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PREDICTION MARKETS
AS A FORECASTING TOOL
Daniel E. O’Leary
ABSTRACT
Internal prediction markets draw on the wisdom of crowds, gathering
knowledge from a broad range of information sources and embedding that
knowledge in the stock price. This chapter examines the use of internal
prediction markets as a forecasting tool, including as a stand-alone, and
as a supplement to forecasting tools. In addition, this chapter examines
internal prediction market applications used in real-world settings and
issues associated with the accuracy of internal prediction markets.
INTRODUCTION
Internal prediction markets are different than so-called naturally occurring
markets, in that prediction markets are inter nal markets where generally
virtual dollars are used as a basis to try and put prices on particular events
or sets of events, for problems of direct relevance to a specific organization.
These markets are designed to gather information from a broad range of
users in the context of a market, where participants ‘‘bet’’ on the likelihood
of potential future events using prices to, ultimately predicting the
probability of the outcome of some event.
Advances in Business and Management Forecasting, Volume 8, 169–184
Copyright r 2011 by Emerald Group Publishing Limited
All rights of reproduction in any form reserved
ISSN: 1477-4070/doi:10.1108/S1477-4070(2011)0000008014
169
Prediction markets provide an information gathering and aggregation
mechanism across the population of traders to generate a price on some
stock, where that stock being traded typically is a prediction or forecast of
some even t. For example, a stock may be ‘‘the number of flaws in a product
will be less than x.’’ Researchers (e.g., Berg, Nelson, & Rietz, 2008; Wolfers &
Zitzewitz, 2004) have found that prediction markets provide accurate
forecasts, sometimes better than sophisticated statistical tools.
Historically, forecasting the future has been the domain of experts and
computer-based forecasting capabilities (e.g., Dalkey, 1969). However,
recently, firms have begun to gather opinions from a broader base of
employees using prediction markets, in order to use what has been referred to
as trying to gather the wisdom of crowds (e.g., Surowiecki, 2004). As an example,
recently, a news article (Havenstein, 2008 ), asked about Google, ‘‘What do
80,000 bets say about its future?’’ That article followed a Blog announce-
ment (Cowgill, 2009) that Google was making use of predict ion market s to
forecast launch dates, new office openings, and other issues of ‘‘strategic’’
importance to Google. Cowgill (2009) had found that the resul ting market
prices provided ‘‘y informative predictions in the sense that their predictive
power increased as time passed and uncertainty was resolved.’’ As with many
things in business, if Google is doing them there is interest in how other firms
might use them, who used them before Google and how prediction markets are
evolving once they have been placed in a corporate environment.
Although this chapter is primarily focused on business applications (e.g.,
Hemsoth, 2011; Kambit, 2011), a number of other settings also have found
that prediction markets can be useful. For example, Polgreen, Nelson,
Neumann, and Weinstein (2007) found that prediction markets have been
helpful at forecasting infec tious diseases. Others have had prediction markets
for events such as ‘‘Charlie Sheen to be arrested, in rehab or in hospital before
midnight ET 30 June, 2011’’ (which had a 5% ch ance on June 28, 2011) (http://
www.intrade.com/v 4/markets/contract/?contractId¼747478).
Theoretical Basis
There are different theoretical bases for using prediction market s to
forecast. Forsythe, Palfrey, and Plott (1982) suggest multiple theoretical
sources including rational expectations and efficient markets. Rational
expectations theory specifies a direct relationship between expectations and
actual market price behavior (e.g., Harrison & Kreps, 1978 ). Efficient
markets theory has been used as a basis to suggest the rationale for the
DANIEL E. O’LEARY170
accuracy of markets in forecasting events accurately (e.g., Berg, Forsythe,
Nelson, & Rietz, 2003). In a path-breaking analysis of efficient markets,
Fama (1970) indicates that ‘‘prices at any time fully reflect all available
information.’’ As a result, theory suggests that we can gather expectations
that full y reflect all available information in the form of market prices.
Purpose and Plan of this Chapter
The purpose of this chapter is to review the emerging phenomena of ‘‘corporate
prediction markets,’’ with a focus on their use as a tool for forecasting and pre-
dicting events. In so doing, I review some of the applications of corporate
prediction markets that are likely to be used in forecasting the future. Further, I
also review issues related to forecast accuracy and whether prediction markets
should be stand-alone or in conjunction with other forecasting approaches.
This first section provides an overview of the issues, a brief definition of
what prediction markets are, and summary of the purpose of this chapter.
The second section provides a number of example markets, and the third
section examines some example uses of internal prediction markets. The
fourth section investigates forecast accuracy and issues related to that
accuracy. The fifth section examines some of the tool capabilities of
prediction markets. The sixth section examines whether markets should be
used as a stand-alone tool or in conjunction with other tools. The seventh
section analyzes some of the concerns of using internal prediction markets
for forecasting. The eighth section examin es what are some of the
characteristics of prediction market problems. Finally, the ninth section
briefly summarizes the chapter and examines some extensions.
EXAMPLES OF PREDICTION MARKETS
There are a number of prediction markets, being used in generic and
corporate contexts. In what follows, Iowa Electronic Markets (IEM) and
Hollywood Stock Exchange (HSX) are examples of open markets, while
Google and Microsoft are examples of closed corporate markets. A brief list
of some other markets is summarized at the end of the chapter.
Iowa Electronic Markets
Perhaps the longest running prediction market is the IEM (http://
www.biz.uiowa.edu/iem/). IEM is an experimental market, operated by the
Prediction Markets as a Forecasting Tool 171
University of Iowa, developed for teaching and research purposes. Using
‘‘real’’ money virtually anyone can sign up and be a part of the market. Over
the years, the markets have been more accurate than polls at predicting the
results of elections.
Hollywood Stock Exchange
As another example, the HSX (http://www.hsx.com/) provides markets that
trade on movies, and their box office returns. Traders start off with H$
2,000,000, a virtual currency, and make trades. Ultimately, some of the
portfolios are worth hundreds of millions H$. Leading traders are
positioned on leader boards so that other traders are aware of what the
market capabilities are and to give ‘‘publicity’’ to leading traders.
Google
Google has experimented with prediction markets in order to study information
flows (e.g., Coles, Lakhani,& McAfee, 2007). Using ‘‘Gobbles’’ (virtual money),
employees bet on a number of different issues, including how much demand
there will be for a particular product or even how the company will do during a
future time period. A number of findings came out of this research. Google
found that betting behavior was related to physical proximity of those around
the employee. For example, Cowgill, Wolfers, and Zitzewitz (2008) found that
there are strong correlations among the predictions for those that physically sit
near each other.
Microsoft
Berg (2007) summarized some of the history of the use of predict ion markets
at Microsoft. Microsoft apparently began using prediction markets in 2003,
with the so-called ‘‘Information Forecasting Exchange.’’ Based on
information from Todd Proebsting’s Microsoft page (http://research.micro-
soft.com/en-us/um/ people/toddpro/), those initial efforts were at most a
part time project (‘‘nights and weekends’’). Starting in 2006, Microsoft
expanded those efforts with what was referred to as ‘‘PredictionPoint.’’ Over
the years, Microsoft has used a number of prediction markets to attack a
number of issues, such as ‘‘will the company meet their schedule?’’ or ‘‘how
many bugs will be in the software?’’
DANIEL E. O’LEARY172
USE OF CORPORATE PREDICTION MARKETS
There have been a number of uses of prediction markets in corporations,
including project management, product quality markets, and forecasting
events that effect the organization.
Project Management
Remidez and Joslin (2007) found internal markets can be used to facilitate
forecasting project management events. In particular, they found that a
prediction market correctly predicted 24 of 26 milestones. Further, direct
contact with Cisco has indicated that they were planning on using markets
to improve the flow of information about projects as part of a project
management office effort. In Berg’s (2007) analysis of the use of prediction
markets at Microsoft, out of projects undertaken at nine product groups,
project management schedules were examined at six of them and were the
most frequently mentioned use of market s.
Berg (2007) did note some limitations associated with using market s to
forecast project schedules, including the following. First, there was some
concern that the foreca sts could turn into self-fulfilling late forecasts. In
particular, there was concern that a person might drag a project into being
late in order to generate the right market outcome. Second, the results are
visible to all participants, potentially resulting in a difficult situation if
management does not have a plan in place to accommodate a forecasted late
project. Third, there was some concern about how to use the results. In
particular, if the project is on time, there is no interest in the market result,
but if it is late, then there may be no time to make an adjustment. Each of
these could be the source of further research to determine the extent to
which these concerns are likely to actualize themselves.
Product Quality
Another market that has found use in forecasting is the analysis of product
quality (http://blog.mercury-rac. com/category/conferences/). Product qual-
ity markets can include a range of sources of product quality, including
manufactured products and software. King (2006) noted that Microsoft ran
markets on the number of bugs that would be found in software over some
Prediction Markets as a Forecasting Tool 173
period. Cowgill et al. (2008) also indicated that product quality was forecast
at Google using prediction markets.
As an example, apparently, EA has used internal prediction markets
based on product quality for the games they developed. The markets at EA
apparently were well accep ted by executives and lower level employees.
Executives got the informat ion they needed and lower level employ ees had a
forum where they could provide and use the real informat ion. However,
middle managem ent responsible for the processes were the primary source
of resistance. EA used ‘‘metacritic’’ scores as the key performance indicators
to generate the stocks and markets (http://blog.mercury-rac.com/2007/10/
17/notes-from-the-london-prediction-market-conference-part-1/).
Impact of Events
Markets also can be used to foreca st the likelihood of key events to the
organization and the likely impact of events on corporations or other
entities. For example, an organization may be interested in the likelihood
that they will lose a particular client by some specific date. In this case, the
market would be generated around the probability of the loss or non-loss of
the specific client. In addition, they might be interested in the effect of losing
that particular client by that date, for example, extent of lost sales. In this
second case, the markets would be generated around the estimated loss of
sales, contingent on the lost client.
POTENTIAL ISSUES AFFECTING FORECASTING
ACCURACY USING PREDICTION MARKETS
There is a substantial literature in prediction markets (e.g., Luckner, 2008).
In particular, the literature has examined a number of factors related to
forecast accuracy, including the accuracy in the short run and the long run,
whether using virtual dollars or real money makes a difference, the impact
of trader knowledge and other factors. Our concern is primarily with the set
of factors that might impact the accuracy and quality of the market.
Time: Short Run versus Long -Run
There is evidence that prediction markets offer both short-run (e.g., 1-day
ahead) and long-run (weeks or months) accuracy. Berg et al. (2003)
DANIEL E. O’LEARY174
examined the short-run accuracy, and Berg et al. (2008) exami ned the long-
run accuracy. Berg et al. (2008) found that markets out performed polls of
presidential elections roughly 74% of the time, but 100% of the time in
those markets 100 days in advance. Market s appear to have a long-run
prediction capability.
Play Money versus Real Money
Servan-Schreiber, Wolfers, Pennock, and Galebach (2004) investigated the
relationship between using play money and real money. They found the
difference between the average forecasts errors was insignificant. Further,
Rosenbloom and Notz (2006) also found no statistical difference between
the two forecasts. Accordingly, this suggests that corporate prediction
markets can be effective in gathering forecast information using play or
virtual money.
Trader Knowl edge
An important issue is the impact of trader knowledge on prediction markets.
Berg (2007) has suggested that foreca sting accuracy of the markets varies in
proportion to the trader knowledge. Using established research, O’Leary
(1999) and Rodriguez & Watkins (2009) note that a critical point is wher e
traders have a probability greater than .5 of being right in order for the
collective to get to the right decision. Of course, this translates into a level of
expertise or knowledge of the trader with respect to the specific market.
Underpricing
At least two studies have found underpricing in prediction markets in middle
probability events. One study found minor underpricing of stocks in the
range of 20%–60% across a 0–100% scale ( http://www.consensuspoint.com/
prediction-markets-blog/ipredict_accuracy). Similarly, results from Google
(Coles et al., 2007) found underpricing of stocks in the range of 15–50%
across a 0–100% scale. As a result, there appears to be a bit of an inefficiency
or lack of accuracy in prediction markets over a particular range. Future
research will need to compare these results to other cases to determine the
extent to which these results are systematic or simply an issue in these cases.
Prediction Markets as a Forecasting Tool 175
Move the Market?
In some settings, a particular issue, stock or proposition, may be
particularly important to a specific participant. In that case they may try
to ‘‘move the market’’ with their betting activity as a means of influencing
the price. For example, in one case a key backer of a political candidate
wanted the prediction market to generate a particular outcome, likely under
the impression that the outcome would feed back into the real-world
outcome (Rhode & Strumpf, 2005). Accordingly, the backer tried to invest
enough money to move the market. In an open market the scheme did not
work, most investors were rightly convinced that the opposition would more
likely win. However, in a closed corporate market with limited liquidity and
traders, it is questionable as to whether or not an investor can move the
market. If a participant is able to move the market, then clearly the accuracy
of the market may be compromised.
TOOL CAPABILITIES OF USING MARKETS
FOR FORECASTS
Hewlett-Packard found that markets were more accurate than corporate
forecasting tools 75% of the time (Yeh 2008 and others). But this finding
begs questions such as, ‘‘why would the markets be more accurate than
forecasting tools?’’ It is likely that the tool characteristics or cap abilities of
markets, such as broad access to other types of information, access to
real-time information, trader anonymity, truth telling, and other issues
provide prediction markets with the ability to generate highly accurate
forecasts.
Broad Information Access
Perhaps the primary benefit de riving from the use of markets for internal
purposes compared to other forecasting tools is that firms have access to
information sources that they may not have had access prior to implementing
the market. Since the markets involve a wide range of participants,
information and knowledge is gathered from a broad range of sources,
potentially some that are not part of the normal reporting process.
As a result, prediction markets potentially open new communication
channels, as new traders join. In addition, they create a new medium for
DANIEL E. O’LEARY176
interacting with those information sources. Since involve a broader base of
users, issues such as information asymmetry could be mitigated to a certain
extent.
Continuous Feedback and Real-Time Information
Markets can provide continuous feedback about future events. For
example, as noted in one market used for forecasting. ‘‘If I am leading a
project and the stock is, will this thing launch on time, if the stock price goes
down I instantly know something has happened.’’ (http://www.dni.gov/nic/
NIC_specialproducts.html) As a result, market forecasts gather timely
information on a continuing basis, as long as there is new information and
as long as the market is continuing. Alternatively, a forecast using a
sophisticated approach is likely to have limited data and operate over a
limited time horizon.
Anonymity
Most markets are anonymous. As a result, information can be embedded in
the price from a range of sources, including sources where the informat ion is
supposed to remain secret or at least not directly disclosed. Thus, potentially
information not available to those making the forecast, ultimately can be
embedded in the price or probability of the event. Further, those providing
an official forecast are not anonymous and often the forecast must reflect
political realities that limit its effectiveness.
Truth Telling
Because of anonymity, participants can express the truth in their
market operations regarding the prediction events. Further, because of
the incentives in the market, participants have incentive to trade on what
they know as the truth. Accordingly, as noted by Abramowicz and
Henderson (2007) ‘‘Prediction markets can increase the flow of informa-
tion, encourage truth telling by internal and external firm monitors, and
create incentives for agents to act in the interest of their principals.’’ Thus,
the information in the markets may be better than that used in other
tools.
Prediction Markets as a Forecasting Tool 177
Additional Information
Since internal prediction markets gather knowledge distributed throughout
the firm, they function in part as ‘‘suggestion boxes.’’ As a result, markets
potential can gather information that might not normally be gathered as
part of the normal management hierarchy, reaching out to those not in the
management hierarchy, leading to information potentially beyond those
responsible for forecasting, mitigating asymmetr ies of information.
Involvement
Further, since a broad range of users provide information, potentially there
is a higher level of involvement by those in the organization. In many cases
‘‘involvement’’ can lead to improved personal and corporate performance
(e.g., Woolridge & Floyd, 1990 ). The extent that market participants are
‘‘involved’’ can lead to the generation and use of broader bases of
information than with other types of forecasting tools.
USE MARKETS AS A STAND-ALONE OR IN
CONJUNCTION WITH OTHER FORECASTING
TOOLS
If markets have such excellent accuracy, should we see them as stand-alone
tools or should they be used in conjunction with other forecasting tools?
Stand-Alone or in Conjunction
If there are sophisticated forecasting tools, then any trader, with access to
that infor mation, will be able to embed the forecasting information in the
market. Further, those traders will bring the official forecasting information
in with any additional information that the trader feels is important.
However, if there is no forecasting tool, then no additional information
other than the expectations and other knowledge of the traders will be
embedded in the price. Accordingly, from an information perspective, more
and different information is embedded in the market if there are traders who
also have access to forecast information. As a result, we generally would
expect better performance when markets are run in conjunction with other
sophisticated forecasting tools.
DANIEL E. O’LEARY178
Time Ahea d
As noted in a previous section, prediction markets appear to have a long-run
forecasting capability. Accordingly, it would appear that using both markets
and other forecasting tools would provide the organization a more complete
forecast.
SOME CONCERNS OF USING MARKETS
FOR FORECASTING
Although prediction markets appear to provide an effective forecasting tool,
there are some potential limitations, some of which we list here.
Trade-Off – Cost Benefit of Adding a Market
Although this chapter has argued that markets can provide important and
accurate information for forecasting purposes, prediction markets can
generate additional costs. For example, not only is there the cost of the
market, the software, etc. but there also is the time and effort incurred by
employees following and participating in the markets. Clearly, time spent on
markets is time that could be spent in other areas. Accordingly, prediction
markets must provide additional information or benefits to be cost-effective.
Who Are the Participants in Forecasting Events
One generalization is that participants are likely to be more junior mem-
bers of the firm (http://www.consensuspoint.com/prediction-markets-blog/
prediction-markets-focus-of-mba-thesis-research). If that is the case, then
we are likely to see market prices move in response to the information
that junior members of the firm have access. Alternatively, junior personnel
may be more insulated from political realities of more senior management
and thus be more honest. Thus, the organization must ask its elf, whose
information do they want to gather and disseminate.
Returns to the Participants
Although, since some kind of virtual dollar typically is used, the payoff to
participants often is not a direct one to one for the investment, instead it
Prediction Markets as a Forecasting Tool 179
may result in honors, publicity as a market leader, t-shirts, and the
opportunity at lump sum payoffs for being one of the more successful
participants. Accordingly, one potential problem is whether or not su ch
returns are of sufficient interest to draw participants into the markets.
Further, even if they are drawn into the markets, will they take the market
serious enough to make a difference?
Impact on Effort
What would be the impact of markets on ‘‘effort?’’ If the outcomes are not
positive, there can be a range of alternative responses. Suppose the
prevailing price on whether a project will finish on time is low compared to
the price that it will finish on time. Participants affected by the event may
work harder to mitigate what they think the market may see. On the other
hand, they may think that it is hopeless and give up, particularly, if it is clear
to the participant s the ultimate demise of the operation is imminent.
Alternatively, if the market forecast is positive, then the effort might be
reduced as participants feel that the event is destined to happen. As a result,
we may see the generation of self-fulfilling prophecies. Accordingly, Berg
(2007) and others have suggested that for prediction markets, ‘‘betting on
failure leads to failure’’ and ‘‘predicting success as an attempt to create a
good appearance.’’
Real Gambling (on the Side)?
Although an issue that might not directly impact the firms involved,
conceivably, prediction markets could lead to additional off-line gambling
on the market or other related or unrelated issues. Further, some
organizations and some poten tial participants may think that gambling is
not appropriate for a workpl ace or any setting.
WHAT IS A GOOD PREDICTION MARKETS
FORECASTING PROBLEM?
There are a number of settings where markets work, but we examine four
where markets are more likely to be effective.
DANIEL E. O’LEARY180
Sparse Information
Deloitte (2010) suggests that ‘‘Prediction markets are especially suited to
situations where there is sparse data otherwise available that may be used to
define a forecasting model.’’ In this setting, information is dispersed across
the firm. If there is sparse information, a market can act to pull the
information together and provide an aggregation function.
Lack of Open and Truthful Information
If the information flows to management are not open, if people are not
telling management the ‘‘true’’ story , then markets may remove those
information asymmetries as seen in a situation where Microsoft used a
market to predict when a product would ship (http://www.midasoracle.org/
2007/01/23/case-micr osofts-internal-prediction-markets/). If a story is not
true, then those traders who recognize the lack of truth can trade on that
information and make money from the market.
Lack of Organizational Responsibility
Apparently, EA produced around 120 games a year; however, there was limited
accountability for the forecasts (http://blog.mercury-rac.com/2007/10/17/
notes-from-the-london-prediction-market-conference-part-1/). As a result,
the quality of the forecasts was limited. A lack of organizational responsibility
can result in a lack of communication of proper information. However, as seen
at EA, a prediction market can facilitate and broker that information.
Information Asymmetries
If there are information asymmetries, or information gets stuck at different
points in an organization, then prediction markets may work to mitigate those
asymmetries. Traders can trade on information asymmetries with the result being
the integration of that information in the market price and a loss of asymmetries.
SUMMARY AND EXTENSIONS
This chapter has examined how firms have begun to use internal prediction
markets to forecast and predict the future. The chapter examined some of
Prediction Markets as a Forecasting Tool 181
the kinds of problems that markets are being used to examine. Further, the
chapter investigated the potential accuracy of markets as a forecasting tool.
In addition, the chapter analyzed some of the unique capabilities of markets
as a forecasting tool, and the extent to which markets are a stand-alone-type
tool. In addition, this chapter reviewed some of the concerns of using
markets as a forecasting tool. Finally, the chapter analyzed some of the
characteristics of problems for which markets provide an important tool.
Extensions
There are a number of extensions to this chapter. First, we have focused on
two types of problems: project management and product quality. Future
research could focus on other types of problems, for example, financial.
Second, this chapter has taken markets on their own, and not integrated
with other types of technologies, for example, Wikis. Future research could
analyze integration issues between the different technologies. Third, this
chapter has recognized some of the limitations in prediction markets;
however, addition research could focus on biases that might result in
limitations of markets, such as the underpricing of the 15–50% mentioned
earlier in the chapter. Fourth, prediction markets can be seen as knowledge
management efforts. Accordingly, future research could focus on structur-
ing markets as vehicle to evolve tacit knowledge into explicit knowledge.
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B., & Floyd, S. (1990). The strategy process, middle management involvement and
organizational performance. Strategic Management Journal, 11(3), 231–241.
Prediction Markets as a Forecasting Tool 183
SELECTED WEB PAGES
Ask Markets – http://www.askmarkets.com/ (Build your own market)
‘‘Bet 2 Give’’ – https://bet2give.com/b2g/index.html (Real money, winnings go to non-profits
organizations
‘‘Bet Fair’’ – http://corporate.betfair.com/about-us.aspx (Designed to eliminate the need for a
book maker)
Consensus Point – http://www.consensuspoint.com/ (Software company)
Crowd Cast – http://www.crowdcast.com/resources/ (Software company)
Hollywood Stock Exchange – http://www.hsx.com/ (Buy and trade in movies)
Foresight Exchange – http://www.ideosphere.com/ (Play money market)
Inkling Software – http://inklingmarkets.com/ (Software company)
News Futures – http://us.newsfutures.com/home/home.html (Market software and consulting)
Prediction MarketsIndustry – http://groups.google.com/group/prediction-markets-open-discussion
DANIEL E. O’LEARY184
