Jump to the
Navigation Bar

Can Markets Predict the Future?

"By election day, the markets with an average absolute error of
around 1.5 percentage points, were considerably more accurate than the
Gallup poll projections, which erred by 2.1 percent."

Prediction markets -- also known as information markets or events
futures -- first drew widespread attention in July 2003 when it was
revealed that the Pentagon's Defense Advanced Research Projects Agency
(DARPA) was establishing a Policy Analysis Market to allow trading in
various forms of geopolitical risk, including economic and military
scenarios. The objective was to discover whether trading in such contracts
could help predict future events. Bowing to a storm of criticism that it
was proposing "terrorism futures," DARPA dropped the program. But other
prediction markets, dealing with everything from sports and entertainment
to elections and finances, have emerged and gained growing interest and
participation.

In Prediction Markets (NBER Working Paper No. 10504), authors Justin
Wolfers and Eric Zitzewitz describe the types of contracts that might be
traded in prediction markets and then survey several applications, with
special attention to market design issues. Finally, they assess the
predictive value of such markets.

Wolfers and Zitzewitz begin by noting that much of the enthusiasm for
prediction markets derives from the efficient markets hypothesis. In a
truly efficient prediction market, the market price will be the best
predictor of the event, and no combination of polls or other information
can be used to improve on the market-generated forecasts. Wolfers and
Zitzewitz do not insist that prediction markets are literally perfectly
(or fully) efficient; however, they acknowledge that a number of successes
in these markets, both within firms and with regard to public events such
as presidential elections, have generated substantial interest among both
political and financial economists.

In a prediction market, the researchers note, payoffs are tied to
unknown future events, and how the design of how the payoff is linked to
those events can elicit the market's expectations of many things. A in a
"winner-takes-all" contract, for example, the contract costs a specific
amount and pays off a specific amount, and only pays if a specific event
occurs, such as a particular candidate winning an election. The price on a
winner-takes-all market represents the market's expectation of the
probability that an event will occur. By contrast, for in an "index"
contract, the amount that the contract pays varies in a continuous way
based on a number that rises or falls, like the percentage of the vote
received by the candidate. Finally, in "spread" betting, traders bid on
the cutoff that determines whether an event occurs, like whether a
candidate wins more than a certain percentage of the popular vote.

The various types of contracts may reveal the market's expectation of a
specific parameter: a probability, a mean, or median, respectively. But
prediction markets also can be used to evaluate uncertainty about these
expectations, for example a family of winner-takes-all contracts that pays
off only if the candidate earns 48 percent of the vote, 49 percent, 50
percent and so on. This family of winner-takes-all contracts then will
reveal almost the entire probability distribution of the market's
expectations. A family of spread-betting contracts can yield similar
insights.

With these factors in mind, Wolfers and Zitzewitz examine the data
compiled from analyses of the University of Iowa's Iowa Electronic Market,
which has offered trade on presidential election contracts since 1988.
Charting the pricebids for the past four presidential elections, the data
show that as election day drew nearer, the prediction markets' projected
candidate vote shares grows more accurate. Prediction markets also beat
appeared better calibrated than independent analysts on the probability of
the ouster of Saddam Hussein. The Hollywood Stock Exchange likewise has
proved highly accurate in predicting opening weekend box office success
and Oscar winners.

Even some prediction markets with very small participation have shown
striking results. An internal market at Hewlett-Packard produced more
accurate forecasts of printer sales than did the firm's internal
processes, and at Siemens an internal market predicted the firm would
definitely fail to deliver a software project on time, even as traditional
planning tools said the deadline could be met. In each firm, the traders
numbered only between 20 and 60 employees.

Wolfers and Zitzewitz maintain that the success of prediction markets,
like all markets, depends on their design and implementation. Some key
design issues include: how buyers are matched to selltraders; the
specification of the contract; whether real money is used (some prediction
markets operate for entertainment purposes and use make-believe currency);
and the kind of information available to provide a basis for trading. When
such factors are weighed judiciously, Prediction markets are better at
pricing some events than others. The markets, like many individuals, are
not always well calibrated on small probability events. In addition,
markets on complex events, or events where there is likely to be inside
information, often fall to attract sufficient liquidity.

Wolfers and Zitzewitz conclude with cautious optimism. They believe
that prediction markets are extremely useful for estimating the market's
expectation of certain events. Simple market designs can elicit expected
means or probabilities, while more complex markets can elicit variances,
and contingency markets can be used to elicit the markets' expectations of
covariances and correlations.

Prediction markets have their limitation, the researchers caution, but
they may be useful as a supplement to more traditional means of
prediction, such as opinion surveys, expert panels, consultants, and
committees.

-- Matt Nesvisky

Publications
|

Programs |

Data |

About |

People

Publications:

Main Publications
Page

New This
Week
Working Papers
Books

Books in
Progress
Digest

Reporter

Bulletin on Aging & Health
2002 Japan Conference
Historical Bulletins
Free Subscriptions
Paid Subscriptions

Programs:

Program descriptions and
members

Working
Group Descriptions and Papers

Selected Projects:
Conference on Research in Income and
Wealth
Sloan Science and Engineering
Workforce Project
Boston Census
Research Data Center

Call for Papers

Data:

NBER
Collection
Business Cycle
Dates
Latest Business Cycle
Memo
New Economic
Releases
Selected Sources
Economic Organizations
US Government Agencies
Other Data Collections

Economic Report of the
President
Economic
Indicators
Congressional Budget Office

About

What we are
Contact us
Non-data Links
Search

Site
Map
Employment

Fellowships
Early History
(.pdf)

People:

Staff
Researchers
Board
Contact Us

Search via Google: