias. In general, the statistical analysis indicated that features markets of the Firm X and Google, together with sales markets at Ford Motor Company, could be considered to be reasonably well calibrated. All these markets had prices that were positively correlated with the outcomes. The findings that are more relevant for the central topic of this master thesis were dealing with the question whether prediction markets, as a newly integrated forecasting system, show better results than the previously accustomed mechanisms in the form of expert and manager opinions. It was already pointed out that Ford Motor Company has a long history of using expert opinions on the weekly basis. Hence the comparison of expert opinions with prediction markets results

107 Cowgill/Zitzewitz (2015) 108 Cowgill/Zitzewitz (2015) 63 represents a purposeful next step. In the case of Google and the Company X, the expert forecasts were used to design the securities (percentile forecasts derived from bin boundaries made by experts, further used in constructing the prediction markets securities). The aim was to compare projections which were as contemporaneous as possible. In the case of Ford Motor Company, this meant the comparison of every expert forecast and prediction markets forecast which was obtained just before the respective expert forecast. In the case of Google and Firm X, this necessitated the comparison of an expert forecast and the first forecast of the prediction market made on the particular security. The following Table 12 represents the main features and results of these comparisons.

Ford Google X Market type 1 continuous 3-5 bins 3-10 bins 1 binary outcome outcome Expert forecast Expert Derived Derived Contract source over/under Topic Auto sales Demand Macro Macro numbers numbers Timing of PM Just before expert First day of First day of First day of PM forecast PM PM

PM forecast 0,67 0,82 1,01 1,16 (0,10) (0,14) (0,19) (0,19) Expert forecast 0,38 0,09 0,11 0,27 (0,08) (0,58) (0,57) (0,17)

Observations 78 197 1330 748 Unique markets 6 191 185 296 Time periods 13 30 45 58 MSE (PM/expert) 0,742 0,727 0,924 0,908 P-value of 0,104 0,00004 0,002 0,002 difference with one

Table 12: Prediction markets and experts in direct comparison 109

Table 12 above summarizes the regressions of the outcome on the forecasts obtained by prediction markets and experts (i.e., regressions of the security pay-offs on the prediction

109 Cowgill/Zitzewitz (2015) 64 markets and experts forecasts) at all three companies. The results clearly show that forecasts produced by prediction markets have a lower mean-squared error than the ones created by experts in every single case. The resulting coefficients also indicate the same conclusion. Standard errors The closer they are to one, the higher the weight they have, the more precise the forecast is. Standard errors are displayed in parentheses. Additionally, the f-test p-values for the equivalence of the two variances are displayed. Even though the expert forecasts at Ford have a long history and formal status, the improvement achieved by the prediction market of the company was among the greatest. Moreover, the p-value for the statistical significance of the improvement was largest at Ford. However, the authors pointed out that this should be related to the much smaller sample size at the Ford Motor Company.110

4.3.3 Corporate prediction markets in Austria: Siemens Austria

Prediction markets at Siemens department for software and system development in Austria represent the first corporate (industrial) application of stock market-like prediction markets. Following the initial insights from the Iowa Electronic Markets, professor Gerhard Ortner from the Department of Managerial Economics and Industrial Organization at the University of Technology in Vienna wanted to exploit the capabilities of such information gathering mechanism in the corporate surroundings. Professor and his team came in contact with the people from Siemens Austria by coincidence. Siemens people thought of some new possibilities of improving internal management skills and were interested in forecasting events with the help of market mechanism. At the beginning of April 1997, Siemens launched the original project with the purpose of forecasting some Siemens-related important events and consequently supporting project management decisions.111

4.3.3.1 Trading mechanism

Siemens Austria decided to take part in such experiment since one of the critical problems in the project management represents controlling of already started projects and forecasting the completion of the current and upcoming ones. The management, in general, needs to gather as much as possible up-to-date information concerning the progress of current projects and preparations and planning for the upcoming ones. Big corporations have been using several project management tools and methodologies in order to gather such relevant information. Milestone trend analysis and Agile represent the best-known project management tool and

110 Cowgill/Zitzewitz (2015) 111 Ortner (1997) 65 methodology respectively. Milestone trend analysis (MTA)112 is one of the most used techniques in controlling the schedule of the project. It is a tool which enables the project team to approximately be aware of whether the work corresponding to certain project milestones is ahead of, on, or behind schedule. One of the most important features of this technique is the absence of anonymity meaning that every informational input is directly trackable to its originator. Agile113, similarly, represents a group of software development methodologies which are based on iterative development. Such continuously updated solutions evolve through collaboration between different cross-functional teams. Agile methodologies help in the general organization of disciplined project management. On the other side, being able to provide management with anonymous predictions as quickly as possible is one of the most significant advantages of prediction markets in comparison to other forecasting mechanisms. The trading mechanism of prediction markets at Siemens Austria had the form of the double auction market and was fully computerized. Prediction markets mechanism was implemented into the intranet server of the company and was using the software product FX developed by Kumo Inc.114 Initial testing and the preparation of material was done in the lab of the Department of Managerial Economics and Industrial Organization at Vienna University of Technology. Two separate markets were designed at the beginning. One market was a simple payoff version, and the other market was a version with the more complicated payoff rule:

1. Market (“B500”) consisted of a straightforward question which represented the winner-take-all security: “Can the project be finished in the planned time horizon?”. YES and NO securities were available for trade. 2. Market (“Verzug”) was designed in order to predict a possible delay with the help of Early and Late securities, which had the linear specification of the payoff.

The payoffs of the two traded securities had the following shapes and are displayed by Figure 11:

 “Early” (YES: max (1-0,2*weeks late, 0) ATS  “Late” (NO) security: min (0,2*weeks late, 1) ATS

112 http://www.project-management-knowhow.com/milestone_trend_analysis.html, [State: 20.03.2018] 113 https://www.cprime.com/resources/what-is-agile-what-is-scrum/, [State: 20.03.2018] 114 https://www.kumo.com, [State:20.03.2018] 66

Figure 11: Payoff rules of the securities in the “Verzug” market 115

4.3.3.2 Participants

Approximately 60 traders took part in first prediction markets at Siemens Austria. 88,1 percent of the participants were male participants, 67 percent of all participants turned out to be developers. When asked whether they believe that a prediction markets tool could be a reliable new tool in project management, participants with and without trading experience did not significantly differ in opinion and gave answers which were moderately affirmative. Potential participants were all employees who were working on the respective project, except the management. This case was a classic example of the closed type of prediction markets in which the majority of the relevant knowledge was distributed amongst the employees of the company. Markets were opened from May the 12th 1997 until early October 1997. The number of employees who actively took part in trading steadily rose until the closing of the markets. All participants got the chance to make themselves familiar with the entire trading procedure and its goals before the markets opened.116

4.3.3.3 Incentives and payment schemes

Prediction markets at Siemens Austria were organized as the real-money markets. Every participant who was willing to invest 100 Austrian Schillings (ATS) of her/his own money got an additional 200 Austrian Schillings from the Siemens Quality Management Division. This fact served as an additional motivation for participation. The participants were eventually

115 Ortner (1997) 116 Ortner (1997) 67 rewarded according to the final balance of their accounts. It is important to notice that Siemens Austria used a different incentive scheme than the likes of Google, Ford Motor Company and Firm X when talking about their prediction markets. These were the only corporate prediction markets which asked their employees to use their own money.

4.3.3.4 Findings and performance

Professor Ortner and his collaborates found that Siemens prediction markets produced relatively stable predictions after the initial one-month period. Afterward, the predictions showed minimal fluctuations. Even though the “Verzug” markets experienced significantly lower participation than the “B500” market, which was not surprising given the fact that the B500 market is a lot simpler to understand and take part in, the “Verzug” market produced surprisingly accurate and stable predictions. After the initial one-month period and more than three months before the scheduled deadline, the market regularly predicted 2-3 weeks of delay. It was an astonishingly accurate result for such a new instrument, bearing in mind that the real, eventual delay amounted to a thirteen-day time span (eleven working days).117 It can be concluded that the prediction markets framework and the rules under which it operated became clear to traders relatively quickly. Facilitators of this experiment pointed out that various significant events took place during the running phase of prediction markets and that no considerable, direct influence on prediction markets prices was registered. This showed that participants of prediction markets used their knowledge and anticipation on the constant basis, long before such event became public, allowing the resulting prices not to be too affected by such events. The prices included a significant amount of relevant information before events even happened. Siemens and the facilitators of the experiment all hoped for such a feature. The market questionnaire revealed that 45 percent of the participants managed to achieve profits, whereas as much as 86 percent of participants declared their willingness to take part in prediction markets at their company repeatedly. The experiment revealed no days-of-the-week effects, meaning there were no regularities noticed concerning the trade on specific days of the week. When, however, the trading volume was divided into trades per time of the day, some critical regularities were detected.

117 Ortner (1997) 68

Figure 12: Trades at a specific time of the day 118

Figure 12 reveals that the mark of the B500 market changed into M500. A contractor of the software product announced that due to some internal problems an initial deadline set had to be shifted one month into the future. Such an unforeseen incident represented a significant modification of the initial project. It was a trading circumstance under which the markets had to be closed, and respective shares bought back. Within one hour new markets with the marks M500 and M500 Verzug were opened. Based on Figure 12 it can be concluded that the trading distribution peaked in the morning and between 11:00 and 12:00. The facilitators of the experiment refer to such trading as “coffee break trading behavior.” Such occurrence represents valuable evidence that the prediction markets participation did not negatively influence the normal working routine, and that the communication within the participants of the project possibly improved during the break time. Of course, the implementation of prediction markets by Siemens Austria could not have been flawless as even nowadays corporate prediction markets are not. However, the participants suggested a few exciting market improvements at that time. An improved introduction phase, betterments in the communication between the project management and market participants, the possibility to enter or leave the markets during their lifetime, and potential involvement of suppliers and customers into the market were the most interesting ones. When looking back at the other corporate prediction markets included in this master thesis and it can be concluded that the successor-experiments applied some of these suggested improvements into their design. All the companies included in the master thesis introduced better and more detailed sample periods before launching their prediction markets.

118 Ortner (1997) 69 Prediction markets at Google, for example, became an integral part of communication between the management and the lower ranked employees. Companies usually deployed prediction markets which were opened to all the employees and even to some of the company’s counterparts. The first corporate implementation of prediction markets by Siemens Austria can be safely regarded as the groundbreaking experiment which paved the way for all the following prediction markets applications in the corporate surroundings.

4.3.4 Corporate prediction markets in Austria: Mobile phone companies

The second parcel of the Corporate prediction markets in Austria section of the master thesis represents a pretty interesting and unique study concerning the real-world application of prediction markets. The study serves as a relevant material for this master thesis because of two main reasons. The application area of Austria is, of course, the first one. The second reason is the design, or more precisely two exact dimensions of it. This study is the first encountered corporate prediction markets study which uses a combination of company employees and external subjects as active participants. It is also the first corporate prediction markets study included in this master thesis which was not conducted within the headquarters of the corresponding company exclusively. It was a virtual, field experiment conducted under the surveillance of the Institute for Production Management of Wirtschaftsuniversität in Vienna. The study was conducted in the field of telecommunications, and it aimed to try to forecast the market share of new mobile phone contracts for all active mobile phone companies on the Austrian market at the time. The study took place over the time span from April 2008 until September 2008. Straightforward index contracts were traded in the market.

4.3.4.1 Trading mechanism

Facilitators of this experiment wanted to avoid the use of the automated market makers because of the well-known negatives they possess. That is the main reason why they opted to use the combination of corporate (internal) and external traders and to inspect the accuracy of such combination. This left the use of the continuous double auction as the obvious choice. The market was open twenty-four hours per day with no short selling allowed, and no transaction costs. Each share could have been traded within the range of 0.10-100. Traders were also allowed to trade a bundle of shares which consisted of one piece of each tradable share for the value of 100 with the bank at any time. This trading feature closely resembles the unit portfolio feature of the political Iowa Electronic Markets. Such characteristic positively influences the liquidity and aims at taming of market manipulation.

70 4.3.4.2 Participants

The total of 36 subjects took part in the project. 28 of them were employed by mobile phone companies and were considered to be experts in the mobile phone industry. They were all sales and call center managers. Since the goal of the inclusion of the remaining eight traders was to eliminate the need for the automated market maker and provide liquidity, they all had to be familiar with the trading mechanics of stock and prediction markets. External traders were also informed about their role. This was a tricky decision made by facilitators which could have easily influence their trading behavior.

4.3.4.3 Incentives and payment schemes

Every participant was endowed with 50,000 in play money and 500 pieces of each share tradable in the market. These 500 pieces of each share also amounted to 50,000. A lucrative part of the market was set at €10,000, an amount which was distributed equally over the whole monthly time span of the market. Every single trader that managed to achieve some profit within one month was entitled to receive a proportion of the total prize money, relative to his earnings in the respective month. The payment of the prizes occurred in the middle of the following month.

4.3.4.4 Findings and performance

The goal of the study by Waitz & Mild (2013) was to examine whether its innovative design resulted in a liquid prediction market with efficient and accurate forecasts. The efficiency and the accuracy of a particular market are hard to judge in the case of absence of comparative methods. This is why a team of market analysts was asked to make a forecast for each of the six shares upon request of the facilitators of the market in the last month of its operating. Encouragingly, the prediction market produced better results than the expert team. The expert team was wrong for four out of six shares, and quite significantly in two cases. Prediction market results were wrong for three out of 6 shares, also in two cases to a significant extent. The efficiency of the market had to be validated differently. In the respective prediction market, the actual values of all the shares had to sum up to 100. According to the starting incentives scheme, this would mean that the sum of the shares on the market also had to be 100. If this was not the case, the predictions would automatically be imprecise. The two-sided statistical t- test showed that the market was inefficient, but this was mainly due to the unit portfolio rule which was explained earlier. The bank of the market had a continuously variable number of shares in its depot. However, when looking at the extent of these inefficiencies, they accounted

71 for less than a fourth of the total absolute errors made by the market. This is why the facilitators of the experiment concluded that the liquidity of the market was not an acute issue. The experiment produced positive results. Scientific and business worlds are never going to be able to produce a perfect forecasting tool. In this case, the uncertainty over future events would see to exist. The field of prediction markets aims to collect the dispersed knowledge and maybe even more significantly, to improve upon every other forecasting tool present at the particular time. It is why this experiment can be considered to be a valuable contribution to this particular scientific field. 4.4 Prediction markets as an idea generation tool

Even though this part of the master thesis could have been easily included in the corporate prediction markets section 4.3, it will be ascribed to an entirely new section, since the idea generation potential of prediction markets exceeds the corporate application alone by far. It is beyond any doubt that the idea generation process became critical in today’s time of fast- evolving markets and demanding customers. The common sense properties of such information aggregation mechanism (IAM)119 indicate that it could be deployed successfully in many other biotopes, including social welfare, environment, public safety, geopolitics as well as in various spiritual and religious aspects of life. Many of these examples represent a long shot currently, but also a possibility which the society, in general, should seek to materialize. One of the best examples of the claims as mentioned earlier are the findings from the famous scientific paper called “Orange Juice and Weather.”120 The author of the respective paper found out that the value of the orange juice produced in Florida directly related to the cold weather in the region. After examining the futures contract prices of the concentrated orange juice, the paper found substantial proof that the National Weather Service’s temperature forecast should be adjusted downward. It also suggests that the more accurate prediction of the freeze could be obtained, in the case when the closing futures price finds itself above its opening price. Even though there are significant concerns about the degree to which the prediction markets prices can be manipulated, the introduction part of the master thesis showed that any attempt to implement such strategy would encounter many difficulties to succeed. Some researchers found enough proof for arguing that prediction markets could be used for setting the monetary policy.121

119 Chen/Plott (2002) 120 Roll (1984) 121 Cf. Hahn/Tetlock (2005), qtd. in Deck/Porter (2013) 72 However, it is evident that prediction markets in the role of an idea generation tool excelled the most in the corporate environment. It is often an ongoing challenge for companies to synthesize insights from numerous resources, as this is an essential part of idea screening and idea creation processes within every company. Such operations are today popularly called the Fuzzy Front End. There is no universally accepted definition of the Fuzzy Front End. It can be, however, referred to as the process for the insight-driven and foresight-inspired search of new ideas that can be later applied to products, services, and business strategies.122 Creating new, original concepts through the intent of finding market gaps from both the company’s and customers point of view, is what defines the Fuzzy Front End process. It would seem logical that companies use the knowledge of their employees as the primary input of such process, surprisingly, however, the findings clearly show that only very few of them do.123 There are many different reasons behind this, with the lack of motivation, lack of a formal mechanism for sharing and selecting ideas, and the lack of a cost-efficient tool for choosing the most promising ideas, being the most common ones. The Fuzzy Front End represents, therefore, a part of information aggregation process which challenges the companies to develop a mechanism which could exploit the intra-company resources in the most effective way. The development of the internet brought with itself some exciting new technologies which try to make use of the interconnectivity and interactivity characteristics of the Internet. Some of these technologies also seek to exploit the widely distributed knowledge within various subjects and employees. Such technologies represent the predecessors of modern idea-generating prediction markets, with the most noticeable ones being: open-innovation initiatives124, innovation contests125, and Internet-based innovation communities126. Idea-generating prediction markets improve upon all these techniques. They combine the features of all previously mentioned techniques with the ones of the financial markets. As already thoroughly elaborated, three crucial aspects of financial markets are responsible for their efficiency:

1. Profit incentives 2. Information disclosure incentives 3. Prices as effective information aggregators

122 http://www.product-arts.com/articlelink/461-the-fuzzy-front-end-unfuzzied, [State: 26.03.2018] 123 Cf. van Dijk/van den Ende (2002), qtd. in Soukhoroukova/Spann/Skiera (2012) 124 Cf. Chesbrough (2003), qtd. in Soukhoroukova/Spann/Skiera (2012) 125 Cf. Terwiesch/Xu (2008), qtd. in Soukhoroukova/Spann/Skiera (2012) 126 Cf. Franke/Keiz/Schreier (2008), qtd. in Soukhoroukova/Spann/Skiera (2012) 73 Now, both prediction markets and idea markets rely heavily on the role of Hayek’s institutionalized price system. However, the role of the consumers of these markets (participants as well as operators) must not be neglected. The spatial agent-based model, developed by Chie and Chen (2014), elaborates on the role of individuals within such information aggregation mechanisms by defining two essential capacities, the tolerance capacity and the exploration capacity. The tolerance capacity represents a clustering phenomenon driven by different identities and is very similar to the herding or the agreeableness bias. The exploration capacity practically determines how much customers endeavor to know. When both of these capacities are managed and exploited effectively, the results on both micro and macro level are likely to become improved. One of the objectives of idea markets is to serve the exact purpose. A general idea behind the idea-generating prediction markets concept is that it possesses the theoretical and practical foundations for efficient sourcing and evaluation of ideas. Such prediction markets are known today as the idea markets. The primary target of such markets is to activate the innovative potential of the companies by facilitating the creativity in the whole business chain they are a part of. However, the idea markets concept is not to be considered as identical to the prediction markets concept as there are two noticeable differences:

1. The number of stocks tradeable in prediction markets is always strictly determined by the facilitator of the market. The number of stocks tradeable in an idea market is contingent upon the number of new suggestions made by participants and is theoretically unlimited. 2. Time horizon. Prediction markets are always resolved according to the outcome of a near-future outcome. Idea markets underlying events can take up to several decades in order to result in a concrete outcome.127

Before the master thesis goes into a more specific explanation, following depiction will be used as a general presentation of an idea-markets mechanism.

127 http://ideosphere.com, [State: 27.03.2018] 74

Figure 13: Idea markets concept 128

Figure 13 represents the mechanism which was applied as part of the particular empirical study in a real-world setting, facilitated by Soukhoroukova et al. (2012). The mechanism has a clear and distinct way of working and will be thus used as a general explanation of the mechanics of idea markets, or more precisely, mechanics of idea sourcing, idea filtering, and idea evaluation stages of the innovation process. Employers can suggest any product or technology idea as long as it is original and new to their company. Every such suggestion becomes an idea stock candidate, offered at a uniform price in a virtual or real-world currency. During some subsequent specified period, every idea stock candidate has a chance of becoming a real idea stock available for trading in the idea market if it reaches the specified threshold value. This specific value should be of course adjusted, depending on the number of market participants. Same as in the prediction markets, idea markets can later use different trading and reward mechanisms, according to the results of participants at the market close. Idea stocks with the highest virtual stock prices are to be interpreted as the most promising ideas among idea markets participants. Advantages of idea markets mechanism within companies are numerous. Even though such mechanism cannot guarantee the success of the idea produced since a result depends on various factors outside the company, it certainly makes sure that the knowledge of every employee has an equal chance to be appropriately and successfully utilized. Idea markets can also help to avoid the well-known problems resulting from hierarchical and corporate governance structures within every company.

128 Own depiction based on Soukhoroukova/Spann/Skiera (2012) 75 4.4.1 Empirical studies

Soukhoroukova, Spann, and Skiera wrote and published a scientific paper in 2012 based on the results of one of the first real-world implementations of the idea markets mechanism. The authors of this paper joined their forces with a large technology company whose name had to stay unknown out of confidentiality reasons. According to the authors, it was a USA-based company with revenues of more than $3 billion in 2010 and 80 percent of earnings coming from outside of its home country at the time. This particular idea market focused on three main categories which had to be predicted:

1. Technological forecasting 2. Specific new product ideas 3. General new business and product ideas

The three categories were chosen on a request of the collaborating company. The first category idea stock numbers aimed to predict an estimated percentage of revenues that would be influenced by the technology in the following ten years. The second category numbers aimed to predict the number of units of the product that would sell in 10 years. The third category numbers aimed to predict the best new product and business ideas, where the ten best ideas were to be rewarded with 100 units of virtual currency, and with 0 units of virtual currency otherwise.

4.4.1.1 Trading mechanism

Every idea stock had a uniform price of 5 units of virtual currency. Every other subsequent price for each share of stock was calculated with the help of an automated market maker, which ensured the constant liquidity state of the idea market. Exact specifications of the trading mechanism were not made available by the authors. It is known, however, that it was a Web- applied software which was already used in several earlier projects. The whole idea market used a company’s intranet.

4.4.1.2 Participants

Every employee of the company was able to take part in an idea market, meaning that the workers from 100 different countries were eligible for participation. The user interface was offered in both German and English. Explanation of the idea markets mechanics, as well as the manual, appeared in the corporate monthly newsletter and on the Web site of the idea market. E-mails were also sent to employees, and even flyers were used in order for blue-collar workers

76 to be reached. 576 participants took part in the idea market, with 86% coming from the USA. The remaining 14 percent came from 16 different countries. 157 participants on average actively traded in the idea markets on work days.

4.4.1.3 Incentives and payment schemes

Each registered trader started his trading spell with ten thousand units of virtual currency. At the market close, ten best traders were awarded the total of three thousand US dollars, with the best one receiving one thousand five hundred US dollars. Some additional real-money incentives were also used, as the facilitators thought that the combination of virtual and real- money incentives would provide the best results. We witnessed a similar custom with the Siemens Austria prediction markets. Every submitter of an idea stock received $12 with first 25 submitters receiving $30. The real-money vs. virtual money dilemma will remain one of the most critical problems of prediction markets' scientific field. The facilitators of this particular idea markets opted for one exciting solution, even from the future prediction markets point of view.

4.4.1.4 Findings and performance

Time horizon of idea markets success evaluation differs significantly in comparison with the time horizon of prediction markets success evaluation. Because of this fact, some different unorthodox categories of criteria had to be used to evaluate the success of idea markets. Facilitators of this particular idea market used, therefore, following four categories of criteria in order to evaluate the success of the respective idea market:129

1. Acceptance of the idea market – this subcategory comprises a number of participants, number of idea stocks, number of trades. 2. Quality of the idea sourcing and filtering – management and participant surveys. 3. Quality of the idea evaluation – a number of trades per idea stock, participant and management surveys. 4. The overall performance of the idea market – consensus with experts, interest for new product development (participant survey), repetition of the idea market (management survey).

An outside expert committee was used to determine the cash dividends of idea stocks because of the herding reasons. The committee consisted of four members who were not the company’s

129 Soukhoroukova/Spann/Skiera (2012) 77 employees. Two of them were research & development directors of technological companies, one was a director of a major consulting company, and one was a CEO of a venture capital company. The facilitators made sure that participants do not know the members of the committee. A committee was making individual as well as group evaluation of cash dividend for each idea stock on a daily basis. Since the determination of the success of new product ideas takes years, their commercial success in the form of specific quantitative data could not have been used as the measure of their quality. Personal evaluations of participants and managers had to be used instead. They used, for example, a survey of 25 senior managers one week before the market close with the aim of avoiding the influence of the committee on respective managers. Participants were surveyed two weeks after the end of the idea market. The idea submission period lasted 24 days out of 36 active market days and produced the total of 252 unique ideas. Most of the idea proposals were in the form of general descriptions with a maximum length of two pages. Initial ten days of the trading produced 77,1 percent of all ideas, probably indicating an initial enthusiasm evoked by the new forecasting platform. Participant surveys revealed that more than a half of the idea submitters had never suggested or being asked of a product idea before. Such findings speak strongly in favor of the idea markets implementation and were one of the main goals of the idea markets facilitators. Survey also revealed that 89 percent of the participants intended to take part in an idea market again as the majority of them described the idea market as being fun. When thinking about the general design of such an idea market, one has to take potential biasedness of participants into account as they could focus their trading on their idea stocks. Few things mitigate such a problem. Not every trader is an idea submitter. In this particular idea market, there were at least three times more unique traders than idea submitters. Trading restrictions could also neutralize such difficulties. The closing idea market results showed eventually that only 14.1 percent of submitters purchase orders and 12.5 percent of their sales orders were related to their own idea stocks. Such problems could, however, cause more difficulties in an idea market of a lower order of magnitude, as the smaller number of participants would automatically imply the higher probability that a particular trader trades his own idea stock. This is indeed something to keep in mind for future developments. The general findings of the idea market implied that it significantly improved the idea generation process regarding participation and idea evaluation, but the consensus among the evaluations of the idea market senior management, expert committee, and individual participants was only moderate. Findings regarding the consensus are not to be considered as a surprise since the success of every new product depends heavily on numerous financial, behavioral and time-horizon factors which are almost impossible to

78 predict. Many researchers report that the commonly observed failure rates in this field amount to more than 50 percent, indicating an intrinsically high level of uncertainty related with the idea generation processes.130 Even though connected with the considerable uncertainty of the commercial success, idea markets mechanism could be considered as a great tool in collecting dispersed knowledge among the members of one organization. If further appropriately developed, this tool could result in great success in the field of product development. It may never be a revolutionary lucrative forecasting weapon, but a tool that helps to listen to people’s needs.

4.4.2 The Tech Buzz Game

The activities related to the general idea generation process earned in today’s business, as well as the scientific world the nickname the Fuzzy Front End. It is because everything related to the creation of new concepts and products intrinsically possesses an uncertain nature and is very hard to predict. Since the whole process is so mystified and vague, corporate surroundings started calling upon prediction markets mechanism in order to at least try to transform the uncertainty of the idea generation process into the category of risk. Even though there is still no universally accepted definition to be found, some consensus defines it as the genesis of new product concepts and categories which usually address a previously unmet market need. 131 The Fuzzy Front End as such can also be thought of as a highly ingenious stage of a specific development process which could, by considering and combining various company and market drivers, result in the creation of entirely new market needs.

Figure 14: From the Fuzzy Front End until the commercial exploitation 132

130 Cf. Ataman/Mela/van Heerde (2008), qtd. in Soukhoroukova/Spann/Skiera (2012) 131 http://www.product-arts.com/articlelink/461-the-fuzzy-front-end-unfuzzied, [State:15.04.2018] 132 Own depiction 79 Figure 14 intuitively hints at what The Fuzzy Front End represents. The Fuzzy Front End represents the very first stage of the new product development process. Completely unstructured activities and unpredictable outcomes characterize it. Those unstructured activities contain identification of market and technology gaps, assessment of the opportunities provided by such gaps and creation of various initial concepts. The product development phase represents the new stage which is clearly distinguished by a particular structure whit more or less predictable outcomes. The last phase of the new product development process is, of course, the product itself. The reason the Fuzzy Front End was introduced is that it represents maybe a crucial stage of the idea generation process in general. The main problem with it is that it is too unstructured and unpredictable. The implementation of a prediction markets mechanism into the idea generation process could potentially provide it with some of the necessary structure and predictability.

The Tech Buzz Game represents one of the first known attempts made by praxis in order to provide an idea generation process with some structure and predictability. The Tech Buzz Game was a joint venture project between Yahoo! Research Labs and O’Reilly Media. It was an online fantasy prediction market launched in March 2005 at the technology conference in California with the goal of trying to forecast the high-end technology trends. The category which quantified the interest on a specific trending technology was a buzz, measured by the number of Yahoo! Search users seeking information on it. Such buzz-scoring technology was initially developed for the Yahoo! Buzz Index and tracks the number of Web searches and trends. 133 The objective of this online prediction market was to anticipate the future buzz and according to that trade available stocks. There were many sub-markets available. The Browser war market, as an example, was a market which contained seven different stocks: Internet Explorer, Firefox, Opera, Mozilla, Camino, Konqueror, and Safari.

4.4.2.1 Trading mechanism

The Tech Buzz Game project had two main goals, with one of them being to field test the dynamic pari-mutuel trading mechanism. This mechanism was already explained into detail in the introduction part of the master thesis. NewsFutures developed the software which was powering the prediction market. Participants were only required to enter the amount of money they wanted to invest in specific stock, and the dynamic pari-mutuel software computed how many shares they were able to buy. The same was conducted for sell orders.

133 Mangold/Dooley/Flake/et al. (2005) 80 4.4.2.2 Participants

The crowd of emerging technology conferences is the crowd with significant expertise, great networking, and intense interest. This is why the project enjoyed great response immediately after it was started. The prediction market site received 2.7 million hits and 13.310 created accounts in the first week. It was an open-participation prediction market. Same as it was the case in Google prediction markets, the use of trading bots was allowed.

4.4.2.3 Incentives and payment schemes

The Tech Buzz Game used fantasy dollars as the virtual currency. All paid dividends and final cash settlements were in proportion to the actual search buzz. This meant that every Friday at 6:00 pm, each stock received a total dividend in the vicinity of the stock’s buzz score times 100. For example, if the individual stock buzz score was 50, the total dividend for the stock was $5000. This amount would be then divided among the shareholders of the stock in equal portions. Cash-out events took place at long intervals, with all money being distributed to shareholders in every market. The Tech Buzz Game used initially the dynamic pari-mutuel pricing system which was based on money-ratio price function. It meant that the ratio of any stock prices was always equal to the ratio of money invested in the same stocks. Such function led to a precipitous falling of prices in many markets, arbitrage opportunities and resulted eventually in the change of the price function. This malfunction resulted in the share-ratio money function. This new price function defined the ratio of any two stock prices as always equal to the ratio of outstanding shares of the same two stocks.

4.4.2.4 Findings and performance

Even though no detailed scientific reports were made on the Tech Buzz Game up until today, the underlying paper shows that the platform produced two main findings eventually. The first significant finding was that the money-ratio price function contains flaws which lead to arbitrage opportunities and the degradation of the basic rules of fair trading. The second finding relates to the fact that this experiment was accompanied by a great interest of an online community and provided some great insights into some interesting social phenomena. The experiment proved that online communities play an essential role in today’s world and that people rely heavily on them Human behavior types, human personalities, seem more and more to fall into specific categories. Technology development, modern surveillance systems, and data-aggregation applications offer the possibility of studying human behavior and factors that lead to specific decision-making patterns. Advantages of a tool which would be, based on few

81 recorded actions of the subject, be able to determine the exact type of subject’s personality and with that the mechanics of his decision making, would be beyond our comprehension. It is known for a fact that some people are generous, some are greedy, some are clownish, some are serious, and some are mean. Based on such features, several basic human models that almost everyone recognizes called archetypes were developed. Prediction markets could potentially prove to be extremely useful in this field. Facilitators of the Tech Buzz Game recorded several familiar archetypes. These were the Braggart, the Novice, the Leader and several others.134 This proved that prediction markets represent not only a promising forecasting mechanism but also a suitable framework for conduction of various social experiments.

This experiment, as did many others in this particular scientific field, showed that prediction markets should not be considered exclusively as a forecasting mechanism. Quality networking and leaders with excellence are things which lead to success. However, it became evident that breakthrough ideas and projects require innovation management practices which are profoundly and radically different. It is probably safe to say, that prediction markets could be a tool which offers such capabilities.

4.4.3 Customers involvement in idea prediction markets

Previously analyzed idea markets did not include customer involvement in their design. It is, however, an exciting and tempting option which could potentially improve idea market results and eventually create products which provide greater customer value. From the standpoint of view of a specific company, customers belong to the extern ingredients of their business factualness. As such, they are a factor that is hard to control and even harder to predict. So even though the profit category remains the first and the primary goal throughout the business history, customer satisfaction has to be a close second on that list of priorities. It is because the profit and the customer satisfaction categories almost perfectly correlate with each other. In order for customer satisfaction to grow, customer expectations and behavior have to be predicted accurately. Idea markets are a framework which could help achieve such desired predictability. The previous section of the master thesis showed that involvement of employees in the idea generation process, as well as open-market type idea markets, could use the knowledge of the participants and improve upon previous idea generation results and forecasting concepts. The study called “Damaging brands through market research”135 builds upon previous corresponding researches and tries to examine at which stages of idea generation

134 Mangold/Dooley/Flake/et al. (2005) 135 Horn/Brem/Ivens (2014) 82 process would be possible to include the knowledge of consumers, and how would such involvement influence the brand itself. The stages of every innovation process, in general, look as follows.

Figure 15: Stages of the innovation process 136

Figure 15 above nicely depicts the stages of the innovation process. There are accordingly six crucial stages of every innovation process. The potential involvement of customers could take place in every depicted stage except the research and development (R&D). Research and development are almost always conducted within a company, require specific expertise and are usually extremely confidential. All the other stages could be theoretically improved with the inclusion of the consumers. Crowdsourcing possesses essential advantages in the form of incorporated knowledge and can be crucial in the successful developing of various product specifications such as design and color.137 The newest scientific papers pay attention to the new, expert role of the crowd, which takes place by dint of prediction markets mechanism. It is however clear that the integration of consumers into the idea generation process cannot be a straightforward operation. The causes of several adverse effects could be manifold. Participants can become bored during the process or do not possess enough knowledge to comprehend the mechanics of such a prediction markets mechanism. The final consequences would then be a damaged brand image and a distorted brand awareness. Otherwise, the application of such crowd-based prediction markets would already be a widespread and conventional method. There are accordingly two important aspects which need to be addressed when talking about customer involvement in the innovation process based on prediction markets mechanism:

136 Own depiction based on Horn/Ivens (2015) 137 Soukhoroukova/Spann/Skiera (2012) 83 1. Does customer participation in company’s idea markets influence the customer perception of the corresponding brand? 2. Whether the customer participation in the innovation process (through prediction markets) could end up damaging a brand in any way?

In order to pursue its further goals, the master thesis has to elaborate on the definition of the brand briefly. A brand is a name, symbol, logo, design or image, or any combination of these, which is designed to identify a product or service and distinguish it from those of their competitors.138 Building on this canonic definition, a brand is an entity which offers customers (and other relevant parties) added value over and above its functional performance.139 This also means that branding is closely related to experience. When looking at the second engrossed definition, a logical corollary would be that the design itself and even more so an involvement into the company’s innovation process has to have a particular influence on the brand awareness. As the master thesis already pointed out, the whole innovation process, together with branding process as his integral part, is designed to influence customers in their buying decisions and potentially result in gaining of market share and higher profits. Therefore, in order to successfully tap into and satisfy both symbolical and functional needs of the customers, a specific and unique brand concept is necessary.140 The underlying study of this section of the master thesis tries to find answers to our two primary questions empirically. The answers to these two questions should provide a few pieces necessary to assemble a general picture of customer involvement in the innovation process and idea markets in general. The empirical study was conducted as follows.

4.4.3.1 Trading mechanism

The study consisted of two prediction markets, a short-term and a long-term one, and was conducted in Germany. During both of these prediction markets, the participants could trade shares on the predictions for sports shoes and sports shirts from the leading global sports brand. The software used was provided by Kenforx Corporation and developed by the Karlsruhe Institute of Technology. The core trading mechanism was the continuous double auction. The participants were connected via the highest-speed internet connection, and the network connection was provided by the German National Research and Education Network (DFN). Questions like “What would be the ideal sales price for product X?” were faced by participants.

138 Kotler/Keller/Brady/et al. (2012) 139 Schultz/Barnes/Schultz/Azzaro (2009) 140 Cf. Park/Jaworski/MacInnis (1986), qtd. in Bhat/Reddy (1998) 84 These contracts were developed in cooperation with the management of the sporting goods company as they were considered relevant and gave the study desirable real-world applicability.

4.4.3.2 Participants

There was a total of 116 participants in both prediction markets, and they were predominantly undergraduate and graduate students attending a German university. Since both prediction markets were trying to predict certain aspects of sporting goods, young and mid to highly educated people were considered as ideal participants. The average age of participants was accordingly 24.25 years. At the start of the experiment, participants were divided into five distinctive prediction markets groups randomly, each consisting of 15-23 people. Only the values from participants who completed all three surveys, 54 of them, were included in the quantitative analysis. Participants were not only required to complete all three surveys, but answers also had to be correct and non-contradicting. Before the beginning of the trade, participants received the help in the form of an extensive help section and a video guide. During the trade, traders were the only ones allowed to enter the rooms in which the experiment was conducted.

4.4.3.3 Incentives and payment schemes

This idea markets experiment rests upon play money usage. Every approved prediction markets account received 500 virtual dollars and a certain amount of virtual stocks. Since a virtual money concept always brings with itself the problem of motivation, additional use of other extrinsic motivators has to be considered. This is why researchers included some additional motivators in the form of Amazon gift cards. The maximum prize was a €100 gift card, and there was a total of 45 gift cards to be won. Having in mind the number of prediction market participants, such amount of gift cards seems pretty high. However, such large incentives probably made sure that no liquidity or participation problems occur.

4.4.3.4 Findings and performance

Chronological structure of the tasks of this prediction market was an interesting one as it somewhat differs from the structure of other prediction markets included in this master thesis. These steps were organized in such a way that participants had to start the prediction market participation with the completion of the first survey. Participants then had to end the prediction market participation with the completion of the final, third survey. Also, in between the short- term and the long-term prediction markets, participants had to complete the second survey. T-

85 values for the items were based on the five-point Likert-type scale, with five representing the highest degree of agreement with the corresponding question. Such structure made sure that prediction markets facilitators continuously get real-time feedback on the framework from the participants, and it seemingly made sure that the focus of the participants constantly stayed on the respective market. The previous sections of the master thesis showed that the motivation of prediction market participants represents an essential aspect of this forecasting and idea- generating mechanism, and is often considered to be unsteady with adverse effects on the prediction market accuracy. The experiment resulted in low mean absolute percentage errors across all questions, the fact which is to be regarded as a good performance. The mean and median values obtained for the sports items imply an overall positive brand image, and there was almost no difference in brand perception according to the results of all three surveys. The surveys were so chronologically structured that Survey 1 took place before both the short-term and long-term prediction markets were opened. Survey 2 took place between both prediction markets, and Survey 3 took place after the long-term prediction market was finished. It is safe to conclude that customer participation in respective prediction markets provided no evidence of a change in attitude towards the brand. Therefore, the hypothesis that customer participation in prediction markets will damage the perceived brand image could be refuted by this experiment.

The study above represents an interesting and newsworthy experiment concerning the prediction markets scientific field. It dealt with the aspect of customer participation in prediction markets, something which had not been previously done. It also provided some valuable findings to the scientific field. Even more importantly, the study was able to closely link up the real-world applications with a theory, something which very few experiments were successful at. Customer knowledge and their constant and active involvement in prediction markets could prove to be decisive for the future of corporate forecasting industry. 4.5 Other utilization possibilities of prediction markets

A large part of this master thesis dealt with the well-known and established prediction markets application fields. Consequently, various approaches, experiences, and application attempts available throughout the academia were analyzed. The nature of the prediction markets mechanism is however such that previously quoted application fields represent only a minor part of its potential application scope. The following section will try to portray this diversified spectrum.

86 4.5.1 Negotiations

Whether we are talking about important geopolitics affairs, climate change, education or something else, negotiations represent an integral part and a deciding factor of the respective outcomes. However, a crucial problem of negotiations, especially of ones on the international level, is that the outcome depends on various macro- and micro–level factors which are accordingly extremely hard to predict. Climate change is the pons asinorum141 of the humankind. Numerous recent events, including the USA attempt to quit the Paris climate agreement, amplify even further the magnitude of this issue.142

One such affair was the Copenhagen climate summit in 2009. The 2009 Copenhagen climate summit was held as a part of the United Nations framework convention on climate change. The outcome of this convention was dependent on more than 180 countries who took part, which made it increasingly hard to predict. Since the outcome of the summit should have been legally binding, and since the negotiations included discussion about substantial financial commitments and aids, it was in the best interest of all parties included to become capable of predicting the outcomes confidently. One of the main postulates of prediction markets is that the relevant knowledge is dispersed among many forces. This is how the idea of prediction markets implementation came up. The Copenhagen prediction market ran for as long as the summit itself, which was two weeks. The market consisted of 17 different markets which included reductions targets for various countries, long-term stabilization goals, level of funding provided by developed countries and others. Prediction markets facilitators used continuous double auction as the trading mechanism. All the contracts were the winner-take-all type of contracts. There were no transaction costs and trading restrictions imposed. Participation was open to the general public because of liquidity concerns and participants were recruited verbally, with the help of flyers, and online. A total of 113 participants took part in Copenhagen prediction markets. Play money was used in order to avoid legal complications with each participant receiving 5000 units of play money and 50 market bundles (one unit of each share traded in the market) in each market entered. The top three traders in each market were rewarded with a certain number of carbon credits143 to offset 12, 4 and 2 months of personal emission.144

141 A problem that severely tests the ability of unexperienced person 142 https://www.whitehouse.gov/briefings-statements/statement-president-trump-paris- climate-accord/, [State:06.05.2018] 143 https://www.investopedia.com/terms/c/carbon_credit.asp, [State:06.05.2018] 144 Betz/Cludius/Twomey (2014) 87 The accuracy of every market included in Copenhagen prediction market was judged based on whether the share that traded the highest at the market close matched the real outcome of the Copenhagen summit. Coarsely judged, eight markets forecasted the right outcome and seven predicted the wrong one. Two markets provided no relevant results since one was undecided, and the summit produced no outcome for the other one. The Copenhagen prediction market was an interesting experiment which faced many problems. The main issue with such an innovative type of prediction market was the way its performance would be judged, as there are now benchmark results available for comparison. This is why the panel of experts, including lawyers and climate policy experts, had to be called upon in 13 out of 17 markets available. There is also the question of the negotiation framework or negotiation technique because the corresponding choice can have a significant impact both on the real-world outcome of the negotiations as well as on the performance of the implemented prediction market. It can be stated that this particular prediction market did not give enough time to participants to think about potential participation, to prepare themselves for that same participation, and most importantly, the facilitators of the prediction market were not able to ensure the focus and the time devotion of the majority of the participants towards prediction markets. All of the parties attending the Copenhagen summit showed that their main occupation was expectedly the summit itself. This was, of course, the reason the market was opened for the general public. Another critical issue for this market was the design and the formulation of winners-take-all contracts. Some of the markets aimed to predict extraordinarily complex and ambiguous topics which require concrete knowledge. Liquidity concern issue was not avoided either. The facilitators reported that five of the seventeen markets recorded fewer than 16 active traders. The number of participants necessary for a prediction market to produce valid results is yet to be determined. However, one of the aims of almost every prediction market is to gather as much specific knowledge as possible. In order to achieve such goal, the recorded participation almost certainly does not suffice.

Despite its many imperfections and the labyrinthine nature of international negotiations process, the 2009 Copenhagen prediction market showed just a glimpse of prediction markets limitless potential and represented a valuable contribution to the prediction markets scientific field. A very significant role of Copenhagen prediction market was to raise awareness regarding the importance of the carbon credits incentives. Carbon credits are widely regarded as certified emissions reduction certificates (CER). CER is a certificate which is issued every time the United Nations prevents one tone of CO2 equivalent being emitted through carbon projects registered with the Clean Development Mechanism (CDM). In other words, every country who

88 signed the Kyoto Protocol in 1997 was given a certain amount of CERs, representing the annual limit these countries are permitted to emit. It is also known for a fact that the current weather and climate change forecasting rely mostly on complicated climate models run by supercomputers.145 Even though the accuracy of such models has to improve over time inevitably, their inherent inabilities to raise awareness of the humanity regarding the climate change makes them unsuitable to be the single forecasting mechanism in this field. Prediction markets could be an optimal solution to such problems. The emission trading scheme itself could be organized around prediction markets mechanism, as such tool possess the potential to brand trading with certified emissions reductions, improve forecasting, and raise awareness about the importance of the climate change. All of these could prove to be as equally important in regard to the climate change phenomenon. “The secret of getting ahead is getting started.”146

4.5.2 Infectious diseases

Infectious diseases are a significant cause of mortality throughout the world, especially in its least developed parts. The recent outbreak of Ebola virus disease (2014-2016) ravaged significant parts of the African continent and showed how such fast-moving infectious disease could cause damage of unprecedented dimensions.147 It has become clear that the community engagement in the form of surveillance, contact tracing, infection prevention, and case management represents the key to subdue and control future outbreaks successfully. In this context, the forecasting of infectious diseases could play a crucial role. There are several forecasting methods currently employed by health-care workers. These are various statistical approaches including Bayesian networks, mechanistic approaches, agent-based models, and their combinations.148 The obvious issue of all these techniques is that they are based on historical, past data. Public health officials and researchers collect the data which in many cases become irrelevant for clinical purposes as it already became widely available. The fact is that the information about infectious diseases is very widely distributed. Parents can notice some standard symptoms, a school nurse might observe higher-than-expected absence rates, and a clinical microbiologist can obtain some crucial information out of the conducted analysis- reports. The medical community with the World Health Organization at the helm does not have

145 Hsu (2014) 146 Mark Twain 147 http://www.who.int/news-room/fact-sheets/detail/ebola-virus-disease, [State: 18.05.2018] 148 Shaman et al. (2014) 89 a capable system which would be able to successfully aggregate information from all these various sources and produce valuable forecasts.

This is where prediction markets could come into play. Numerous examples throughout this master thesis showed that prices represent great information aggregators. The equally important feature of prediction markets is that they are a real-time mechanism. Such features brought the idea of prediction markets deployment in the medicine into existence. Iowa Influenza Markets represent the first known, successful application of prediction markets mechanism in the world of medicine. 2004-2005 Iowa Influenza Markets opened on September the 20th, 2004. The market aimed at trying to predict the extent of influenza disease in the State of Iowa during the 2004-2005 influenza season. Iowa Influenza Markets lasted 14 weeks. The specific forecasting targets of the markets were the surveillance signals. Five contract types were ranging from no- report contracts until widespread contracts. The whole trading system was the same one described already in the Iowa Electronic Markets chapter of the master thesis. The participation was restricted to registered traders only, whereas the registration was possible by invitation only. The invitations were handed out only to members of the Iowa medical community and others who at the time had the corresponding expertize regarding influenza. It can be stated that the facilitators aimed at gathering as much specialized knowledge as possible. Liquidity concerns were of secondary importance. Iowa Influenza Markets were play-money markets with every new account endowed with the same quantity of play money units. At the markets close, the play money balance of every individual was converted into USA dollars ($) balance at the rate of one to one. The remaining balances were used as grants for the covering of professional and educational expenses. The basis for these Influenza Prediction Markets was the Center for Disease Control and Prevention designation of flu activity on weekly premise. The performance of the first prediction market within the ecosystem of medicine is hard to judge based on the available resources. The Center for Disease Control and Prevention published spatial flu activity data with a temporal component149, whereas the Iowa Influenza Markets official site provides only the price history of all 5 type of contracts traded in the markets.150 The application is nevertheless worth mentioning as it represents the groundbreaking deployment of prediction markets mechanism in the world of medicine.

The following years 2006 and 2007 brought plans for several new infectious-disease prediction markets151. The Center for Disease Control of the USA also tried to promote some innovation

149 https://www.cdc.gov/flu/weekly/usmap.htm, [State: 18.05.2018] 150 https://iemweb.biz.uiowa.edu/OUTBREAK/index.html, [State: 18.05.2018] 151 Polgreen/Nelson/Neuman (2006) 90 in flu activity modeling and forecasting for the 2013/2014 flu season. However, the most significant application of prediction markets in the world of medicine, by all relevant parameters including the size and the duration of the market, represents the Epidemic Prediction Markets of Taiwan in 2010.152 It was a project developed by a collaboration of the Center for Disease Control of Taiwan and the National Chengchi University in Taiwan. The focus of the facilitators of this prediction market lied on the scope of the market and its trading liquidity. This is why the trading mechanism in the form of market scoring rule was used. With Iowa Influenza Markets and Copenhagen Prediction markets, this was not the case, as the corresponding facilitators of these markets opted for continuous double auction mechanism. Participation was granted only to medical professionals from all over Taiwan with 126 members taking part in total. The end statistics of participation showed that most of the participants took part in the target week with 38 percent of total predictions being made. Each participant received 10,000 units of play money for each prediction event, and the final net balance ranked each trader accordingly. Facilitators opted for the mixture of real-money prizes and reputable awards. It is a combination none of the previously analyzed prediction markets in the thesis utilized. At the market close, every month of trading was worth $33 with the most successful of participants receiving rewards of $1000, $666, $333 together with prize certificates. Historically, the Center for Disease Control of Taiwan relied on the average number of disease cases as predictors for the activity of diseases. Hence, these values were used as a benchmark for the comparison with the performance of Epidemic Prediction Markets. The performance was encouraging. Winning ratios were determined by comparing the benchmark values and prediction markets values with the real-life cases. Target week predictions showed that the Epidemic Prediction Markets were more accurate in 701 out of 1,085 prediction events resulting in winning ratio of 64.6 percent. When looking at the cross-analysis of all five diseases, Epidemic Prediction Markets were more accurate than the benchmark values for the prediction of three out of five different diseases. In contrast, predictions of the confirmed cases of dengue fever and the confirmed cases of severe complicated enterovirus infection were better off with the benchmark values of averages. The causes of such findings could be numerous, ranging from the prediction complexity of these two diseases to the lagging nature of their confirmation. Dengue fever and enterovirus infection data must undergo laboratory tests for around one month in order to be confirmed.153 Such lagging period certainly has to result in the slower incorporation of information and predictions with lower accuracy accordingly.

152 Tung/Chou/Lin (2015) 153 Tung/Chou/Lin (2015) 91 Epidemic Predictions Markets in Taiwan produced no perfect results. However, considering the originality of the project and the trickiness of the application field, it achieved encouraging results. Several design aspects, including the benchmark values, could be brought into question. Following the recent capsizing cases of the stock market, finance world and academia gained a tendency towards quantitative models and methods which are far more complex than the simple method of averaging. The master thesis already mentioned that there are no credible proofs which would substantiate such inclination. It has previously been stated that this project stays for the most relevant one in this application field. It is because it lasted 31 weeks, covered five different diseases in seven geographic areas of Taiwan. Epidemic Prediction Markets of Taiwan showed that this forecasting approach could not only be used in predicting of influenza- like diseases, as the previous relevant studies would maybe suggest, but it could also prove to be useful in a broad spectrum of medical problems and infectious diseases.

4.5.3 Scientific progress

“Through chances various, through all vicissitudes, we find our way.” (Virgil, 30-19 BC)

It is the human nature that always strives for more and the scientific advance in this regard represents the embodiment of such covet. It is an irreversible process which will hopefully lead to a better state of our society. The scientific progress, however, cannot be measured exclusively by numbers as it is also a qualitative category, and very likely even more so. Still, some kind of quantification is always desirable. The current scientific world may be hindered by the lack of reproducibility of the statistically significant findings. Reports say that the costs associated with irreproducibility of preclinical research in the USA alone amount to $28 billion a year.154 It shows that the statistical significance is worth almost nothing until a real-world replication confirms it. Even with the objectivity and the approach consistency of the academia put aside, the lack of the mechanism which would be able to identify non-replicable results remains when talking about the fecundity of the scientific world and the perfection of its creative tools. The 2015 study conducted by scientists from various academic institutions across the globe proposes a prediction markets mechanism as the suitable one to mitigate the concern of growing amount of scientific work which is not replicable.155

154 Freedman/Cockburn/Simcoe (2015) 155 Munafo at al. (2015) 92 There are several reports from various fields including medicine and economics which underline concerns about the failed reproducibility attempts of statistically significant results. Even though the number of scientists including Robin Hanson talk about various imperfections of scientific progress and the peculiar tendency of the academia to reward popular, rather than the most credible and promising of researches, the 2015 study is the rare one which deals with the problem of statistical significance and exorbitant money wasting caused by unsuccessful replicability attempts. The experiment conducted was based on 44 scientific studies from the field of psychology. It represents a groundbreaking implementation of prediction markets with the purpose of quantifying the reproducibility of the studies mentioned above. The experiment consisted of two separate prediction markets dealing with 23 and 21 replication studies respectively. Both of the prediction markets lasted two weeks, and participants could place their bets on whether or not the original results would be replicated. The experiment used a web- based market interface with the logarithmic market scoring rule being the trading mechanism in the form of the automated market maker. Winners-take-all types of contracts were used because of the ability to interpret their prices as the predicted market probabilities of the outcome occurring. Prediction markets registered 47 and 45 active participants respectively. These were real-money prediction markets with each registered participant receiving $100 for trading.

The idea of the experiment was to evaluate the performance of the markets based on whether the market prices can be considered informative, whether the market prices can be interpreted as probabilities of replication, and whether the prediction markets outperform the results of pre- trading surveys. Since the success rate of the implemented prediction markets represents the focus of the thesis, it suffices to say that prediction markets performed significantly better than the mentioned pre-trading surveys and that prediction markets correctly predicted the outcome of 29 out of 41 studies replicated, which amounts to 71 percent. The most notable contribution of the underlying research lies in the distinction between estimates of the probability that a published result will be replicated and the probability of a tested hypothesis being true. The probability of a tested hypothesis being true is referred to as the positive predictive value. The mechanical relationship between the prediction market price and prior as well as posterior probabilities of the hypothesis tested looks as follows.

93

Figure 16: Evaluation of the hypothesis process 156

Initial probability of the hypothesis (p0) indicates the theoretical plausibility of the hypothesis. After the initial study results, this prior is being updated to a posterior p1, which expresses the chances of the initial study results to be confirmed by replication. The obtained replication results will then generate posterior p2. This suggests that based on market prices, and the results of the initial study and its replication, p0, p1 and p2 values can be restored. For example, the probability that the research hypothesis is correct after observing the results of the initial study (p1) ranged from 10 percent to 97 percent with a mean of 57 percent for the 44 studies included. Such mean estimate implied that it could be expected that 43 percent of statistically significant findings will not be replicated. Such research findings are usually the ones who are officially published in some of the top scientific journals. The crux of the matter is that the world of science relies heavily on the statistical significance in the form of p-value, despite its imperfection. It became a regular tool for assessing the credibility of scientific results because it is a straightforward and quick method, especially when compared with the actual replication which is costly and rigorous. The underlying experiment benefited from the fact that almost all of the initial 44 studies experienced replication attempts. The scientific field of psychology most certainly helped the cause. It cannot be neglected however that the reproducibility of studies from other scientific fields represents a tall order. There are and will most certainly be cases where the reproducibility attempt typifies a bigger problem than the initial hypothesis. While trying to avoid making this master thesis a tautology, this section of the master thesis accentuated one more potential utilization of the prediction markets mechanism. As much as

156 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687569/figure/fig02/, [State: 24.05.2018] 94 the scientific productivity is important, honesty, fair play, and equitableness must not be neglected. An example of Piers Corbyn, a London astrophysicist can be mentioned. He developed a theory of long-term weather cycles but was unable to get academic interest. His last attempt to gain publicity was to start betting against the bookmaker William Hill, who used odds posted by the British Metrological Service. Even when accounting for the vigorish of the bookmaker he was able to win 80 percent of his bets in 1994/1995 season.157 This case showed that the pontification still plays a significant part in science. The prediction markets mechanism should provide further help, necessary in the battle against the wastefulness of financial and intellectual resources. 4.6 Legality of prediction markets

Policymaking and the scientific progress do not usually complement each other. Consequently, even though advice of the science is increasingly called upon when talking about the policy- making, utilization of the scientific progress is in most cases being stalled by the contemporary unavailability of the adequate legal framework. This does not necessarily always have to be negative, as not all of the scientific progress is automatically beneficial to the society. The suitability of the scientific progress and its findings predominantly depend upon the purpose for which they are going to be used. National and cultural contexts play an important role as well.

Apart from the sole fact that prediction markets are a work in progress, the introduction above illustrates just a glimpse of adversities prediction markets evolution has to overcome. In the process of achieving the desired robustness, prediction markets have to get legal support. Only with becoming a part of common legal framework prediction markets instrument will be able to influence human lives strongly. On the other side, making the prediction markets mechanism a part of a worldwide legal framework will put the humanity into position to fully exploit its vast potential, the fact the entire master thesis tried to emphasize. Even though the prediction markets mechanism comes under the academic- and general public-spotlight ever more, minimal effort is being devoted to the topic of the legality of prediction markets. The reasons for such state could be numerous, but it is to be remembered that prediction markets are a mechanism which finds itself in the state of development and that the roles in which it could be potentially deployed are numerous. Because of such diversity, creating one single prediction markets law or policy is probably never going to be possible. However, what is the current legal

157 Hanson (1995) 95 situation of prediction markets? In order to answer this question, corresponding laws in the European Union, USA, and the longest deployed prediction market in the form of Iowa Electronic Markets will be briefly analyzed and represented.

The logic implies that within the current realm of legality, prediction markets could be defined either as gambling activities or as the commodity futures trading subject. The master thesis will elaborate on each of this two alternatives apart.

4.6.1 Gambling activities

According to the official EU data, annual revenues from online gambling activities in 2015 were somewhere in the region of €13 billion, compared to €9,3 billion in 2011.158 Increasing amount of tax revenues generated in the Member States, which is of course positively correlated with the amount of annual revenue mentioned above, the high level of technical innovation followed with time and space expansion give the gambling activities a substantial economic significance. The fast pace of online technologies development has a high impact on the gambling industry and simultaneously represents one of the main common clues with the prediction markets mechanism. However, the exact legal definition is of the utmost importance for this section of the master thesis.

 The EU legal definition:159

The gambling and online gambling terms are defined as the “games of chance.” While not all Member States have a legal definition of the concept, in most jurisdictions a game of chance is defined as a game that offers an opportunity to compete for prizes, where success depends entirely or predominantly on coincidence or an unknown future result and cannot be influenced by the player. They generally comprise of stake, prize & random outcome elements.

 The USA legal definition:160

The common law in the USA generally requires the presence of three integral elements in order to define an activity as a gambling transaction. The three elements are prize, chance, and consideration of risk. Concerning the element of chance, the authorities are in general agreement that if such element is present and predominates in the determination of a winner,

158 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52012DC0596, [State: 12.06.2018] 159 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52012SC0345, [State: 12.06.2018] (Comission 2012) 160 Cf. Johnson vs. Phinney, 218 F.sd 303, 306 (5th Cir. 1955), qtd. in Bell (2002) 96 the fact that players may exercise varying degrees of skill is not of relevance. Although several federal statutes apply to gambling activities, they typically rely on state law for a firm definition.

The legal definitions above impose an obvious corollary. The prize element and the risk element (stake, consideration of risk) unquestionably apply for every type of the prediction market. Participants of prediction markets are being rewarded whenever they beat the others in predicting of a particular outcome. It serves as one of the main incentives for participation. Concerning the risk element, participants consciously and willingly engage in a risky action by taking the one side of the particular contract. Thus, the attention has to be turned to the element of chance or the random outcome. Determination or the quantification of the chance plays a decisive role here. The element of chance is hard or almost impossible to quantify. On the other side, virtually every financial instrument today contains a certain amount of chance, as well as the other two elements. Both EU and USA legal definitions of gambling state that chance has to predominate over skill or knowledge of the person who engages in the gambling activity. The whole prediction markets concept relies on the marginal (informed) trader concept. There is no doubt that every prediction market has to contain several ignorant participants that rely on luck and do not want to put enough effort into their trading strategy. However, the encouraging results show that the knowledge predominates the ignorance within prediction markets. On the other side, the profit of the betting houses continues to rise161, proving that betting markets are the framework whose aim must be the opposite to the utilization of the knowledge of bettors. It is much likely the exploitation of the lack of their knowledge. Based on everything previously said, it can be stated that there is an apparent difference between prediction markets and betting activities. They have a different character and historical results to support such a statement. However, this still does not mitigate the difficulty to legally define the prediction markets instrument and provide it with enough legal credentials.

4.6.2 Futures markets

Futures contracts represent derivative securities which provide payoffs that are determined by the prices of other, underlying assets. A specific future contract is an obligation which calls for delivery of an asset (or in some cases, its cash value) at a pre-specified maturity date for an agreed-upon price, to be paid at contract maturity.162 Such a composition makes futures, and especially their non-standardized version in the form of forward contracts, suitable for various

161 Gross Gaming Revenues were estimated to be €84,9 billions, https://eur- lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52012SC0345, State [15.06.2018] 162 Bodie/Kane/Marcus (2014) 97 hedging activities. Based on their similar mechanics, contracts traded in prediction markets closely resemble futures contracts. This is why the idea of regulating prediction markets in the same way as the contracts of futures trade seems to be appealing. The legal definitions however always demand utmost meticulousness. All the subjects of commodity futures trading are regulated by specific legal acts which are enforced by the respective supervision-authority. If the transaction of prediction markets were qualified as a futures market transaction, they would have to obtain a concession from the corresponding authorities. In the USA, supervision of such transactions belongs to the Commodity Futures Trading Commission or CFTC based on the legal act called the Commodity Exchange Act or the CEA. In the case of Austria, the management and administration of such organized market are also based on a concession, with the following important distinction. A securities market demands a concession from the Financial Market Supervision or the FMA, whereas a pure commodity market requires a concession from the Federal Ministry for Digital and Economic Affairs or the BMDW.163 Since the January 2018, Markets in Financial Instruments Directive II or the MiFID II governs and standardizes the regulatory disclosures required for all financial markets across the European Union. The subprime financial crisis from 2008/2009 made the regulatory framework extremely severe, emphasizing the general economic interests and transparency. Everything stated above illustrates just a glimpse of regulatory obstacles which have to be overcome when trying to establish any new financial market legally. The concrete legal ramifications which could result from attempting to incorporate prediction markets into the legal framework of commodity futures trading subjects could prove to be even more elusive. Prediction markets are together with futures markets considered as markets which host trading in which skill predominates over chance when looking at the long run, even though the dividing line between these markets and the gambling market remains to be a very blurry one, as already pointed out. Both prediction and futures markets incorporate zero-sum mechanics, one trader can profit only at the expense of the other. It is accordingly clear that both prediction and futures markets resemble the same trading mechanics regarding the element of chance and the zero-sum game. So where does the problem lie? The problem lies within the fundamental trading mechanics. Bell (2006) states that prediction markets trading resembles a classic example of spot trading, with spot trading being considered as the simultaneous payment for and the immediate delivery of corresponding rights.164 Spot trading is, therefore, an exchange of money for the present

163 https://www.ris.bka.gv.at/GeltendeFassung/Bundesnormen/20009944/BörseG%C2%A0201 8%2c%20Fassung%20vom%2004.01.2018.pdf, [State: 18.06.2018] 164 Cf. Black`s law dictionary (6th ed. 1990), qtd. in Bell (2006) 98 value of a particular item, with the present value being conditional on the present as well as the future relevant factors. However, traders on a prediction market conduct a specific trade in the present, with the instrument traded being payable under specific conditions in the future. The actual delivery takes place only in the future. Accordingly, prediction markets should not be regarded as the spot trading instrument. Prediction markets incorporate the mechanics of futures trading. On the other side, futures trading is being defined as the future delivery of unconditional rights.165 Unconditional rights infer a precisely defined obligation with elements such as price and time horizon being familiar and definitive. In the case of prediction markets, the payment takes place immediately and is conditional on the final result. Because the element of the future plays a decisive role, the conditionality makes sense only in the case of derivative securities, or futures and forwards. Moreover, derivative markets deal often in both tangible commodities as well as in financial products whose value depends on underlying assets. Prediction markets, on the other side, usually deal in intangible matters which take the form of a specific question concerned with a clear fact, or matter.

The fundamental characteristics of prediction markets make the field unmistakably distinctive and require a specifically tailored legal framework.

4.6.3 Lex specialis

The master thesis devoted much space to the Iowa Electronic Markets, as the IEM represents the most prominent prediction market up to date. The IEM analysis enabled a comprehensive insight into what predictions markets are all about. However, how does a legal situation of IEM look alike? Could it provide a generally applicable regulatory template for the future of prediction markets? Unfortunately, the IEM offers no such solution. IEM operates as an experimental and academic program at the University of Iowa on a strictly non-profit basis. The approval for the operation was obtained from the President of the University. Since it is composed of submarkets which deal with generally important topics, the maximum investment by any single participant had to be limited to $500. Otherwise, one of the main purposes of the IEM would be the hedging ability, the feature that almost exclusively belongs to derivative markets. In years 1992 and 1993, based on undeniable facts concerning the IEM purpose and manner of operation, the IEM received two no-action-letters. The Division of Trading and Markets of the Commodity Futures Trading Commission decided that there is no need to recommend that the Commission takes any enforcement action in connection with the operation

165 https://www.futuresfundamentals.org/get-the-basics/futures-and-options/, [State: 20.06.2018] 99 of the Iowa Electronic Markets. Commodity Futures Trading Commission emphasized however that the Commission does not render any opinion as to whether the operation of the IEM violates the provisions of any state law. These matters are always to be confirmed independently.166 Up to date, the two non-action letters received represent the solution to IEM legal status as well as the relief to the operational constraints of the market. A non-action-letter can be regarded as a form of lex specialis, a doctrine which relates to the interpretation of laws. The doctrine states that a law governing a specific subject matter overrides a law that regulates general matters. It can be applied in both domestic and international contexts. Keeping in mind the complexity of the entire prediction markets field and everything the master thesis presented in the legality section, the combination of lex specialis and various state-wise amendments imposes itself as the most suitable and recommendable solution concerning the legality of prediction markets.

166 https://www.cftc.gov/sites/default/files/files/foia/repfoia/foirf0503b004.pdf, [State: 22.06.2018] 100 5 Conclusio

The results of the comprehensive literature analysis, which lies at the heart of this master thesis, indicate a potentially immense scope and power of prediction markets in both forecasting and advisory roles. Governments in many, if not all countries, rely heavily on scientific forecasts and advice provided by various advisory structures. USA and Japan federal governments, for example, use more than a thousand formal and ad hoc advisory committees, with the aim to improve their efficiency providing a variety to both their structure and authority. In such a way, governments are trying ever more to include academies and professional societies as significant policymakers, even though their position in the overall scientific-advisory and policy-making system differs significantly from one country to another. The necessity of developing an instrument which would be able to utilize the knowledge from various resources purposively is becoming evident. The L'Aquila Case, which took place on 6th of April 2009 in Italy, became famous because it emphasized the need of humanity to be able to forecast threats to national security with far greater precision. It also emphasized the need for the responsibility allocation when dealing with the consequences of such forecasts. A major earthquake occurred causing the death of 309 people in the Italian city of L’Aquila. In October 2012, prison sentences were handed to seven Italian scientists due to their role in producing scientific forecasts before this sad event. Even though the sentences for six of the scientists were overruled in 2014, the repercussions of this case continue to hamper the growing involvement of science in the advisory and forecasting roles across the planet.167

The case above confirms the apparent need for an efficient forecasting mechanism. Prediction markets possess the potential to change the technological, legal, financial and security structures of the world we know today. All of the previous sections of master thesis tried to topicalize this potential by following a rigorous, meticulous and comprehensive method of examining and summarizing the most notable findings concerning, above all, the applicability of prediction markets. Based on the examined application attempts and the resulting findings, it can be stated that prediction markets could potentially furnish the exact features (i.e., speed, security, transparency decentralization), almost all of the current governing systems lack. They could provide that craving sense of the unlimited but structured freedom. Still, there are numerous ups and downs to be surmounted until they reach such level of excellence. Further practical developments, including the regulatory framework, are of the highest priority. This would hopefully result in an academic consensus regarding the fundamental theoretical

167 https://www.bmbf.de/files/scientific(1).pdf, [State: 25.06.2018] 101 postulates behind the prediction markets mechanism and help discard the trial reputation. The prediction markets field has to make the next step and come to a situation, from which the academia and practitioners will be able to shift between the theoretical and practical efforts strategically. As in every other scientific field a balance between the two, something that seems to be missing at the moment, is necessary. The popularization of prediction markets is something that does not seem to get the required attention as well. In order to make prediction markets accessible to the general public, some other mechanism has to lose a part of its popularity consequently. Nevertheless, these are just some of many topic-related phenomena, both overwhelming and irresistible. As is, the entire topic itself. Hopefully, the prediction markets marvels will become an essential part of the every-day life. 5.1 Critical assessment

Some of the statements, indications, and conclusions which can be found throughout the master thesis may be debatable. The introduction part of the thesis stressed that the work represents a result of the systematic and detailed analysis of the existing scientific literature referring to prediction markets. As such, the master thesis could contain a noticeable risk of bias already present in the sources used and should be regarded as work with primarily descriptive and summarizing character. Even though the name of the master thesis topic indicates otherwise, the scientific quality of the master thesis would be improved with the inclusion and conduction of a purposive prediction market experiment considerably. Since such endeavor requires significant resources, this was unfortunately not possible. Furthermore, the aspired general applicability of the studies used could also be brought into question. Prediction markets framework is still too manifold, and further improvements are needed to be able to draw more clear conclusions. Limited literature resources also had their impact on the quality of this scientific work. Unrestricted access to the researchgate.net platform would have been of great help since the platform currently represents one of the most common ways of publishing relevant research material.

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