I will soon publish a full post on the relative accuracy of the VP prediction markets (and whether the prediction markets are useful at all, taking into account the occasional upsets theorized by Koleman Strumpf) —-tackling Paul Kedrosky, Felix Salmon, and Barry Ritholtz.
- By the way, I was pleased to see (elsewhere) that Emile Servan-Schreiber of NewsFutures has the same (bad) opinion about those VP prediction markets as I do. I will index Emile in this file, next week. Emile is a smart and experienced man, and we have the confirmation of this under our very nose (elsewhere), once again. Emile Servan-Schreiber = one of the great thinkers of the field of prediction markets.
- I was also pleased to see that our good Wall Street friend Eddy Elfenbein got things right.
Now, back to the PM-bashing Paul Kedrosky post:
Paul Kedrosky criticizes the “-boosters”- of the prediction markets —-Justin Wolfers, Robin Hanson, John Delaney, Chris Masse, etc.
As for me, I dislike opportunistic bloggers and venture capitalists like Paul Kedrosky who approach the prediction markets without a basic understanding of the forecasting approach.
Here is the explainer that I have been publishing on the frontpage of Midas Oracle, for all to see.
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.
Finally, for those who missed it, the great interview given by Koleman Strumpf to CNN, in April 2008.
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 [prediction] 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 putt
ing 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.
Back to the PM-bashing Paul Kedrosky post.
Here’-s the sneaky, un-informed and totally biased ( ) comment from our good friend Jason Trost (who should know better), which was attached to the Paul Kedrosky post:
UPDATE: Portfolio + Computational Complexity
One week later, the debate is still raging under his post.
New comments from David Pennock and Jason Ruspini.
I like when people argue with each other. It generates pageviews. Commenters frenetically download that webpage to see whether their opponents have added something.
- The structure of InTrade’-s commissions …-?…-
- The design of InTrade’-s multi-outcome markets …-?…-
Previous blog posts by Chris F. Masse:
- The best research papers on prediction markets
- 2008 Electoral Map
- American Enterprise Institute’s Center For Regulatory And Market Studies (Policy Markets)
- IIF’s SIG on Prediction Markets
- Mozilla FireFox users, do you have trouble downloading academic papers (as PDF files) from SSRN?
- “Impact Matrix. Used to collect and gauge the likelihood and business impact of various events in the very long term.”
Awesome slides in bold.
Brought to you by Koleman Strumpf (circa November 2007):
Henry Berg, Microsoft <-slides>-
Discussant: Robin Hanson (George Mason Department of Economics) <-slides>-
Christina Ann LaComb, GE (The Imagination Market- abstract is free, text is gated) <-slides>-
Discussant: Marco Ottaviani (Kellogg School of Management, Management and Strategy) <-slides>-
Dawn Keller, Best Buy (Best Buy’s TAGTRADE Market) <-slides>-
Bo Cowgill, Google (Putting Crowd Wisdom to Work) <-slides>-
Jim Lavoie, Co-Founder and CEO, Rite-Solutions <-slides>-
David Perry, Co-Founder and President, Consensus Point <-slides>-
Mat Fogarty, Founder and CEO, Xpree Inc <-slides>-
Tom W. Bell, Chapman University School of Law <-slides>-
Previous blog posts by Chris F. Masse:
- A second look at HedgeStreet’s comment to the CFTC about “event markets”
- Since YooPick opened their door, Midas Oracle has been getting, daily, 2 or 3 dozens referrals from FaceBook.
- US presidential hopeful John McCain hates the Midas Oracle bloggers.
- If you have tried to contact Chris Masse thru the Midas Oracle Contact Form, I’m terribly sorry to inform you that your message was not delivered to the recipient.
- THE CFTC’s SECRET AGENDA —UNVEILED.
- “Over a ten-year period commencing on January 1, 2008, and ending on December 31, 2017, the S & P 500 will outperform a portfolio of funds of hedge funds, when performance is measured on a basis net of fees, costs and expenses.”
- Meet professor Thomas W. Malone (on the right), from the MIT’s Center for Collective Intelligence.
hile prediction markets have been in the spotlight this year, they are still unfamiliar to many folks. As one small step towards improving their visibility, along with my colleague James Lemieux I ran a prediction market at the University of Kansas School of Business. The markets ran for three and a half months and almost all traders were undergraduate business majors (you can see the very end stages of the market at: http://kufin400.inklingmarkets.com, username: myfoxkc and password: myfoxkc).
These markets were quite popular. The 475 traders made over 27,000 transactions in the 139 available markets. As a matter of reference, that is about 200 transactions per market while in Google’-s market this ratio is 260.
There was a mix of both socially redeeming topics (issues of interest to the Business School such as how many internships the undergrads would get this school year) and others designed to attract interest (politics, sports, entertainment, finance). I was surprised to see that passions–- and trade volume–- ran quite high even in the more serious markets. For example, one contract’-s expiry was based on whether the XM-Sirius merger would be consummated by March. When the DOJ announced its approval at the end of that month, there was only a small price increase. As the comments below suggest, this was not because the traders were asleep at the wheel but rather because they had a good understanding of the regulatory environment.
Inkling Markets provided the platform for our markets (if you are unfamiliar with Inkling, they have active public markets which you can sample). Inkling’-s software and support is really ideal for classroom markets. There are nice features for both the people running the markets (James and I) as well as for traders (the students).
For the market admin:
– it is a snap to set-up and administer new contracts
– Adam Siegel and Nate Kontny are very responsive to questions, often responding within the hour
– an intuitive trade interface, which is accessible even for those without experience with financial markets (though this can be a drawback if you would also like students to become familiar with order books)
– lots of goodies (customizable profile pages, market-specific discussion boards, graphs) leads students to visit the market a lot
– the daily/weekly top traders list encourages participation
I would strongly recommend others give prediction markets in the classroom a try. I found them to be both a great pedagogical tool and also one which the students really, really like. Students learned first hand about the role of information discovery as well as the biases often seen in prediction markets (though I will add it was difficult to illustrate the home town bias given the success of the athletic teams at my school this year). Feel free to get in touch with me if you have questions about how to set-up your own classroom markets.
Let’-s hope that that WSJ columnist is right.
Another MSM journalist who draws heavily on Koleman Strumpf’-s work and does not cite him, or his colleague (Paul Rhode).
What a shame.
PDF file of the paper on historical prediction markets
Ah, Kansas…- Barbecues, pickup trucks, rednecks, country music, and…- the local FOX News.
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.