InTrade

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Oregon’s Democratic Primary

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InTrade

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BetFair

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Kentucky’s Democratic Primary

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InTrade

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BetFair

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NewsFutures

Some delegates from FL or MI will be seated at the Democratic Convention.

© NewsFutures

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First look at individual states for the 2008 US presidential elections

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2008 US Presidential Election Winner – Individual

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2008 US Presidential Elections

Source: Dynamic, compound prediction market charts from InTrade

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Next US President

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Winning Party

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Female President?

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Democratic Candidate

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Republican Candidate

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Source: BetFair Politics Zone

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Barack Obama to win the Democratic nomination

© NewsFutures

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Hillary Clinton to win the Democratic nomination

© NewsFutures

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Next US President Will Be Democratic.

© NewsFutures

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Next US President Will Be Republican.

© NewsFutures

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Explainer On Prediction Markets

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Prediction markets produce dynamic, objective probabilistic predictions on the outcomes of future events by aggregating disparate pieces of information that traders bring when they agree on prices. Prediction markets are meta forecasting tools that feed on the advanced indicators (i.e., the primary sources of information). Garbage in, garbage out… Intelligence in, intelligence out…

A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election. The price of this event derivative can be interpreted as the objective probability of the future outcome (i.e., its most statistically accurate forecast). A 60% probability means that, in a series of events each with a 60% probability, then 6 times out of 10, the favored outcome will occur; and 4 times out of 10, the unfavored outcome will occur.

Each prediction exchange organizes its own set of real-money and/or play-money markets, using either a CDA or a MSR mechanism.

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More Info:

- The Best Resources On Prediction Markets = The Best External Web Links + The Best Midas Oracle Posts

- Prediction Market Science

- The Midas Oracle Explainers On Prediction Markets

- All The Midas Oracle Explainers On Prediction Markets

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To understand why Six Apart is releasing an open source version of Movable Type, we need to briefly revisit its past. Movable Type was once the darling of the blogosphere, especially from its original launch in 2001 to about 2004 (when licensing issues upset many bloggers). Since 2004/05, many bloggers have migrated to the open source WordPress – and perhaps of more concern, a lot of third party developers transferred their efforts from MT to WordPress. However to make up for the loss of momentum in the consumer market, Movable Type began to sow some seeds in the Enterprise market. Back in October 2006 Read/WriteWeb reported on the release of Movable Type Enterprise 1.5. Movable Type at that point was being positioned as an advanced tool, suitable for enterprises and power bloggers alike. What’s changed since then is that it now wants to be a social media platform, and the open sourcing will address some market concerns over licensing.

Midas Oracle is proudly powered by WordPress.org, MySQL and AquaFluid.

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.

Very good explainer.

John Delaney mentions Justin Wolfers and Robin Hanson. It could be that the papers he refers about were written by the Iowa Electronic Markets scholars, instead. (I could be damn wrong, possibly.)

As for the metaphor with the NYSE, I disagree —a prediction exchange executive should come up with the CME metaphor, rather.

Anyway. Good explainer for the masses —no pun intended.

Addendum: I forgot to say that JD makes mention of the Lance Fortnow’s claim. Previous: “If Virginia and Montana go democratic then the markets predicted every race correctly.”

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Midas Oracle – 9,227 – 16,008
Midas Oracle » Blog Archive » “If Virginia and Montana go democratic then the markets predicted every race correctly.” – 1,195 – 1,492
Midas Oracle » About – 924 – 1,317
Midas Oracle » Authors – 836 – 1,382
Midas Oracle – 409 – 511
Midas Oracle » Blog Archive » Prediction Markets Definitions – 223 – 316
Midas Oracle » Exchanges – Markets – 213 – 258
Midas Oracle – 199 – 222
Midas Oracle » Blog Archive » OPEN LETTER TO THE COMMENTERS OF THE MARGINAL REVOLUTION BLOG – 194 – 204
Midas Oracle » Blog Archive » Overcoming Bias dot com = Robin Hanson’s group blog on truth discovery and decision rationality – 173 – 221
Midas Oracle » Blog Archive » Reality check on TradeSports-InTrade’s recent statement – 149 – 197
Midas Oracle » Betting – 132 – 154
Midas Oracle » Blog Archive » Prediction Markets: Tapping the Wisdom of Crowds + Yahoo!’s new currency: Yootles + Yootopia project – 122 – 151
Midas Oracle » Blog Archive » No change: Mispricing is greater in illiquid markets – 114 – 151
Midas Oracle » Blog Archive » BetFair’s Annual Review 2006: How did the world’s #1 real-money prediction exchange (betting exchange) fare in 2005/2006? – 111 – 156
Midas Oracle » How To Post – 110 – 170
Midas Oracle » Blog Archive » Deep Throat on BetBrokers, “the World’s first independent betting brokerage” – 108 – 152
Midas Oracle » Blog Archive » Google Analytics is now tracking the Midas Oracle readers (and authors). – 102 – 145
Midas Oracle » Blog Archive » Arbitrage NewsFutures-TradeSports: Does Alex Forshaw ask the right question? – 99 – 129
Midas Oracle » Blog Archive » “If Virginia and Montana go democratic then the markets predicted every race correctly.” – 98 – 108
Midas Oracle » Blog Archive » H.R. 4411: Internet Gambling Prohibition and Enforcement Act – 96 – 122
Midas Oracle » Blog Archive » The Pascal’s Wager… as seen by TradeSports trader “Caveat Bettor”. – WARNING: THIS QUOTE WILL MAKE HISTORY. – 96 – 157

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NOTE: The numbers above for the individual blog posts (not pages) should be understood as ADDITIONAL to the pageviews of both the frontpage and the site feed.

COLOR ME PERPLEXED by Alex Foreshaw’s [snarky sic] rant about this recent TechCentralStation article. Alex [believes] the article was an ideological, intellectually dishonest knee-jerk defense of free markets. Did we read the same article?

The article I read mentioned that there’s no way to know if TradeSports screwed up this time or not. I suppose you might call that a defense of the markets. It is also true — and if Alex disagrees, he should publish his own notions of probability.

In my notion, there is no way of knowing if a single market is ‘right’ or not unless A) the market in question had a price of zero or one, or B) repeat trials can be used to determine the rate the favorite fails. Neither situation applies to the TradeSports election markets — the prices were never zero or one, and the election cannot be repeated.

In any case, that part of the article constitutes about 300 words out of 1,184. The remaining 75% actually trashes the notion of using market prices as probabilities. The authors argue that many buyers of Hillary 2008 contracts at $2.22 might think Hillary’s chances aren’t that good. Many even think they are zero. There’s a reason to buy these contracts even if you think she’s certainly headed for loss. If you can anticipate a future Hillary bubble — even if it is short-lived — you could re-sell your $2.22 contracts for a profit. That’s exactly what the authors did with Harold Ford contracts.

Yeah, the rest of the article did trash that notion, to some extent. Which made the opening three paragraphs of said article all the more bizarre:

In the wake of the Democratic takeover of the House and Senate, much hay has been made over the failure of the prediction market Tradesports (or the Iowa Electronic Markets for the more legalistically squeamish). Going into November 7^th, TS gave the Republicans a seventy percent chance of retaining the Senate. For much of the life of the contracts, the markets predicted both chambers staying in GOP hands. Were the markets wrong? The answer, unequivocally, is no.

The suggestion that Tradesports “missed” on the control of the Senate is based on a faulty understanding of probabilities. By Election Day, Senate control really came down to three close races, Virginia, Missouri, and Montana. A probability calculator shows that if the markets accurately gave each of those races about a 60% chance of going to the Democrat, then the odds that all three would go Democratic were only 22% (0.6 to the third power). Therefore, a rational person would choose the Democrat in the individual races, but would nonetheless choose the Republicans to retain control. [these are not independent events!] After all, the Republicans needed only one of those three, while the Dems needed all three. Only if we posit that the Democrats had an 80% chance to win each race would Senate control have been even a 50-50 proposition.

The fact that the election went the way it did does not mean that the probabilities on individual races were inaccurate. We can’t play out the election 10,000 times to find out the actual probabilities. If we could, John Tester would lose some of the time, Jim Talent would win some of the time, and George Allen might—might!—have made a smart campaign decision once in a while. In the end, the Republicans would probably keep the Senate more often than not—just like Tradesports indicated. [if... the... events... are... independent... which... they're not] [all emphases mine--ed]

“Were the markets wrong? The answer, unequivocally, is no.” Interesting way to open an article that dwells on all the imperfections of the market, and why, under the impossible and unverifiable condition of “if the markets coulda done it 10,000 times over, their numbers woulda been proven right.”

I am tired of people saying that “the prediction markets got it right” every single time, using different metrics for each instance, when if you use the same metrics across time, they clearly haven’t gotten it right all of the time. If you switch your metrics around–making your t=0 at midnight before the election started, or 5pm when election returns came in, or whatever–you can always find some numbers to back up your conclusion, even if the aggregate of numbers points against your conclusion.

We can’t run the experiment 10,000 times. We ran it once. The outcome doesn’t mean “the market was wrong”–the possible outcomes should fall on a normal (or some such) distribution of seats changing hands, and we have no way of knowing what the mean and stdev are because our sample size is 1. However, by indicating a 70% likelihood of GOP Senate retention, the market implied that the Republicans would lose 3-4 Senate seats, not six. The market effectively said that the mean outcome should be that the Republicans will hold the Senate 70% of the time, and Democrats will win in 30% of trials. The actual outcome was in the 30% and not the 70%. (IMO–the Republicans were lucky they didn’t lose at least 1 more Senate seat and 5 more House seats that night, judging from all public information and a smidgen of private information up to the preceding midnight.) That this outcome fell within the 30% and not the 70% strikes me as problematic, because if that happens again (and again…), it cripples the popular conception of prediction markets’ predictive power, at least where politics is concerned.

My point is that spinning this as a positive outcome, or as some kind of validation for predictive markets is unjustified. You can’t have your cake and eat it too, arguing a la Lance Fortnow that the markets “predicted every outcome correctly,” and also say that GOP.SENATE.06 was correctly priced by the market. The difference between the two is the degree of intercorrelation, and it is no validation of prediction markets that the actual outcome fell considerably far out on the less likely end of the expected-outcome distribution.

In the language of y’all academics and diploma-endowed types, we are unable to reject the null hypothesis, that the market mispriced the probability distribution. To do otherwise seems indicative of missing data that disputes your thesis, a.k.a., unconscious bias if not pure error.

(cross-posted from the TS Maven)

A perfectly efficient, zero-alpha market can only come about through an inefficient labor market, because traders are significantly underperforming what they could make in other job sectors, based on their average education investment. (For that I must acknowledge Carl, also known as The Man--if I understood his teaching correctly.)]

But, there I go digressing again. Here is what the TCS article said:

The suggestion that Tradesports “missed” on the control of the Senate is based on a faulty understanding of probabilities. By Election Day, Senate control really came down to three close races, Virginia, Missouri, and Montana. A probability calculator shows that if the markets accurately gave each of those races about a 60% chance of going to the Democrat, then the odds that all three would go Democratic were only 22% (0.6 to the third power). Therefore, a rational person would choose the Democrat in the individual races, but would nonetheless choose the Republicans to retain control. After all, the Republicans needed only one of those three, while the Dems needed all three. Only if we posit that the Democrats had an 80% chance to win each race would Senate control have been even a 50-50 proposition.

Implicit in that argument of TCS’ self-proclaimed probability experts, of course, is the assumption that those three races are totally independent events. Which they aren’t. [After posting the original at the TS Maven, I realized that Bo Cowgill, David Pennock and others jumped on this well before I did.] It would be much more accurate to say that Tradesports significantly underestimated the intercorrelation between the three races. Which is a pretty far cry from what TCS wants to print–but that’s the way it is.

TCS’ agenda to promote free markets is great. But it shouldn’t portray them as an omniscient distillation of all knowledge, because markets suffer from many of the same imperfections that individual humans do–it just takes a lot more information asymmetry to bring those imperfections (herd behavior, irrational risk aversion, blah blah) out of the woodwork. I’m sure there’s a marketing rule that says, “Never inflate expectations too far beyond what your product is capable of.”

While the Political futures got the House right, it appears they were wildly wrong about the Senate. That would be quite a blow to these thinly traded, tiny markets. And of all the things they tend to get right, elections are their forte. If it turns out they blew this, I would venture to say it would lower their profile dramatically. This is not a “told-you-so” — but one will be forthcoming if GOP control of the Senate falls.

For those who believe markets are perfect prognosticators of the future, you are strongly advised to reread The kinda-eventually-sorta-mostly-almost Efficient Market Theory.

Not So Fast Mister Ritholtz: Professor Lance Fortnow made the point that, taken one day before Election Day, the TradeSports’s prediction markets of the individual races for the US Senate were accurate (provided that Virginia and Montana go democratic).

A Short Note About Accuracy: It makes no sense at all to aim for absolute accuracy. It’s understood by everyone (except by some decerebrated idiots who believe in the reversal of the psychological arrow of time) that THE FUTURE CAN’T BE PREDICTED PRECISELY, BY ESSENCE. Once that is said, let’s focus on relative accuracy (i.e., the prediction markets versus some comparable institutions). WERE BARRY RITHOLTZ’S 2006 POLITICAL PREDICTIONS ACCURATE? I will let him answer, but a close examination of the 2004 situation showed that our Wall Street pundit did not pass the test.

#1. Professor Lance Fortnow made a specific point: taken one day before Election Day, the TradeSports’s prediction markets of the individual races for the US Senate were accurate (provided that Virginia and Montana go democratic).

#2. Professor Lance Fortnow DID NOT SAY that the TradeSports’s prediction market for the control of the US Senate was accurate. Please, don’t put words in his mouth.

#3. Analysis reports from economists and statisticians are coming, but, please give them time to digest the data… once the dust has settled.

Thanks for your attention,

Chris Masse