When Markets Beat The Polls – Scientific American Magazine

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Via Mat Fogarty of Xpree (an innovative firm providing software for enterprise prediction markets), the Scientific American magazine on prediction markets &#8211-&#8221-When Markets Beat the Polls&#8221-.

Ask me by e-mail to get a copy of the PDF file.

Abstract:

When Markets Beat the Polls– March 2008- Scientific American Magazine- by Gary Stix- 8 Page(s)

In late March 1988 three economists from the University of Iowa were nursing beers at a local hangout in Iowa City, when conversation turned to the news of the day. Jesse Jackson had captured 55 percent of the votes in the Michigan Democratic caucuses, an outcome that the polls had failed to intimate. The ensuing grumbling about the unreliability of polls sparked the germ of an idea. At the time, experimental economics&#8211-in which economic theory is tested by observing the behavior of groups, usually in a classroom setting&#8211-had just come into vogue, which prompted the three drinking partners to deliberate about whether a market might do better than the polls.

A market in political candidates would serve as a novel way to test an economic theory asserting that all information about a security is reflected in its price. For a stock or other financial security, the price summarizes, among other things, what traders know about the factors influencing whether a company will achieve its profit goals in the coming quarter or whether sales may plummet. Instead of recruiting students to imitate buyers or sellers of goods and services, as in other economics experiments, participants in this election market would trade contracts that would provide payoffs depending on what percentage of the vote George H. W. Bush, Michael Dukakis or other candidates received.

Robin Hanson had more.

Polls vs. Prediction Markets

IEM Track Record

OSCARS 2008: The Hollywood Stock Exchange has been more accurate than InTrade.

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&#8230- dixit James Surowiecki (commenting on Felix Salmon&#8217-s post):

Interesting. The Hollywood Stock Exchange, not surprisingly, did better [than InTrade]. Cotillard was, as at Intrade, a comfortable second favorite. But so too was Swinton &#8212- in fact, she was even more of a second favorite, as her price was very close to Blanchett&#8217-s when the market closed. In fact, if you look at her chart:
http://movies.hsx.com/servlet/SecurityDetail?symbol=A8TSW

it&#8217-s hard not to conclude that the market was really incorporating new information in the week leading up to the ceremony.

I didn&#8217-t follow the Hollywood Stock Exchange [*] (or even BetFair) closely for the Oscars 2008, but here are InTrade&#8217-s expired event derivatives (event futures):

Best Picture

Best Director

Best Actor

Best Actress

[*] UPDATE: HSX claims a 75% success rate.

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&#8230- Intelligence in, intelligence out&#8230-

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.

Linear Programming – Combined Value Trading – Parimutuel Call Market – Combinatorial Call Markets

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David Pennock:

[…] Each order is associated with a decision variable x that ranges between 0 and 1, encoding the fraction of the order that the auctioneer can accept. There is one constraint per outcome that ensures that the auctioneer never loses money across all outcomes. The choice of objective function depends on the auctioneer’s goals, but something like maximizing the fill fraction makes sense.

Once the program is set up, the auctioneer solves for the x variables to determine which orders to accept in full (x=1), which to accept partially (0&lt-x&lt-1), and which to reject (x=0). The program can be solved either in batch mode, after waiting to collect a number of orders, or in continuous mode immediately as new orders arrive. Batch mode corresponds to a call market. Continuous mode corresponds to a continuous auction, a generalization of the continuous double auction mechanism of the stock market.

Each order consists of a price, a quantity, and an outcome bundle. Traders can just as easily bet on single outcomes, negations of outcomes, or sets of outcomes (e.g., all Western Conference NBA teams). Every order goes into the same pool of liquidity no matter how it is phrased.

Price quotes are queries to the linear program of the form “at what price p will this order be accepted in full?” (I believe that bounds on the dual variables of the LP can be interpreted as bid and ask price quotes.) […]

Go reading all the post. There is a bunch of good comments&#8230- the best was submitted by Mike Giberson&#8230-

The concept of probabilistic prediction explained to the journalists and bloggers

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HubDub:

How to quote Hubdub

As well as being a fun place for people to make their own predictions, Hubdub also provides real value to anyone looking for numerical forecasts of the way a news story will turn out. At its core, Hubdub is a prediction market, which means the probabilities given for any of the hundreds of news stories we track are a combination of the thousands of pieces of information brought to bear on each question by our users. While Hubdub is still a young service building a track record of the accuracy of its forecasts, similar prediction markets (both play and real money) have found a very strong relationship between the forecast chances and reality. When quoting one of our forecasts, the correct terminology is to describe the percentage as a &#8220-percent chance&#8221- or &#8220-probability&#8221-, not what a percentage of our users think, as this is not what is measured. For example, &#8220-Hubdub is forecasting that Obama has a 67% chance of getting the nomination&#8221- is correct, whereas &#8220-67% of Hubdub users forecast that Obama will win the nomination&#8221- is incorrect. If in doubt please contact us.

Freakonomics did not quiz Bo Cowgills boss, Hal Varian, on prediction markets -triple alas.

No GravatarHal Varian, Google’s Chief Economist

Freakonomics interview. (I did ask, but they didn&#8217-t listen.)

Hal Varian&#8217-s post.

Read the previous blog posts by Chris F. Masse:

  • OutReach
  • If Warren Buffett can’t figure out derivatives, can anybody?
  • Many people twitter on prediction markets.
  • Folks, when you have something important to say, write up a full post, not a comment.
  • Prediction Market Journalism
  • TechCrunch is 221 times bigger than Midas Oracle.
  • Earthquake measuring 9.0 or more on Richter scale to occur anywhere on or before December 31, 2008