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The Growth of Gambling and Prediction Markets:Economic and Financial Implications

By DAVID PATONw, DONALD S. SIEGELz and LEIGHTON VAUGHAN WILLIAMSzzwNottingham University Business School zUniversity at Albany, SUNY zzNottingham

Business School

Final version received 2 June 2008.

In recent years, there has been a substantial global increase in gambling and prediction markets, including

casinos, sports betting, lotteries, elections and wagering on nancial instruments. This trend has

heightened interest in numerous economic and nancial issues related to this sector. These include

questions relating to the efciency of these markets, heterogeneity in risk attitudes among economic

agents, and the use of prediction markets in policy analysis. The papers in this special issue provide a mix

of theoretical and empirical evidence on these issues.

INTRODUCTION

In recent years, there has been a substantial global increase in gambling and predictionmarkets, including casinos, sports betting, lotteries, elections and wagering on nancialinstruments. This trend has heightened interest in numerous economic and nancialissues related to this sector. These include questions relating to the efciency of thesemarkets, heterogeneity in risk attitudes among economic agents, and the use ofprediction markets in policy analysis.

We have also witnessed major changes in the taxation and regulation of gambling inthe UK and other nations, as well as technological innovations that inuence this sector(e.g. the internet). These changes have stimulated numerous research and consultancyprojects related to this rapidly growing sector. Such projects include analyses of theeconomic and social impacts of gambling, as well as examinations of the manner in whichbetting markets can be used as a tool for aggregating decentralized information so as toproduce efcient and unbiased forecasts. This emerging interest in what have come to beknown as prediction markets has generated considerable interest among academics indetermining how best to design and utilize these new markets.

A convenient denition of a prediction market is a speculative market that is createdfor the purpose of making a prediction. In such a market, an asset is created whose nalcash value is tied to a particular event (e.g. who will win the US Presidency) or keyeconomic indicator (e.g. a rms total sales revenue in the next quarter). The market priceof the asset is then interpreted as a prediction of the probability of the event or theexpected value of the parameter. Traders who have bought low and sold high arerewarded for improving the market prediction, while traders who buy high and sell loware penalized for reducing the accuracy of the market prediction.

There is now a strong body of evidence suggesting that prediction markets may bemore accurate than other mechanisms (e.g. election polls), in terms of predictingoutcomes. At the very least, they elicit a different perspective on how people actuallybelieve the future will play out compared to conventional surveys. As noted by Levitt(2007), corporations are now using prediction markets as a decision support tool.

The link between gambling and prediction markets can be traced in the UK to theabolition of a turnover tax on betting in 2001, and its replacement with a much lower

Economica (2009) 76, 219224

doi:10.1111/j.1468-0335.2008.00753.x

r The London School of Economics and Political Science 2008

effective incidence of taxation on the gross prots of companies offering betting services(Paton et al. 2002, 2004). In concert with the recent rapid expansion of person-to-personbetting exchanges, with relatively low margins, the extent to which price biases may existin these markets, and more generally their ability to forecast a range of outcomes, hasbeen addressed and tested across a range of published outputs in the recent academicliterature (e.g. Smith et al. 2006; Borghesi 2007).

The rapid growth in gambling and prediction markets has heightened interest in avariety of economic and nancial issues related to these activities. Policy-makers seekguidance on how to tax and regulate these markets at the state, regional, national andcross-country levels. Furthermore, betting and prediction markets provide a unique andconvenient framework within which to examine fundamental issues relating to traditionalareas of economics.

Our objective in this special issue is to help ll various gaps in this emergingliterature. We issued an open call for papers (which appeared in Economica and also wasdistributed to a wider audience) via the Financial Economics Network, EconomicsResearch Network, TIMS/ORSA and the Royal Economic Society. The leadingcontributors to this burgeoning literature were also directly solicited. We received 52manuscripts. To aid in the development of these papers, a special issue workshop washeld at the UC-Riverside Palm Desert Graduate Center on 2122 May 2007. Each paperwas allocated an assigned discussant who provided feedback to the authors. After theworkshop, each author was invited to resubmit his or her paper. The papers were thensent to external reviewers, following standard procedures in Economica. Five papers wereultimately accepted for inclusion in the special issue.

PAPERS IN THE SPECIAL ISSUE

The articles in the special issue address three key themes:

1. Information efciency in betting and prediction markets.2. Theoretical and empirical evidence on the favourite/longshot bias in betting markets.3. The use of prediction markets for policy analysis.

In the remainder of this introductory essay, we provide focused summaries of thearticles in the special issue and then attempt to place them in a broader context.

Hanson and Oprea

The paper by Robin Hanson and Ryan Oprea is a theoretical analysis of the efciency oflow volume information markets (e.g. prediction markets and idea futures). It isinteresting to note that some politicians, journalists and economists have raised concernsabout manipulation in these thinly-traded markets. The purpose of the Hanson andOprea paper is to assess the impact of manipulators on the efciency of informationmarkets. In modelling the behaviour of agents who participate in these markets, theauthors identify three types of traders: liquidity traders, informed traders, andmanipulators.

In their model, manipulators function as noise traders, in the sense that they inducemore traders to become better informed. Other features of the model are that there areclues provided regarding the true asset value by a manipulator, and the authors alsoallow for irrational choices, provided by the quantal response equilibrium (McKelvey

220 ECONOMICA [APRIL


and Palfrey 1995; Goeree and Holt 2001). Their key theoretical result is thatmanipulators play an important social role by increasing the efciency of informationaccuracy. Specically, their actions result in an increase in the variance of the targetprice. This, in turn, increases the accuracy of the average price, by increasing the returnsto informed trading and thus the incentives traders have to become informed. We believethat the Hanson and Oprea result constitutes fertile ground for experimental research.

Wolfers and Zitzewitz

The paper by Justin Wolfers and Eric Zitzewitz advances traditional retrospectiveanalyses of the expected effects of policy changes (see Wolfers and Zitzewitz 2004;Snowberg et al. 2005) and the forecasting efciency of betting markets (e.g. Strumpf andRhode 2004; Vaughan Williams 2005), by demonstrating how prediction markets can beused to prospectively estimate policy effects. The example they use to illustrate this ismarket trading in contracts tied to the removal from power of Saddam Hussein, whichprovides information regarding nancial market participants expectations of theconsequences of the Iraq war. They conducted an ex ante analysis, disseminated beforethe war, which revealed that a 10% increase in the probability of war was accompaniedby a $1 increase in spot oil prices in the futures markets. Equity price movements impliedthat the same shock led to a 1.5% decline in the S&P 500.

More generally, Wolfers and Zitzewitz used the existence of widely-traded equityindex options to back out the entire distribution of market expectations of the warsnear-term effects, nding that the ow of war-related news throughout their sample wasable to explain a large proportion of daily oil and equity price movements. Theirsubsequent analysis suggested that these relationships continued to hold out of sample.Most interesting is the implication that this type of analysis can allow us to characterizewhich industries and countries are likely to be more or less sensitive to particular types ofbreaking news event. Indeed, the authors focus on the more general lessons that can belearned from their paper by highlighting those features of the case study that make itparticularly amenable to policy analysis, and by discussing some of the issues in applyingthis method to other policy contexts.

Peel and Law

Many authors have attempted to reconcile observed behaviour in betting markets withstandard models of attitudes to risk. The paper by David Peel and David Law attemptsto provide a more general non-expected utility model that is capable of explaining whatappears to be risk-seeking behaviour in gambling and risk-averse behaviour in othercontexts such as insurance.

The authors emphasise the existence of heterogeneity in risk attitudes and individualprobability distortions. Allowing for such heterogeneity enables a single model toencompass a range of observed outcomes. The insight that risk attitudes may differ insystematic ways associated with cultural or institutional factors is likely to be ofconsiderable benet to researchers who have long puzzled over why, for example, we nda standard favourite-longshot bias in some betting markets such as the UK (see, forexample, Vaughan Williams and Paton 1997) but a reverse bias in others. Peel and Lawdemonstrate one application of their model by re-examining existing literature on therelationship between Tote and bookmaker returns. We believe, however, that the model

2009] THE GROWTH OF GAMBLING AND PREDICTION MARKETS 221


proposed in this paper will generate a number of further empirical applications both inbetting markets and in other contexts.

Bruce, Johnson, Peirson and Yu

The theme of differences in underlying characteristics of bettors as a way of explainingwhy we observe varying levels of the favourite-longshot bias in different contexts iscontinued in the fourth paper in the special issue. Alistair Bruce, Johnnie Johnson, JohnPeirson and Jiejun Yu introduce a theoretical model in which bettors differ according to(i) the level of information they possess, (ii) their motivations for gambling and (iii) thelevel of transactions cost. Previous authors have used each of these in isolation aspossible reasons as to why we observe the favourite-longshot bias (see Vaughan Williams1999). The contribution of this paper is to bring the three streams of literature togetherinto a single encompassing model.

The authors test their model using a dataset covering betting activity on the sameoutcomes by three distinct groupsFbettors at the racetrack, off-track bettors at licensedbookmaking premises and off-track bettors using the telephone. In this data, they ndsignicant differences in the extent of the favourite-longshot bias that are generallyconsistent with their theoretical model.

Link and Scott

The special issue concludes with a paper by Al Link and John Scott, which evaluates thepotential impact of a prediction market on a key public policy initiative: the SmallBusiness Innovation Research (SBIR) programme (Siegel et al. 2003). SBIR wasestablished in 1982 by the US government as a set-aside programme. In its currentversion, SBIR requires all federal research and development (R&D) funding agenciessponsoring extramural research to allocate 2.5% of their extramural research budgets tofund, via a peer-review process, R&D in small (less than 500 employees) rms andorganizations. A key goal of the SBIR programme is the private-sector commercializa-tion of these research projects by these entrepreneurial companies. Indicators ofsuccessful commercialization include the creation and sale of new products, job growth,patents, copyrights, trademarks, and technology licensing agreements.

Given the early stage nature of much of this research, and the fact that commercializationinvolves small rms, who typically have a high failure rate, the likelihood of successfulcommercialization is quite low. Link and Scott view these public investments incommercialization projects as gambles, and conjecture that a prediction market could beused to enhance the rate of successful commercialization of SBIR projects. Using project-leveldata, they present an econometric analysis of whether outside private investors have usefulinformation about proposed SBIR projects prospects for commercialization. Their ndingssuggest that private investors indeed have such information, which provides support for theview that a prediction market could improve the performance of the SBIR programme.

CONCLUSION

Many scholars have analysed the efciency of betting markets. While most of thesestudies agree that it is difcult to earn abnormal returns in these markets, there existestablished biases across a range of these, perhaps the most famous of which is the



favouritelongshot bias, i.e. the tendency for wagers placed at shorter odds to yieldhigher expected returns than wagers placed at longer odds. The explanation for this bias,and why it persists in most arenas, but not all (sometimes it is reversed), has long been thetopic of academic scrutiny. The papers by Peel and Law and by Bruce et al. in this specialissue go some way to resolving this puzzle.

The value of betting markets as a forecasting tool is also of considerable currentacademic interest, and a number of recent papers have addressed this from variousangles, including the aggregation of information from different markets (Paton andVaughan Williams 2005), the inuence of market ecology (Sung and Johnson 2007) andoptimal design (e.g. Servan-Schreiber et al. 2004). The key questions are whether thesemarkets are able to add value to traditional forecasting methodologies, and if so to whatextent and with what caveats. A signicant amount of this research effort has to datefocused on the value of these markets in predicting the outcomes of political elections(see, for example, Strumpf and Rhode 2004), although the focus of other studies rangesfrom the sales of Hewlett-Packard printers to the probability of meeting project deadlinesat Google (Leigh and Wolfers 2007). There is also growing interest in the value ofcontingent markets (If event X occurs, how will this affect outcome Y?).

As these markets become more prevalent, it is inevitable that questions will be raisedregarding whether these markets can be manipulated. The paper by Hanson and Oprea,included in this volume, is timely in addressing this important issue.

Another key issue is how prediction markets can be used as an independent estimatorof the impact of a unique event, such as the outcome of an election, on key economicvariables (see Snowberg et al. 2007). The paper by Wolfers and Zitzewitz makes avaluable contribution to this aspect of prediction markets. Most importantly, their articleprovides us with several general lessons that can be applied across a range of policycontexts.

Finally, the paper by Link and Scott provides an interesting perspective on this very issueof policy context, by evaluating the potential impact of a prediction market on a key publicpolicy initiative, namely the Small Business Innovation Research (SBIR) programme.

Future research can usefully build on all these areas of current public discussion togenerate ideas as to how prediction markets can best be used to add maximum value toexisting forecasting methodologies as well as to most fully inform the widest range of policycontexts. We believe that this special issue of Economica makes a useful contribution toimproving our understanding of the policy implications of gambling and prediction markets.

ACKNOWLEDGEMENTS

We thank Steve Levitt, William Eadington and participants at the workshop The Growth ofGambling and Prediction Markets: Economic and Financial Implications held at the UC-Riverside Palm Desert Graduate Center on 2122 May 2007, for comments and suggestions. Weare especially grateful to Carolyn Stark, Jessica Enders, Ron Willison and Toni Lawrence of theUC-Riverside Palm Desert Graduate Center for helping us to organize the workshop. Financialsupport from the A. Gary Anderson Graduate School of Management at UC-Riverside, theNottingham University Business School, Nottingham Trent University, and the University ofBuckingham Press is also greatly appreciated.

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