Hausch D. B. (Ed.), Ziemba W. T. (Ed.)

Elsevier fm-n50744.pdf 2008/8/6 8:12 am Page: xvii Trim: 7.5in × 9.25in Floats: Top/Bot TS: diacriTech, India List of Contributors Mukhtar Ali, Department of Economics, University of Kentucky, Lexington, KY, USA John Bacon-Shone, Social Sciences Research Centre, University of Hong Kong, Hong Kong Bruno Deschamps, School of Management, University of Bath, Claverton Down, Bath, UK David Edelman, Banking & Finance Unit, University College Dublin, Blackrock, County Dublin, Ireland David Forrest, Centre for the Study of Gambling, University of Salford, Salford, UK Olivier Gergaud, Department of Economics, University of Reims, Champagne- Ardenne, France Joseph Golec, Department of Finance, School of Business, University of Connecticut, Storrs, CT, USA Marshall Gramm, Rhodes College, Memphis, TN, USA Kent Grote, Department of Business and Economics, Lake Forest College, Lake Forest, IL, USA John Haigh, Mathematics Department, University of Sussex, Falmer, Brighton, UK Donald B. Hausch, School of Business, University of Wisconsin, Madison, WI, USA Stewart D. Hodges, Finance Group, Warwick Business School, University of Warwick, Coventry, UK J. E. V. Johnson, Centre for Risk Research, School of Management, Highfield, University of Southampton, Southampton, UK Bruno Jullien, Toulouse School of Economics, Toulouse, France Stephan Kossmeier, Institute for Advanced Studies/Institut fürHohere,¨ Studien (IHS), Vienna, Austria Daniel Lane, Telfer School of Management, University of Ottawa, Ottawa, ON, Canada xvii Elsevier fm-n50744.pdf 2008/8/6 8:12 am Page: xviii Trim: 7.5in × 9.25in Floats: Top/Bot TS: diacriTech, India xviii List of Contributors Victor S. Y. Lo, Fidelity Investments, Boston, MA, USA Victor Matheson, Department of Economics, College of the Holy Cross, Worcester, MA, USA Marco Ottaviani, Department of Management and Strategy, Kellogg School of Management, Northwestern University, Evanston, IL, USA Matt E. Ryan, Department of Economics, West Virginia University, Morgantown, WV, USA Bernard Salanie´, Department of Economics, Columbia University, New York, NY, USA Michael A. Smith, Leeds Business School, Leeds Metropolitan University, Leeds, UK Erik Snowberg, Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA Russell S. Sobel, Department of Economics, West Virginia University, Morgantown, WV, USA Peter Norman Sørensen, Department of Economics, University of Copenhagen, Copenhagen, Denmark Hal S. Stern, Department of Statistics, University of California, Irvine, CA, USA M. Sung, Centre for Risk Research, School of Management, Highfield, University of Southampton, Southampton, UK Maurry Tamarkin, Graduate School of Management, Clark University, Worcester, MA, USA Richard Thalheimer, Thalheimer Research Associates, Inc., Lexington, KY, USA Robert G. Tompkins, Centre for Practical Quantitative Finance, Hochschule für Bankwirtschaft, Germany Leighton Vaughan Williams, Betting Research Unit, Nottingham Business School, Nottingham Trent University, Nottingham, UK Ian Walker, Department of Economics, University of Warwick, Coventry, UK Simon Weinberger, Institute for Advanced Studies/Institut für Hohere¨ Studien, Vienna, Austria Justin Wolfers, Business and Public Policy Dept., The Wharton School, University of Pennsylvania, Philadelphia, PA, USA Alan Woods, Deceased William T. Ziemba, Sauder School of Business, UBC, Vancouver, Canada, Mathe- matical Institute, Oxford University, and ICMA Centre, University of Reading, UK Eric Zitzewitz, Department of Economics, Dartmouth College, Hanover, NH, USA Elsevier fm-n50744.pdf 2008/8/6 8:12 am Page: xix Trim: 7.5in × 9.25in Floats: Top/Bot TS: diacriTech, India Preface This volume surveys the broad subject of sports and lotto investments. The various chapters cover many sports, such as soccer, NFL and college football, baseball, basket- ball, Jai Alai, and lotto markets. We do not discuss casino gambling nor the statistics of sports; rather, we focus on the financial markets associated with legal betting on sports and lotto events. All the chapters are newly written academic surveys that we commis- sioned for this volume. In certain areas, this volume updates to 2008 our earlier edited volume [Hausch, D. B., V. Lo, and W. T. Ziemba (HLZ), 1994, Efficiency of Racetrack Betting Markets, Academic Press, San Diego, CA]. That volume became not only a classic, but a cult item as it helped usher in professional racetrack betting. While small in comparison with hedge funds, the various syndicates across the world have made about $10 billion using computerized betting strategies. This volume continues with some of the basic research behind such investment teams and the academic theory of investment in sports and lotto markets. HLZ reprinted older classic papers and complemented them with new original work. This current volume is entirely composed of newly written chapters that build on earlier papers. So, in our view, HLZ (which was reprinted in its entirety with no changes except a new preface as HLZ, 2008, 2nd ed., World Scientific, Singapore) and this volume are companion books in this field. Other books that discuss similar topics are Vaughan Williams, L., 2003, The Economics of Gambling, Routledge, London; Vaughan Williams, L., 2005, Information Efficiency in Financial and Betting Markets, Cambridge University Press, Cambridge, UK, which are highly recommended; and our trade books, Ziemba, W. T., and D. B. Hausch, 1984, Beat the Racetrack, Har- court, Brace and Jovanovich, NewYork; Ziemba, W. T., and D. B. Hausch, 1986, Betting at the Racetrack, Dr Z Investments, Inc., San Luis Obispo, CA; Ziemba, W. T., and D. B. Hausch, 1987, Dr Z’s Beat the Racetrack, William Morrow, New York. The volume is organized into eight parts. Part I discusses the industry side of the racetrack and other betting markets. Ali and Thalheimer discuss the effects of com- petition from casinos, lotteries, professional sports, and horse racing and wagering on racetrack handle. Bacon-Shone and Woods empirically study the factors that influence both the extent of the public’s wagering and its allocation across the betting pools. One factor addressed is the partial rebate sometimes available to losers of large wagers. xix Elsevier fm-n50744.pdf 2008/8/6 8:12 am Page: xx Trim: 7.5in × 9.25in Floats: Top/Bot TS: diacriTech, India xx Preface Part II studies the bettors and horses in a race. Jullien and Salanie´ survey the literature dealing with the empirical estimation of the bettor’s utility function, includ- ing addressing issues such as representative bettors versus heterogeneous beliefs, and expected versus nonexpected utility. Lo and Bacon-Shone devise probabilities for multientry competitions. Edelman empirically studies the running patterns of race horses, finding distance preferences and establishing pace characteristics somewhat at odds with established physiological results on optimal running. Part III discusses the well-established favorite-longshot bias in horse racing, which is the tendency for favorites to be underbet and longshots to be overbet. Ottaviani and Sørensen present various theoretical constructs that generate this bias. Snowberg and Wolfers use massive data sets to empirically estimate the recent favorite-longshot bias in the U.S., Australia, and other locales. They argue that the anomaly is based more on perceptions than preferences. That means that bettors overestimate the chances of low probability events. They also show that extreme favorites no longer have positive expected value as Ziemba and Hausch found in 1986. This updates the studies surveyed in Ziemba and Hausch (1986); see also the updated graph in Ziemba’s chapter in Part IV and Ziemba, W. T., 2004, Behavioral Finance, Racetrack Betting and Options and Futures Trading, Mathematical Finance Seminar, Stanford University, Palo Alto, CA. Busche, K., and C. Hall, 1988, An Exception to the Risk Preference Anomaly, Journal of Business 61, 337–346; Busche, K., 1994, Efficient Market Results in an Asian Setting, in HLZ; Vaughan Williams, L., and D. Paton, 1998, Why Are Some Favourite-Longshot Biases Positive and Others Negative? Applied Economics 30, 1505–1510 discuss reverse biases in Asia and other locales. Sobel and Ryan document a pattern of public betting that varies by the day of the week. Different levels of casual and serious bettors at the track on different days of the week can explain this variation and provides a basis for understanding the favorite-longshot bias. Tompkins, Ziemba, and Hodges show that there are similar biases in the S&P 500 and FTSE 100 index put and call options. Part IV discusses weak form market efficiency in racing and various sports events. Ziemba discusses efficiency in racing and other sports as well as in lotto games. He describes the place and show betting system that arises because these markets are more complex than the win market. The original 1981 system, which was popularized in the trade books by Ziemba and Hausch (1984, 1986, and 1987), still basically produces profits but needs rebates to do this because, currently, so much of the public’s wagers do not enter the pools until after the race has started. This is because about 87% of the typical track’s handle is bet off that track by other bettors and by professional syndicates. Ziemba also discusses cross-track betting, NFL, and NBA games and provides an introduction to lotteries. The latter topic is discussed in three chapters in Part VIII. Stern studies point spread and odds betting in U.S. college and professional

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