1. Bob and Chris has four elections in their data, so it is hard to draw too much from it. That said, I draw two conclusions. First, markets beat an unconditional use of polls as forecasts. Second, correcting the polls, the two are pretty darn close. Based on a sample of four elections, I’m not sure I am willing to call one method or the other the winner.

2. My conclusion is that this actually tells us a lot of what prediction markets do: they digest and aggregate the polls, and create a pretty useful adjusted forecast. If I don’t have the time to do the careful aggregation that Bob and Chris have, then I’m glad to have the market to do this for me. So I interpret their paper as telling me something about the mechanism by which prediction markets do well in forecasting elections. (This suggests an interesting puzzle: why did prediction markets do well in the pre-polling era?)

3. Your comment that polls are a snapshot, not a forecast, strikes me as a bit beside the point. Many people use polls as a forecast, and so it is reasonable to ask if they are a good forecast. (And pollsters sell them as forecasts, until you suggest they don’t do well, and then they fall back on the “snapshot” argument.)

4. Bob and I have a friendly bet (I can’t remember whether it’s a bottle of wine, or not), on whether his results will hold up in a larger sample. Given that we have both state-by-state polls and state-by-state markets for the 2004 and 2008 election cycles, one can extend his approach so that we get around 100 markets (rather than four). We are planning to work together on this. My guess is that this larger sample will confirm the view that markets beat even Erikson-adjusted polls, although Bob’s guess is the opposite.

Point is, the facts can resolve our debate, and we are planning on doing exactly that. I think it will be fun to work with Bob on this, as he is clearly much more deeply embedded in many of the issues around political forecasting than I am. It should be a fun learning opportunity. [...]

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I think that the strongest evidence for prediction market efficiency comes from sports betting markets. There’s a ton of data out there, and while there are some smallish anomalies (including the favorite-longshot bias), the evidence in favor of the performance of markets v. experts is really very strong there. As you know, I grew up working for bookies, so this really was where I began. My colleagues in finance would suggest that the real evidence comes from equity / bond / futures / options markets, but there are enough well-known anomalies in those domains that I’m a bit less convinced. (And if you were making the rhetorical point that prediction market aficionados often overstate the evidence for their beliefs, I completely agree.)

I hadn’t really coded your piece with Chris as being driven by the favorite-longshot bias, but now you say it, the point is obvious. I’ll make sure to reference your paper in the draft that Eric and I are working on about favorite-longshot biases in political markets generally. (I think you saw early work on this, while in Palm Desert.) For Andrew’s benefit: we have collected data on literally hundreds of political prediction markets, and find fairly pervasive evidence of a favorite-longshot bias. The work on that paper isn’t done yet, but the message is very unlikely to change. There is a very strong favorite-longshot bias in political prediction markets.

BTW, I’m willing to make an even stronger forecast than Bob: I think the unadjusted markets will outperform even the Erikson-adjusted polls. (And for sure, the Wolfers-adjusted markets will beat the Erikson-adjusted polls.) And yes, I’m partly informed on that score by my experience with sports betting markets.

In terms of your question as to whether markets include info not in polls, I think you are right to infer that a lot of what markets are doing is following polls. Equally, the strongest evidence of markets doing some independent info aggregation comes from looking at political prediction markets in the pre-polling era. (If you haven’t seen it, I think you will really enjoy Rhode and Strumpf’s paper in the Journal of Economic Perspectives, where they review political prediction markets back to 1880.)

Also, I think that there are some pretty clear examples of the markets getting ahead of polls. Take Fred Thompson, who still does moderately well in national polls, but who the markets have written off. Obviously the polling result is driven by pure name recognition, and so what the markets are doing here is appropriately discounting the predictable polling errors, but they are using non-poll info to do this. (eg They don’t think Hillary’s advantage will also dissipate.) [...]

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Are Political Markets Really Superior to Polls as Election Predictors? – (draft: PDF file) – by Robert S. Erikson and Christopher Wlezien – 2008-05-02

MO excerpts

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1. The contract had been trading between 23 and 26 all year. It has consistently been about half the price of the “Hillary to get nominated” contract price. This ratio implies that, conditional on nominating Hillary, the Democrats have a 50 percent chance of winning the Presidency.

2. Comparing this with the unconditional probability of a Democratic victory (about 56 percent throughout 2007) suggests that Hillary is a slightly weaker general election candidate than the alternatives (Obama, mainly). [Note I say "suggests" because the comparison of conditional probabilities implies a correlation, but not necessarily that a Clinton nomination would cause a better outcome for the GOP. For more see the fifth question in this paper].

3. Around May 12, someone started buying “Hillary for President” pretty heavily, driving the price up to 40. This price is clearly ridiculous for two reasons:

3a. You could sell the President contracts of Hillary, Obama, Gore, and Edwards for a combined 69 (40+17+8+4) and buy the “Democrat to win” contract for 56.

3b. Since there was no movement in the nomination contract, the conditional probability of Hillary was now a ridiculous 40/52 = 77%, while the conditional probability of “Not Hillary” was 16/48 = 33%.

4. Unlike past manipulation attempts, this manipulator isn’t just dumping a ton of money in to move the price once. He (or she) is moving the price, and then providing support to keep the price high. Note that the price stayed at 40 for about a week (on higher than normal volume).

5. I mentioned the manipulation at the end of my talk at the Palm Desert prediction markets conference, figuring that there was no surer way to get a $100 bill picked up than to tell that crowd about it. Someone emailed Greg Mankiw and he blogged about it the next day. (Justin and I also just tipped off Tyler Cowen). Since then there has been some downward price pressure, but the manipulator isn’t throwing in the towel. He/she keeps replenishing the bid side of the order book, albeit giving ground in the process.

6. By my calculation, the manipulator has spent about $10k to push the Hillary contract up around 12 pts on average for 2 weeks, buying about 8,500 contracts in the process. [I'm assuming 26 is fair value and just summing up volume*(price - 26)].

7. So what do we learn from this?

7a. Manipulation doesn’t have to be as ham fisted as the 2004 Bush reelection contract manipulation.

7b. The manipulators are getting smarter. This manipulator was smarter in one sense by providing price support after the fact. But of course, he/she shouldn’t have pushed the price up to such an obviously ridiculous level (and should have bought and sold other contracts to keep the pricing relationships consistent). The same mistake probably won’t be made next time.

7c. By prediction markets standards, manipulation is expensive. But by political spending standards, manipulation could be reasonably cheap. That said, I can find only one media mention of the inflated Hillary price. $10k for one blog mention probably isn’t great value for money, but the Intrade prices get cited a lot these days, so the manipulator may just have been unlucky.

7d. None of this disputes Hanson and Oprea’s point that, if anticipated, manipulation could increase average prediction market accuracy. In their model, traders all have rational expectations about how much manipulation to expect. In the real world, they may need some help (hence this post).

7e. Although the Hillary price is down to 34.5 (bid-ask midpoint at time of writing), there are about 500 contracts bid above 33, so there is still plenty of free money there if you want it.

The quality of presenters is excellent and the workshop has succeeded in attracting high level contributions. The program and complete list of papers to be presented are available here, while directly related to prediction markets papers are:

• Are Political Markets Really Superior to Polls as Election Predictors? by Bob Erikson, Columbia University and Chris Wlezien, Temple University
• Optimal Price Setting in Fixed-Odds Betting Markets Under Information by Raphael Markellos and Vasiliki A. Makropoulou, Athens University of Economics and Business
• Hitting Home Runs and the Art of Corporate Valuation: Do Managers or the Prediction Market Make Better Predictions? by Orly Sade, New York University and Hebrew University
• Prediction Markets as a Tool for Management of Political Risk by Ole Jakob Bergfjord, Norwegian School of Economics
• Public Signal Bias and Prediction Market Accuracy by Tom Gruca and Joyce E. Berg, University of Iowa
• Ignorance Prior Bias in Prediction Markets by Lionel Page, University of Westminster
• An Analysis of Tradesports’ 2005-06 National Football League Prediction Market by Feng Zhou and Philip O’Connor, University of Waikato
• Testing the Efficiency of Markets in the 2002 World Cup by Steve Levitt, University of Chicago and Ricard Gil, University of California at Santa Cruz
• Price Formation in Sports Prediction Markets: A Cross-Cultural Study by Stefan Luckner, University of Karlsruhe
• Socially Embedded Prediction Markets by Yiling Chen and David M. Pennock, Yahoo! Research
• Manipulators Increase Information Market Accuracy by Robin Hanson, George Mason University and Ryan Oprea, University of California, Santa Cruz
• Is There a Favorite-Longshot Bias in Election Prediction Markets? by Justin Wolfers, University of Pennsylvania, Eric Zitzewitz, Stanford University and Andrew Leigh, Australian National University
• Using Markets to Inform Policy: The Case of the Iraq War by Eric Zitzewitz, Stanford University and Justin Wolfers, University of Pennsylvania

The exponential pattern of growth recorded in my literature review of the field remains consistent, at least.

Cross-posted from gtziralis.com.

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Second Workshop on Prediction Markets – (Details) – EC’07 - ACM Conference on Electronic Commerce – @ San Diego, California, U.S.A. – 2007-06-11~15

The Growth of Gambling and Prediction Markets: Economic and Financial Implications – (Program) – @ University of Riverside, Palm Desert, California, U.S.A. – 2007-05-20~22

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List of conferences at CFM — Event-Mettings post category at Midas Oracle

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Gambling: Design and Implementation 1
- “An Examination of the Determinants of Biased Behaviour in a Market for State Contingent Claims” Professor Johnnie Johnson, Southampton University Professor Alistair Bruce, University of Nottingham Jiejun Yu, University of Birmingham
- “Non-Expected Utility Models and Heterogeneity in Risk Attitudes: Towards an Explanation of Gambling Outcomes for Individuals and Markets” Professor David Peel, Lancaster University
- “Evidence of a Weekend Effect in a Market for State Contingent Claims” Professor Johnnie Johnson, Southampton University Dr. Ming-Chien Sung, Southampton University
Gambling: Design and Implementation 2
- “An Examination of the Economic Behavior In Buying Lottery Tickets” Professor Drew B. Winters, Texas Tech University
- “Why Score Probability Forecasts Categorically? Empirical Evidence of Increased Betting” Professor David Johnstone, University of Sydney Professor David Grant, University of Sydney
“Optimal Betting Strategies for Simultaneous Games” Professor Andrew Grant, University of Sydney
Gambling: Design and Implementation 3
- “Are Political Markets Really Superior to Polls as Election Predictors?” Professor Bob Erikson, Columbia University
- “Under Information Uncertainty” Professor Raphael Markellos, Athens University of Economics and Business
- “Betting on Yourself … and Losing: The Economics of Weight Loss Bets” Professor John Lynham, University of California, Santa Barbara
Gambling and Public Policy 1
- “Gambling with Public Monies: Would a Prediction Market Improve the Performance of the SBIR Program?” Professor Albert N. Link, University of North Carolina at Greensboro Professor John T. Scott, Dartmouth College
- “The Lure of Gambling: Wellspring of Revenue or a Faustian Bargain” Professor Richard McGowan, Boston College
- “Competition and Monopoly in the Gambling: Market-A Theoretical Approach” Professor Herbert Walther, Vienna University of Economics and Business Administration
Gambling and Public Policy 2
- “Different Country, Different Millennium, Different Technology. Same Subsidy. Same Result?” Professor Ramon DeGennaro, University of Tennessee
- “What Does a Concept Attract? The Case of Gaming in Macau” Professor Raymond So, Chinese University of Hong Kong
- “An Economic Analysis of Problem Gambling, The Gambler’s Fallacy, and the Taxation of Gambling” Professor John Pierson, University of Kent
Gambling and Public Policy 3
- “Is Casino Gaming a Productive Sector? A Conceptual and Cross-Jurisdiction Analysis” Professor Ricardo Siu, University of Macau
- “Productivity Measurement in Gambling: Plant-level Evidence from the United Kingdom” Professor David Paton, University of Nottingham Professor Donald Siegel, University of California at Riverside Professor Leighton Vaughan Williams, Nottingham Trent University
- “The Impact of Casino Gambling on Housing Markets: A Hedonic Approach” Professor Mike Wenz, Winona State University
Casinos and Economic Growth
- “The Casino Effect: Do Casinos Spur Growth and Which Communities Benefit?” Professor Chad D. Cotti, University of South Carolina
- “The Effect of Legalized Gambling on State Tax Revenue” Professor Douglas Walker, Georgia College
- “Spatial Characteristics of Gambling Expansion – An Analysis of Cross Border Effects” Professor Shannon Neibergs, Washington State University
Horse Racing
- “Inferring Risk Preferences Using Synthetic Win Bets in Horse Betting ‘Exotic’ Markets” Professor Philip O’Connor, University of Waikato
- “The Parimutuel Kelly Probability Scoring Rule” Professor David Johnstone, University of Sydney
- “Efficiency and Arbitrage Across Parimutuel Pools” Professor Marshall Gramm, Rhodes College
Prediction Markets and Public Policy 1
- “Hitting Home Runs and the Art of Corporate Valuation: Do Managers or the Prediction Market Make Better Predictions?” Professor Orly Sade, New York University and Hebrew University
- “Spread Betting as a Self-Enforcing Implicit Cartel” Professor Schnytzer, Adi, Bar Ilan University Professor Avichai Snir, Bar Ilan University
- “Prediction Markets as a Tool for Management of Political Risk” Professor Ole Jakob Bergfjord, Norwegian School of Economics
Prediction Markets and Public Policy 2
- “The Growth of Gambling and Prediction Markets: Economic and Financial Implications” Professor Alexander Koch, Royal Holloway College-University of London
- “Public Signal Bias and Prediction Market Accuracy” Professor Tom Gruca, University of Iowa
- “Ignorance Prior Bias in Prediction Markets” Professor Lionel Page, University of Westminster
Sports Betting 1
- “Ambiguity, Rules, and Asset Prices: The College Football Wagering Market” Professor Ron Mau, Western Carolina University
- “Does Sportsbook.com Set Pointspreads to Maximize Profits? Tests of the Levitt Model of Sportsbook Behavior” Professor Rodney J. Paul, St. Bonaventure University Andrew Weinbach
- “Market Efficiency and Internet Betting: Evidence from European Football” Professor Bruno Deschamps, University of Bath
Sports Betting 2
- “The Economics of the Setting of Bookmakers’ Odds” Professor John Pierson, University of Kent
- “An Analysis of Tradesports’ 2005-06 National Football League Prediction Market” Professor Feng Zhou, University of Waikato TBA Professor William H. Dare, Oklahoma State University
Sports Betting 3
- “Testing the Efficiency of Markets in the 2002 World Cup” Professor Steve Levitt, University of Chicago Professor Ricard Gil, University of California at Santa Cruz
- “The Impact of Sentiment on Point Spreads in the College Football Wagering Market” Professor Greg Durham, Montana State University TBA Professor Victor Matheson, College of the Holy Cross
Favorite-Longshot Bias
- “An Alternative Explanation of the Favorite–Longshot Bias” Professor Stefan Winter, Ruhr-Universität Bochum
- “The Link between Information and the Favorite-Longshot Bias in Pari-mutuel Wagering Markets” Professor Andy Weinbach, Coastal Carolina University
- “Anyone for Tennis (Betting)?” Professor Ian McHale, Salford University
Gambling and Prediction Markets: Experimental Methods
- “Time-inconsistent Risk Preferences in a Laboratory Experiment” Professor Jeremy Ko, Penn State
- “The Effect of Prior Beliefs and Outcomes on Information Processing in an Investment Experiment” Professor Jeremy Ko, Penn State
- “Forecasting Volume of Transactions in a Betting Market: An Experiment” Professor Loreto Llorente, Universidad Pública de Navarra
Gambling and Prediction Markets: Cross Cultural and Sociological Perspectives
- “Price Formation in Sports Prediction Markets: A Cross-Cultural Study” Professor Stefan Luckner, University of Karlsruhe
- “Socially Embedded Prediction Markets” Dr. Yiling Chen, Yahoo! Research
- “Do More Opportunities Lead to More Gamblers? Explaining the Growth in Gambling in the U.S. from a Cultural Perspective” Professor Andrew Economopoulos, Ursinus College
Plenary Session
- “Manipulators Increase Information Market Accuracy” Professor Robin Hanson, George Mason University
- “Is There a Favorite-Longshot Bias in Election Prediction Markets?” Professor Justin Wolfers, University of Pennsylvania
- “Using Markets to Inform Policy: The Case of the Iraq War” Professor Eric Zitzewitz, Stanford University

Organizers: Donald Siegel, David Paton, Leighton Vaughan-Williams and William Ziemba

Very impressive.

Plenty of papers that I will read and excerpt ASAP.

Feel free to publish a review of a paper on Midas Oracle, if you wish.

Psstt… Don Imus was born in Riverside, California.

#2. Conference at University of Riverside, Palm Desert, California, U.S.A.: May 20~22, 2007.

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