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The Forrester executive summary:

The “wisdom of crowds” is capturing the attention of corporate strategists across the globe, and, as a result, many are now looking to prediction markets — speculative markets in which traders collectively predict future events — to generate collective intelligence. For enterprises, prediction markets bring unique value: They focus on the future, aggregate diverse information pools that can be applied to multiple decision-making domains, create streams of actionable data suitable for executive decision-making, and can often cut through corporate politics and pressures at lower cost than traditional forecasting methods. Market researchers will, however, need to have an active hand in the management of these mechanisms, ensuring strong management support, the right incentives for traders, and a focus on appropriate questions. When executed properly, the value to the enterprise is enormous; as a result, Forrester believes that prediction markets will ultimately find a permanent home in the market research toolbox.

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For information on hard-copy or electronic reprints, please contact the Client Resource Center at +1 866.367.7378, +1 617.617.5730, or resourcecenter –at– forrester –dot– com. We offer quantity discounts and special pricing for academic and nonprofit institutions.

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APPENDIX #1: Prediction Markets – DRM Review

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APPENDIX #2:

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Here is a list of companies that provide software for prediction markets:

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Inkling Markets – (MSR + AMM)

NewsFutures – (CDA + optional AMM + SR)

Xpree – (MSR + AMM)

Zocalo – (CDA + MSR + AMM) – (open-source)

Nosco – (CDA + MSR + AMM)

QMarkets – (MSR + AMM)

Ask Markets – (MSR + AMM)

Exago Markets – (CDA + optional AMM)

Gexid – (?)

ProKons – (?)

Spigit – (?)

HSX Virtual Markets – (Virtual Specialist + AMM)

HubDub – (MSR + AMM) – (not licensed)

Yahoo!‘s Prediction Exchange – (MSR + AMM + DPMM) – (not licensed)

Google‘s Prediction Exchange – (CDA) – (not licensed)

MicroSoft PredictionPoint – (MSR + AMM) – (not licensed)

InTrade – (CDA + AMM for play money) – (not licensed)

TradeSports – (CDA + AMM for play money) – (not licensed)

Iowa Electronic Markets – (CDA) – (not licensed)

HedgeStreet – (CDA) – (not licensed)

TradeFair – (CDA) – (not licensed)

BetFair – (CDA) – (not licensed)

Trading Technologies International – (CDA) – (not for event derivatives)

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Here’s a list of prediction market consultants:

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Robin Hanson – (George Mason University, Virginia, U.S.A.)

• Robin Hanson does prediction market consulting work, and have no exclusive arrangements.
• “I’m more interested in helping groups that want to add lots of value to big decisions, versus groups that just want to dabble in a new fad.”

Inkling – URL: Inkling Markets – (Chicago, Illinois, U.S.A.)

• Adam Siegel
• Nathan Kontny

NewsFutures – (Maryland, U.S.A. & Paris, France, E.U.)

• Emile Servan-Schreiber — Post Archive at Midas Oracle
• Maurice Balick

Xpree – (California, U.S.A.)

• Mat Fogarty

Chris Hibbert – (California, U.S.A.)

• Chris Hibbert (Software architect / Zocalo project manager) — Post Archive at Midas Oracle
• Chris Hibbert’s personal website — Chris Hibbert’s personal blog —
• Chris Hibbert’s CommerceNet profile — (His stint there ended in mid 2006.)

Justin Wolfers – (University of Pennsylvania’s Wharton Business School, Pennsylvania, U.S.A.)

• Justin Wolfers takes on prediction market consulting work.
• The prediction market industry is “a case where the interaction between firm practice and academic research are reasonably close.”

Koleman Strumpf – (University of Kansas, Kansas, U.S.A.)

• Koleman Strumpf — Post Archive at Midas Oracle
• Koleman Strumpf can be approached to consult on prediction market projects.
• “Prediction markets help harness the knowledge of diverse groups. They have great potential as a tool for industry.”

Nosco – (Danemark, E.U.)

• Jesper Krogstrup
• Oliver Bernhard Pedersen

Qmarkets – (Israel)

• Noam Danon

Ask Markets – (Greece, E.U.)

• George Tziralis — Post Archive at Midas Oracle

HP Services & HP Labs – (U.S.A.)

• Predicting the future –with games — Introductory article
• Information Dynamics Lab — Internal prediction markets
• BRAIN – (Behaviorallly Robust Aggregation of Information in Networks) — Scoring Rules (i.e., non-trading technique)
• Bernardo A. Huberman – Bernardo Huberman – Senior Fellow & Director
• Kay-Yut Chen -
• Google Search for “prediction markets”

Hollywood Stock Exchange (HSX) & HSX Research – (L.A., California, U.S.A.)

• Prediction market consultancy firm
• Movie business

IntelliMarket Systems – (L.A., California, U.S.A.)

• Charles R. Plott – Charles Plott – (CalTech Inst., California, U.S.A.)

Gexid – Global Exchange for Information Derivatives – (Germany, E.U.)

• Bernd Ankenbrand — Post Archive at Midas Oracle

ProKons – (Germany, E.U.)

• Peter Gollowitsch

Exago Markets – (Portugal, E.U.)

• Pedro Da Cunha

NimaniX – (Israel)

• Elad Amir (CEO), Littal Shemer Haim (VP Business development), David Shahar (VP R&D)

Michael Giberson – (Texas, U.S.A.)

• Michael Giberson (Energy Economist – Center for Energy Commerce, Rawls College of Business, Texas Tech University) — Post archive at Midas Oracle
• Knowledge Problem – Blog on economics, energy policy, more.

Other Consulting Firms

McKinsey – (U.S.A.)

• Google Search for “prediction markets”
• The Promise Of Prediction Markets – by McKinsey – 2008-04-XX

Accenture – (U.S.A.)

• Google Search for “prediction markets”

Gartner – (U.S.A.)

• Google Search for “prediction markets”

Forrester – (U.S.A.)

• Google Search for “prediction markets”
• Prediction Markets: Wisdom Of The Crowd Comes To The Enterprise. – 2008-07-14

The Boston Consulting Group – (U.S.A.)

• Google Search for “prediction markets”

CapGemini – (U.S.A.)

• Google Search for “prediction markets”

KPMG – (U.S.A.)

• Google Search for “prediction markets”

Price Waterhouse Cooper – (U.S.A.)

• Google Search for “prediction markets”

Ernst & Young – (U.S.A.)

• Google Search for “prediction markets”

Deloitte – (U.S.A.)

• Google Search for “prediction markets”

IBM – (U.S.A.)

• Google Search for “prediction markets”

EDS – (U.S.A.)

• Google Search for “prediction markets”

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Check that CFM page for updates. And contact me so I can make additions to the list. (I’ll then re-publish that updated list on Midas Oracle.)

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• Michael Abramowicz – Michael B. Abramowicz – (Law School, George Washington University, Washington, D.C., U.S.A.) — Post Archives at Midas Oracle
• Bernd H. Ankenbrand – Bernd Ankenbrand – (Lecturer, Witten/Herdecke University, Germany, E.U.) — Post Archives at Midas Oracle
• James Annan – (Global Environment Modelling Research Program, Frontier Research Center for Global Change, Japan)
• Werner Antweiler – (UBC Election Stock Market, Sauder School of Business, University of British Columbia, Vancouver, Canada)
• Kenneth J. Arrow – Kenneth Arrow – (Economics Department, Stanford University, California, U.S.A.)
• Tom W. Bell – Tom Bell – (Law School, Chapman University, California, U.S.A.) — Post Archives at Midas Oracle
• Joyce E. Berg – Joyce Berg – Profile – (Iowa Electronic Markets, Accounting, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Richard Borghesi – (Finance, Texas State University at San Marcos, Texas, U.S.A.) — Post Archives at Midas Oracle
• Kay-Yut Chen – (Information Services & Process Innovation Lab, HP Labs, HP, California, U.S.A.)
• Yiling Chen – (Micro-Economic and Social Systems, Yahoo! Research Labs, New York, U.S.A.) — Post Archives at Midas Oracle
• Eric Crampton – (Department of Economics, University of Canterbury, New Zealand) — Post Archives at Midas Oracle
• Ely Dahan – (Marketing, Anderson School, University of California at Los Angeles, Los Angeles, California, U.S.A.)
• Anita Elberse – (Marketing, Harvard Business School, Harvard University, Massachusetts, U.S.A.)
• Leslie R. Fine – Leslie Fine – (Information Dynamics Lab, HP Labs, California, U.S.A.)
• Gary William Flake – Gary W. Flake – Gary Flake – (MicroSoft, U.S.A.)
• Robert Forsythe – (Dean, College of Business Administration, University of South Florida, Florida, U.S.A.) — Previously: (Economics, Iowa Electronic Markets, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Lance Fortnow – Lance J. Fortnow – (Department of Electrical Engineering and Computer Science, McCormick School of Engineering, Northwestern University, Illinois, U.S.A.)
• Keith Jacks Gamble – Keith Gamble – (Economics (University of California at Berkeley) – California, U.S.A. — Post Archives at Midas Oracle
• Michael Gorham – (IIT Center for Financial Markets, Stuart School of Business, Illinois Institute of Technology, Illinois, U.S.A.) — Previously: (CFTC)
• Thomas S. Gruca – Thomas Gruca – Profile – (Iowa Electronic Markets, Marketing, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Refet Gurkaynak – (Department of Economics, Bilkent University, Turkey)
• Friedrich August Von Hayek — R.I.P. — The market as an information aggregation tool: The Use of Knowledge in Society —
• Robert W. Hahn – Robert Hahn – (Executive Director, Reg-Markets Center, American Enterprise Institute, Washington, D.C., U.S.A.) — Post Archives at Midas Oracle
• James D. Hamilton – James Hamilton – (Department of Economics, University of California at San Diego – California, U.S.A.) — Post Archives at Midas Oracle
• Robin Hanson – Robin D. Hanson – (Economics, James M. Buchanan Center, George Mason University, Virginia, U.S.A.) — Post Archives at Midas Oracle
• M. Todd Henderson – (Law School, University of Chicago, Chicago, Illinois, U.S.A.)
• Chris Hibbert – (Software Architect, Zocalo Project Manager, California, U.S.A.) — Post Archives at Midas Oracle
• Charles Holt – Charles A. Holt – (Department of Economics, University of Virginia, Virginia, U.S.A.)
• Bernardo A. Huberman – Bernardo Huberman – (Social Computing Lab, HP Labs, California, U.S.A.)
• Frédéric Koessler – (Economics, Centre Nationale de la Recherche Scientifique, France, E.U.)
• Anthony M. Kwasnica – Anthony Kwasnica – (Business Economics, Department of Insurance and Real Estate, Smeal College of Business Administration, Penn State University, Pennsylvania, U.S.A.)
• Nicolas Lambert – (Department of Computer Science, Stanford University, California, U.S.A.)
• John O. Ledyard – John Ledyard – (Economics and and Social Sciences, Division of Humanity and Social Sciences, California Institute of Technology, California, U.S.A.)
• Andrew Leigh – (Economics, Research School of Social Sciences, Australian National University, Australia)
• Steven Levitt – Steve Levitt – Steven D. Levitt – (Economics, Director of the Becker Center on Chicago Price Theory, Graduate School of Business, University of Chicago, Illinois, U.S.A.)
• Saul Levmore – (Law School, University of Chicago, Illinois, U.S.A.)
• Robert E. Litan – Robert Litan – (Vice President for Research and Policy, Kauffman Foundation, Kansas City, Missouri, U.S.A.)
• Thomas W. Malone – Thomas Malone – Tom Malone – (MIT Center for Collective Intelligence, Management, MIT Sloan School of Management, Massachusetts Institute of Technology, Massachusetts, U.S.A.)
• Charles F. Manski – Charles Manski – (College of Arts and Sciences, Norwestern University, Illinois, U.S.A.)
• Adam Meirowitz – (Department of Politics, Princeton University, New Jersey, U.S.A.)
• Paul Milgrom – (Department of Economics, Stanford University, California, U.S.A.)
• Tracy Mullen – (Information Sciences and Technology, Penn State University, Pennsylvania, U.S.A.)
• Forrest Nelson – Profile – (Iowa Electronic Markets, Economics, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• George R. Neumann – George Neumann – Profile – (Iowa Electronic Markets, Economics, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Ryan Oprea – Profile – (Director of the LEEPS laboratory, Economics, University of California at Santa Cruz, California, U.S.A.)
• Gerhard Ortner – (University of Applied Sciences, Austria, E.U.)
• Marco Ottaviani – (Management and Strategy, Kellogg School of Management, Northwestern University, Illinois, U.S.A.) – Formerly: (Economics Department, London Business School, United Kingdom, E.U.) — Post Archives at Midas Oracle
• David Paton – (Industrial Economics, Business School, Nottingham University, United Kingdom, E.U.)
• David M. Pennock – David Pennock – (Principal Research Scientist, Micro-Economic and Social Systems, Yahoo! Research Labs, New York, U.S.A.) — Post Archives at Midas Oracle
• Charles R. Plott – Charles Plott – (Economics and and Social Sciences, Division of Humanity and Social Sciences, California Institute of Technology, California, U.S.A.)
• David Porter – (Economics and Finance, Chapman University, California, U.S.A.)
• Todd Proebsting – Todd A. Proebsting – (Microsoft & University of Arizona, U.S.A.)
• Russ Ray – (Finance, College of Business, University of Louisville, Kentucky, U.S.A.)
• Daniel Reeves – (Micro-Economic and Social Systems, Yahoo! Research Labs, New York, U.S.A.) — Post Archives at Midas Oracle
• Paul W. Rhode – Paul Rhode – (US Economic History, College of Management, The University of Arizona, Arizona, U.S.A.) – Formerly: (University of North Carolina, North Carolina, U.S.A.)
• Thomas A. Rietz – Thomas Rietz – Profile – (Iowa Electronic Markets, Finance, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Richard Roll – (University of California at Los Angeles, Los Angeles, California, U.S.A.)
• Thomas W. Ross – Thomas Ross – (UBC Election Stock Market, Sauder School of Business, University of British Columbia, Vancouver, Canada)
• Rahul Sami – (School of Information, University of Michigan, Michigan, U.S.A.)
• Sam L. Savage – Sam Savage – (Consulting Professor, Stanford U., California, U.S.A.) — Post Archives at Midas Oracle
• Thomas C. Schelling – Thomas Schelling – (Economics, School of Public Affair, University of Maryland, Maryland, U.S.A.)
• Emile Servan-Schreiber – (NewsFutures, New York City, New York, U.S.A. & Paris, France, E.U.) — Post Archives at Midas Oracle
• Robert J. Shiller – Robert Shiller – Profile – (Economics, Yale University, Connecticut, U.S.A.)
• Bernd Skiera – (Electronic Commerce, Goethe University, Germany, E.U.)
• Vernon L. Smith – Vernon Smith – (Economics, Economic Science Institute, Chapman University, California, U.S.A.)
• Erik Snowberg – (Stanford University, California, U.S.A.) — Soon at CalTech
• Peter Norman Sorensen – (Finance, Department of Economics, University of Copenhagen, Denmark, E.U.)
• Martin Spann – (Marketing and Innovation, University of Passau, Germany, E.U.)
• Koleman Strumpf – Koleman S. Strumpf – (Economics, School of Business, University of Kansas, Kansas, U.S.A.) – Formerly: University of North Carolina, North Carolina, U.S.A.) — Post Archives at Midas Oracle
• Cass Sunstein – Cass R. Sunstein – (Law School, University of Chicago, Chicago, Illinois, U.S.A.)
• James Surowiecki – (The Wisdom Of Crowds) – (New York, U.S.A.)
• Paul C. Tetlock – Paul Tetlock – (Finance, Business School, University of Texas at Austin, Texas, U.S.A.) — Post Archives at Midas Oracle
• Philip Tetlock – Philip E. Tetlock – (Leadership, University of California at Berkeley, California, U.S.A.)
• Hal R. Varian – Hal Varian – (Chief Economist, Google, California, U.S.A.) — Formerly: (Haas School of Business, Department of Economics, University of California at Berkeley, California, U.S.A.)
• Dorina Tila – (Economics, George Mason University, Virginia, U.S.A.)
• Leighton Vaughan-Williams – (Economics and Finance, Business School, Nottingham Trent University, United Kingdom, E.U.)
• Michael Wellman – Michael P. Wellman – (Computer Science and Engineering, University of Michigan, Michigan, U.S.A.)
• Justin Wolfers – Justin J. Wolfers – (Business and Public Policy, Wharton Business School, University of Pennsylvania, Pennsylvania, U.S.A.) — Post Archives at Midas Oracle
• Eric Zitzewitz – Eric W. Zitzewitz – (Economics, Dartmouth College, Massachusetts, USA) – Formerly: (Stanford University, California, U.S.A.) — Post Archives at Midas Oracle

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Inkling – URL: Inkling Markets – (Chicago, Illinois, U.S.A.)

• Adam Siegel — Post Archives at Midas Oracle
• Nathan Kontny

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NewsFutures – (Maryland, U.S.A. & Paris, France, E.U.)

• Emile Servan-Schreiber — Post Archives at Midas Oracle
• Maurice Balick

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Xpree – (U.S.A.)

• Mat Fogarty — Post Archives at Midas Oracle

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HP Services – HP Labs – (U.S.A.)

• Predicting the future –with games — Introductory article
• Information Dynamics Lab — Internal prediction markets
• BRAIN – (Behaviorallly Robust Aggregation of Information in Networks) — Scoring Rules (i.e., non-trading technique)
• Bernardo A. Huberman – Bernardo Huberman – Senior Fellow & Director
• Kay-Yut Chen -
• Google Search for “prediction markets”

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Hollywood Stock Exchange (HSX) & HSX Research – (L.A., California, U.S.A.)

• Prediction market consultancy firm
• Movie business

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Chris Hibbert – (California, U.S.A.)

• Chris Hibbert (Software architect / Zocalo project manager) — Post Archives at Midas Oracle
• Chris Hibbert’s personal website — Chris Hibbert’s personal blog —
• Chris Hibbert’s CommerceNet profile — (His stint there ended in mid-2006.)

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Robin Hanson – (George Mason U., Virginia, U.S.A.)

• Robin Hanson — Post Archives at Midas Oracle
• Robin Hanson does prediction market consulting work, and have no exclusive arrangements.
• “I’m more interested in helping groups that want to add lots of value to big decisions, versus groups that just want to dabble in a new fad.”

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Justin Wolfers – (U. of Pennsylvania’s Wharton business school, Pennsylvania, U.S.A.)

• Justin Wolfers — Post Archives at Midas Oracle
• Justin Wolfers takes on prediction market consulting work.
• The prediction market industry is “a case where the interaction between firm practice and academic research are reasonably close.”

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Koleman Strumpf – (U. of Kansas, Kansas, U.S.A.)

• Koleman Strumpf — Post Archives at Midas Oracle
• Koleman Strumpf can be approached to consult on prediction market projects.
• “Prediction markets help harness the knowledge of diverse groups. They have great potential as a tool for industry.”

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Michael Giberson – (Virginia, U.S.A.)

• Michael Giberson (energy economist, who is also an expert in prediction markets) — Post archives at Midas Oracle
• Knowledge Problem – Blog on economics, energy policy, more.

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Robert Hahn – (American Enterprise Institute, Washington, D.C., U.S.A.)

• Robert Hahn — Post Archives at Midas Oracle
• Robert Hahn does consulting focused on improving decision making in the private and public sector. “This work builds on our evolving understanding of prediction markets and other economic tools.”

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IntelliMarket Systems – (L.A., California, U.S.A.)

• Charles R. Plott – Charles Plott – (CalTech Inst., California, U.S.A.)

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Mercury Research and Consulting – (United Kingdom, E.U.)

• Jed Christiansen — Post Archives at Midas Oracle

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Ask Markets – (Greece, E.U.)

• George Tziralis — Post Archives at Midas Oracle

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Gexid – Global Exchange for Information Derivatives – (Germany, E.U.)

• Bernd Ankenbrand — Post Archives at Midas Oracle

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Nosco – (Danemark, E.U.)

• Jesper Krogstrup — Post Archives at Midas Oracle
• Oliver Bernhard Pedersen

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Qmarkets – (Israel)

• Noam Danon — Post Archives at Midas Oracle

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ProKons – (Germany)

• Peter Gollowitsch

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Hive Insight – (Raleigh-Durham, North Carolina, U.S.A. & London, U.K., E.U.)

• Robert Wilburn (ex-NewsFutures)

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Foresight Markets – (??)

• BPH Technologies

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NimaniX – (Israel)

• Elad Amir (CEO), Littal Shemer Haim (VP Business development), David Shahar (VP R&D)

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PrediCom – (London, United Kingdom, E.U.)

• Mikael Edholm

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Despite that one blip on the radar [New Hampshire], Strumpf said futures are still the best way to predict the way things will go from here.

Spot the SIDEBAR (which is not located on the sidebar, actually), and click on the little square, just below “video”, to watch the report.

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FOREMAN: I’ve got something I want you to take a look at. Look at this. It could be the price of a stock or a mutual fund. It isn’t. It’s the odds that a particular candidate, the red here is Hillary Clinton, who will become president of the United States. It’s called the predictive market. And while supporters say it’s no different than any other type of investment, for example, hog bellies or pork futures, or crude oil, it sure looks like gambling.

Economics Professor Koleman Strumpf is on the faculty at the University of Kansas School of Business to talk about it now. And in Las Vegas, another scholar in the art of predicting future events, Johnny Avello, the director of the Sports Book at Wynn, Las Vegas.

Let me start with you, professor. How well does this work? When people start betting online as to who’s going to win, does it work?

KOLEMAN STRUMPF, UNIVERSITY OF KANSAS: Yes, the markets have a really tremendous track record dating back at least on the online markets to 1988. And actually earlier, there were impromptu markets that existed outside of Wall Street in the early 20th century.

These things have been around for at least 100 years or probably more. 150 years. And with maybe only one or two exceptions that I can think of, they’ve done a just totally dead on job at forecasting.

[FOREMAN]: Long before polling existed, then you’re saying we had betting. And there were betting lines in newspapers. And if you want to know who’s winning the presidential race, that’s what you looked at?

STRUMPF: That’s exactly right. So in 1904, “The New York Times” reported on the front page what was going on at the Wall Street betting markets since there was no Gallup Poll that existed at that time.

FOREMAN: Johnny, why do you think that this is generally so successful compared to polling?

JOHN AVELLO, DIR. OF RACE & SPORTS, WYNN HOTEL: Well, first of all, you are taking actual bets. And you know, each person that puts their money up is a good indication of, you know, which way they like it.

When you’re doing polling, you know, that’s kind of an ambiguous way of finding out who the winner is because you’re getting a fraction of the people who you’re actually finding out who they like. So I like to call it money versus unpredictability.

FOREMAN: You’re saying the difference is that in a poll, somebody may say something that they believe in generally, or they think that the pollster wants to hear. But when they put money down, they’re going to really bet on what they think is going to happen?

AVELLO: It’s the real thing.

FOREMAN: It’s sort of interesting when you look at these different types of polls. There’s the In Trade system, which is out of Dublin, Ireland that’s sort of interesting. In Trade allows people to bet actual money and large amounts of it on all sorts of outcomes all around the world.

The Iowa markets is one the people have talked about a lot. Set up by a university there. Basically the Iowa markets allow people to bet in limited amounts of money. About $500. And it’s used simply to see how well this works for educational purposes mainly.

Professor, when we talk about these, though, why did polling ever become popular if betting works so well in telling how we would win?

STRUMPF: Well, I think a kind of — much like today, the newspapers, the media was always uncomfortable reporting these markets. There sort of was dubious legal status and maybe some moral issues with it.

Polling for whatever reason seems to have been morally acceptable to the media. And I think as a result when Gallup came around in the late 1930s, the betting markets kind of fell by the wayside. They never of course disappeared, but I think it was sort of a moral issue, the same kind of moral issues that I think arise today in thinking about gambling.

FOREMAN: So let me ask you this, Johnny. Why do you think it’s been so difficult this year, though? Because as far as we can tell, the betting lines have not done any better than the pollsters this year in predicting this election. It has been all over the map. And the betting has been all over the map.

AVELLO: Well, one thing to remember, Tom, is that when the book maker’s putting up a line, what they’re trying to accomplish is divided action. So they’re not trying to pick the winner because let’s take for instance if the book maker put up Hillary Clinton at one to two, and she was bet from to three to win the Democratic nomination. You know, and Obama won. People would say, wow, the book maker really got killed on that.

FOREMAN: The book maker’s not trying to predict it, but obviously, the gamblers are, the people who are betting on these things. And they haven’t done well this time, not compared to past elections. Why do you think that is? Why is this so hard to sort out?

AVELLO: Because they’re not always right. No one’s right at 100 percent of the time. I would say best case. You know, I know that history has shown that the bettor has done well on this. But to be perfectly honest with you, I think if you do 60 percent, you’ve done a great job of picking the winner.

FOREMAN: And Koleman, do you think there’s anything unique that’s making it harder for the betting markets to be as accurate as they had been in the past?

STRUMPF: Well, it’s obviously a close market and opinions are changing rapidly. I just want to kind of maybe extend a little on what Johnny just said in terms of thinking about what these markets mean.

The markets give us a probability of an event occurring. So even if, for example, Obama is a 80 percent favorite in the upcoming Wisconsin primary, which he is, that still means on the flip side that there’s a 20 percent chance that he’s not going to win. So the markets, again as Johnny had said, don’t – they – by sort of definition can’t predict an upset. An upset is a surprise which people hadn’t anticipated. So sometimes there are these quick shifts of opinion, which I — to the best of my knowledge, there’s no way to forecast that in advance.

FOREMAN: And this campaign has been just filled with them. Johnny, one last thing here. Any sense of where the smart money is going these days?

AVELLO: Well, let’s say that there’s been a shift. I believe the smart money was on Hillary Clinton early, and has shifted to Obama. But surprises do happen. And all you need to do is look at the Superbowl to find that out.

FOREMAN: Johnny, thanks so much. Koleman, as well. We appreciate you being here. And speaking of gambling, Madame Tussaud’s Wax Museum here in Washington is hedging its bet. And that kicks off our political side show.

The Journal of Prediction Markets

Editor:
Leighton Vaughan Williams

Associate Editors:
John Delaney – CEO InTrade
Bruno Deschamps – University of Bath
Olivier Gergaud РUniversit̩ de Reims Champagne-Ardennes
Robin Hanson – George Mason University
Charles Noussair – Emory University
Marco Ottaviani – Ex-London Business School
David Paton – Nottingham University Business School
Paul Rhode – University of Arizona
Emile Servan-Schreiber – CEO NewsFutures
Michael Smith – Canterbury University
Martin Spann – University of Passau
Koleman Strumpf – University of Kansas School of Business
Justin Wolfers – University of Pennsylvania
Erik Snowberg – Stanford Graduate School of Business
Eric Zitzewitz – Stanford Graduate School of Business

I’d answer “yes” to my questions stated in the title of this post. These guys should be judged on the quality of their intellectual output —me thinks. Period.

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Prediction Market Industry Association – (PMIA):

Prediction Market Industry Association – (PMIA)

Emile Servan-Schreiber T: +1 (443) 321-2700 E: ejss@newsfutures.com
Jed Christiansen T: +44 796 358 3663 E: jed.christiansen@mercury-rac.com
John Delaney T: +353 1 6200 300 E: john.delaney@intrade.com
Robin Hanson E: rhanson@gmu.edu
Justin Wolfers E: jwolfers@wharton.upenn.edu

I can understand that some, like Niall O’Connor, have reservations on this mixing of interests, though.

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But we should judge these scholars and professionals on what they deliver —not whom they talk to, or associate themselves with.

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Consensus Point’s conference on prediction markets – @ New York City, New York, U.S.A. – 2007-09-24

What Have We Learned from Market Design? – @ Yahoo! Research Silicon Valley, Santa Clara, California, U.S.A. – 2007-10-15

Koleman Strumpf’s conference on prediction markets – @ Kansas City, Kansas, U.S.A. – 2007-11-01

Money Tech conference = Technology and Money, Asset Management and Networks, Computing Horsepower and Trading + Prediction Markets – @ New York City, New York, U.S.A. – 2008-02-06~07

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Previous: The Second Workshop on Prediction Markets – by Yiling Chen

Consensus Point’s conference on prediction markets – @ New York City, New York, U.S.A. – 2007-09-24

What Have We Learned from Market Design? – @ Yahoo! Research Silicon Valley, Santa Clara, California, U.S.A. – 2007-10-15

Koleman Strumpf’s conference on prediction markets – @ Kansas City, Kansas, U.S.A. – 2007-11-01

Money Tech conference = Technology and Money, Asset Management and Networks, Computing Horsepower and Trading + Prediction Markets – @ New York City, New York, U.S.A. – 2008-02-06~07

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As mentioned previously at the Knowledge Problem, Inkling offers a public play-money prediction market. I stumbled across them a year or so ago, and because I’m interested in market design and prediction markets, I decided to try them out. Partly because playing the Inkling markets amuses me and partly because I started doing well, I’ve continue to play on Inkling. Eventually I wormed my way onto their Top Traders list, where I’ve remained for several months.

In the process of amassing my play-money riches, I’ve learned a few useful things about Inkling’s markets. Here I am “giving back” to the Inkling user community by sharing one of my secrets.

This is it: when a market offers you free, riskless profits, take them.

Obvious, right? The trick is in spotting the riskless profits. I recently was able to take some free, riskless profits when Inkling allowed two markets to be set up for the same event: the UEFA Champions League. It isn’t necessary for there to be two markets on the same event for arbitrage to work – I did something similar in the NCAA men’s basketball final four market – but the two market case makes the arbitrage process easier to understand.

By the way, as I write the markets are still live, and I have existing risky holdings in these markets. These stakes are standard bet-I-know-better-than-the-market plays which may or may not pay off. None of what I am about the explain involves taking risks, just taking profits.

The standard multiple result market at Inkling pays off at 100 units for each share of the winning outcome you hold at the closing of the market, and pays off at 0 for all other outcomes. In this case both markets will pay 100 in play-money units for picking the eventual winner in the 2006-07 UEFA Champions League, and 0 for shares in all other outcomes. Because two separate markets are running over the same event, when the prices of the markets diverge, there is a simple and obvious arbitrage opportunity. Of a pair of matching outcomes (Say “Chelsea wins”), buy the lower priced of the two and sell short the same number of shares in the higher priced market.

The final outcome doesn’t matter, because whatever you win on one you will lose on the other. Your entire profit is captured in the net proceeds of the buy low-sell high pair of transactions. Here is a simple example drawn from my trading report:

I sold into the first market at an average price of about $13.72 and bought in the second market at an average price of $10.91. The result: a $140.11 profit for a minute or two of effort. Not a lot of profit, but not too bad for riskless trading. (The short sale does tie up some credit, but since we’re dealing in a play-money exchange that doesn’t pay even play-money interest on your holdings of pretend cash, the only thing you sacrifice is other trading opportunities.)

Outside of the rare pair of auctions covering the same event, I’ve found similar riskless trading opportunities in the recent market to pick the final four teams in the NCAA men’s basketball tournament. The market would pay 100 for each of the four teams that reached the final four weekend. The market started even before the tournament seedings were announced, with a large number of possible teams, and shares traded on various market expectations. Curiously, early on the total value of all of the stocks summed to more than 400 even though owning one of each share was guaranteed to pay off exactly 400. (This is a clue that arbitrage opportunities are available.)

Once the tournament seedings were announced, it is clear that owning one of each share of all 16 teams in a single region would payoff exactly 100. If the share prices summed to less than 100, then buying one share of each team generates a payoff at the difference between the cost of the shares and 100. If the share prices for all teams in a region sum to more than 100, then selling one share each will generate profit to you at the difference between the proceeds of the sales and 100.

For simplicity, say that there are just two teams left in the “West Region.” Let’s call them, hypothetically, “UCLA” and “Kansas.” Buying one share of UCLA and one share of Kansas, will lock in a payoff of 100, so if the prices of the two teams sum to less than 100 then you can obtain riskless profits by making the purchase. (Or sell short if the prices sum to more than 100.)

While it may be less obvious, the underlying arbitrage is similar to the AC Milan example. At this stage of the contest, a share of “UCLA wins” is the logical equivalent of “Kansas loses.” Buying one share of UCLA and one share of Kansas is the logical equivalent of buying a stake in “Kansas loses” and a simultaneous stake in “Kansas wins” – with offsetting holdings your payoff doesn’t change based upon the outcome of the event, and your profit is risklessly captured from arbitraging the market at the time of the transactions.

Many Inkling markets are self-arbitraging in the sense that they automatically account for these interrelationships in pricing multiple outcome prediction markets. For example in the separate market to pick the eventual champion of the NCAA men’s basketball tournament, all prices automatically adjusted in response to any purchase or sale such that the sum of the prices always totaled exactly 100. (In fact, both of the two UEFA Champions League markets are self-abitraging within the markets, but I profitted by arbitraging between the two markets.

As an economist, my opinion is that such self-arbitraging markets likely exhibit superior efficiency properties that would make them desirable in real-money practice. (As a sometime experimentalist and aspiring prediction-market geek, I’d love to test that conjecture in an econ lab.) As an Inkling trader, however, I love to discover and exploit riskless trading opportunities in non-self-arbitraging markets.

[Okay, I’ll admit that I didn’t become an Inkling Top Trader via riskless arbitrage. I took many risky steps along the way, some of which paid off handsomely. But explaining that I often got lucky doesn’t appeal to my inner aspiring PM-geek.

BTW, in addition to pulling riskless profits out of the UEFA Champions League markets I was also carrying very substantial risky holdings in both Chelsea and Man U. Surely one or the other would win it all, right?

Ouch.]

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Koleman Strumpf:

[...] Rite-Solutions [...] runs an innovative ideas market (and I explicitly use this term since it is not a prediction market –they are trying to come up with ideas the firm may want to pursue).

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New York Times – Here’s an Idea: Let Everyone Have Ideas

At Rite-Solutions, the architecture of participation is both businesslike and playful. Fifty-five stocks are listed on the company’s internal market, which is called Mutual Fun. Each stock comes with a detailed description — called an expect-us, as opposed to a prospectus — and begins trading at a price of $10. Every employee gets $10,000 in “opinion money” to allocate among the offerings, and employees signal their enthusiasm by investing in a stock and, better yet, volunteering to work on the project. Volunteers share in the proceeds, in the form of real money, if the stock becomes a product or delivers savings.

Mr. Marino, 57, president of Rite-Solutions, says the market, which began in January 2005, has already paid big dividends. One of the earliest stocks (ticker symbol: VIEW) was a proposal to apply three-dimensional visualization technology, akin to video games, to help sailors and domestic-security personnel practice making decisions in emergency situations. Initially, Mr. Marino was unenthusiastic about the idea — “I’m not a joystick jockey” — but support among employees was overwhelming. Today, that product line, called Rite-View, accounts for 30 percent of total sales.

“Would this have happened if it were just up to the guys at the top?” Mr. Marino asked. “Absolutely not. But we could not ignore the fact that so many people were rallying around the idea. This system removes the terrible burden of us always having to be right.”

Another virtue of the stock market, Mr. Lavoie added, is that it finds good ideas from unlikely sources. Among Rite-Solutions’ core technologies are pattern-recognition algorithms used in military applications, as well as for electronic gambling systems at casinos, a big market for the company. A member of the administrative staff, with no technical expertise, thought that this technology might also be used in educational settings, to create an entertaining way for students to learn history or math.

She started a stock called Win/Play/Learn (symbol: WPL), which attracted a rush of investment from engineers eager to turn her idea into a product. Their enthusiasm led to meetings with Hasbro, up the road in Pawtucket, and Rite-Solutions won a contract to help it build its VuGo multimedia system, introduced last Christmas.

Mr. Lavoie called this innovation an example of the “quiet genius” that goes untapped inside most organizations. “We would have never connected those dots,” he said. “But one employee floated an idea, lots of employees got passionate about it and that led to a new line of business.”

NetCast index (CNBC – The Business Of Innovation): – Episode 1 – MP3 file — Streaming Video – Episode 1 – [Bo Cowgill's note: this Episode 1 video has four parts; the Rite-Solutions part is about 20% of the way through Part 3.]