JOURNAL OF GLOBAL TRADE, ETHICS AND LAW

Volume 3 Issue 2, 2025

PREDICTION MARKETS

Emilio Barone1* and Federico Carli2

1 Department of Economics and Finance, LUISS-Guido Carli University, Rome, Italy, ebarone@luiss.it.

2 Department of Economics, Business, Law and Political Sciences, Guglielmo Marconi University, Rome, Italy,

federico.carli@rcs.it.

*Corresponding Author

Abstract. This paper provides an English version of a study originally circulated

as a working paper in November 2005 on prediction markets and subsequently

published in Italian in Mondo Bancario (January–February 2006). The paper

reviews the structure, functioning, and empirical performance of prediction

markets, with particular emphasis on the Iowa Electronic Markets and other

platforms active in the early 2000s. The analysis covers electoral markets, cur-

rent events, entertainment markets, and macroeconomic derivatives, and

discusses issues related to information aggregation, forecasting accuracy, and

regulation.

Keywords: Prediction Markets, Information Markets, Event Markets.

JEL: D8, G14.

Journal of Global Trade, Ethics and Law

ISSN 2977-0025 (Online). Published under Creative Common (CC) BY 4.0 license.

https://doi.org/10.70150/xrt21s70

Durham &

Thunmann

Emilio Barone & Federico Carli, 2025

“Prediction is very difficult, especially if it’s about the future”

- Nils Bohr

1. Introduction

“Prediction is very difficult, especially if it’s about the future.” These words are

commonly attributed to Nils Bohr (1885-1962), the Danish physicist who, for his

contributions to quantum theory, was awarded the Nobel Prize in Physics in 1922, one

year after Einstein.

In the difficult task of forecasting the future - or, more precisely, of assigning

probabilities to future events - prediction markets, also known as information markets or

event markets, have increasingly attracted attention. Their archetype is the Iowa

Electronic Market (IEM), an experimental market cited by Vernon Smith in his 2002

Nobel Prize Lecture:

“What evidence do we have that the laboratory efficiency properties of continuous

double auction trading apply also in the field? One of the best sources of evidence, I

believe, is found in the Iowa Electronic Market (IEM) used widely around the world.

(Forsythe, et al., 1991, 1999) These markets are used to study the efficacy of futures

markets in aggregating widely dispersed information on the outcomes of political

elections, or any well defined extra-laboratory event, such as a change in the discount

rate by the FED. The ‘laboratory’ is the internet. The ‘subjects’ are all who log on and

buy an initial portfolio of claims on the final event outcomes; they consist of whom ever

logs in, and are not any kind of representative or ‘scientific’ sample as in the polls with

which they are paired. The institution is the open book double auction.

In the IEM, traders make a market in shares representing pari-mutuel claims on the

popular vote (or winner-take-all) outcome of an election, referendum, etc. For example

the first IEM was on the 1988 Presidential Election. Each person wanting to trade shares

deposits a minimum sum, $35, with the IEM and receives a trading account containing

$10 cash for buying additional shares, and ten elemental portfolios at $2.50 each,

consisting of one share of each of the candidates - Bush, Dukakis, Jackson, and ‘rest-of-

field.’ Trading occurs continuously in an open-book bid-ask market for several months,

and everyone knows that the market will be called (trading suspended) in November on

election day, when the dividend paid on each share is equal to the candidate’s fraction of

the popular vote times $2.50. Hence if the final two candidates and all others receive

popular vote shares (53.2%, 45.4%, 1.4%), these proportions (times $2.50) represent the

payoff to a trader for each share held. Consequently, at any time t, normalizing on $1,

the price of a share (÷$2.50), reflects the market expectation of that candidate’s share of

the total vote. A price, $0.43 means the market predicts that the candidate will poll 43%

Smith (2002).

Emilio Barone & Federico Carli, 2025

of the vote. Other forms of contract that can be traded in some IEMs include winner-take-

all, or number of seats in the House, and so on.

The IEM data set includes 49 markets, 41 worldwide elections and 13 countries.

Several results stand out: the closing market prices, produced by a non-representative

sample of traders, show lower average absolute forecasting error (1.5%) than the

representative exit poll samples (1.9%); in the subset of 16 national elections, the market

outperforms the polls in 9 of 15 cases; in the course of several months preceding the

election outcome, the market predictions are consistently much less volatile than the

polls; generally, larger and more active markets predict better than smaller, thinner

markets; surveys of the market traders show that their share holdings are biased in favor

of the candidates they themselves prefer.

In view of this last result why do markets outperform the polls? Forsythe, et al. (1991)

argue that it’s their marginal trader hypothesis. Those who are active in price ‘setting,’

that is, in entering limit bids or asks, are found to be less subject to this bias, than those

traders accepting (selling and buying ‘at market’) the limit bids and asks. Polls record

unmotivated, representative, average opinion. Markets record motivated marginal

opinion that cannot be described as ‘representative.’

For decades, researchers in artificial intelligence have attempted to construct intelligent

systems by writing software that replicates human cognitive processes. More recently -

as the IEM clearly demonstrates - researchers have sought to solve complex problems by

building networks of autonomous agents that interact with one another. The key idea

behind intelligent system design increasingly appears to be that of leveraging individual

agents - connected through information systems - and exploiting their cognitive

capabilities in innovative ways.

Issues of public interest can therefore be addressed through mechanisms analogous to

those used in markets for financial securities. The proposed solution is to create markets

in which both correct and incorrect answers carry economic consequences. The

underlying hypothesis is that such markets will amplify the influence of those who are in

the best position to know the correct answer. Moreover, the very existence of these

markets may induce participants to seek out information that improves the quality of their

decisions.

The reason this approach should work is that less informed participants tend to lose

money and are eventually driven out of the market, while better informed participants

tend to profit and increasingly guide the market, thereby determining prices.

2. Review of Markets and Contract Design

The most widely known prediction market is the Iowa Electronic Market (IEM),

established in 1988. The first contract traded on the IEM paid 2.5 cents for each

percentage point of the popular vote received by each candidate in U.S. presidential

elections (Bush, Dukakis, and others).

Emilio Barone & Federico Carli, 2025

Universities in other countries have also begun to operate their own event markets.

Examples include the Austrian Electronic Market at the Vienna University of Technology

and the Election Stock Market at the University of British Columbia.

There are also prediction markets operated by firms active in the sports betting

industry.

Notable examples include Trade-Sports, Betfair, and the World Sports

Exchange.

According to information published on the Trade-Sports website (August 2005), the

platform - managed by an Irish company whose financial statements were audited by

Deloitte - reported cumulative trading volume of approximately $1 billion, around 70

million contracts traded, and more than 30,000 registered users over roughly three years

of operation.

In some cases (e.g., Newsfutures or the Hollywood Stock Exchange), trading takes

place using virtual currency. These exchanges define the contracts, and participants either

submit their own offers or accept those posted by others. On the Newsfutures platform -

founded in 2000 and active until 2004 - participants could speculate on a wide range of

current events, including politics, sports, cinema, economics, and technology. As an

incentive, prizes were awarded to the top performing participants. Participants in the

Hollywood Stock Exchange use virtual currency to speculate on questions related to the

film industry, such as a movie’s box-office revenue, the number of spectators during its

opening weekend, or the allocation of Academy Awards. Insider trading is entirely legal

in this market and is, in fact, explicitly encouraged. Film studios often rely on the

forecasts generated by these markets.

The Foresight Exchange also operates using virtual currency. The range of contracts

offered is broad, spanning traditional financial contracts as well as contracts related to

disasters, news events, politics, and scientific developments. For example, at the end of

August 2005, the contract “NASDAQ drops below 1000 by 2008” traded at 24, “Whites

US Minority by 2060” at 75, “Cold Fusion by 2015” at 19, “Human Organ Farms by

2015” at 26, and “Moonbase by 2025” at 35.

More recently (in 2002), Goldman Sachs and Deutsche Bank launched a new market -

Economic Derivatives - in which the events to be predicted concern the release of

macroeconomic data, such as employment, retail sales, gross domestic product, consumer

confidence indices, and inflation.

In June 2005, Goldman Sachs reached an agreement with the Chicago Mercantile

Exchange to create the CME Auction Markets, where “a series of innovative event-driven

economic derivatives” would be traded. The auctions were to be conducted using

proprietary software developed by Longitude. The agreement specified that trading would

initially take place via the web starting in September 2005, before migrating to the Globex

platform in January 2006.

This CME initiative illustrates how the phenomenon of prediction markets - initially

confined to over-the-counter markets - began, at that time, to extend into organized

exchange-traded markets.

See Brüggelambert (1988), Ortner (1996), Beckmann, & Werding (1996), Jacobsen et al. (2000).

See Wolfers & Leigh (2001).

For a survey, see Goldfinger (2004).

Emilio Barone & Federico Carli, 2025

A list of existing prediction markets is provided in Error! Reference source not

found., while a sample of contracts traded on the Foresight Exchange is reported in

Error! Reference source not found..

3. Iowa Electronic Markets

The Iowa Electronic Markets (IEM) are small-scale markets - managed by the College

of Business at the University of Iowa - in which contracts are traded using real money.

The most prominent of these markets are the Iowa Political Markets.

Contracts traded in

these markets are designed so that prices can be used to generate forecasts of electoral

outcomes.

The IEM operate continuously, 24 hours a day, and employ a continuous double-

auction trading mechanism. Traders invest their own funds, execute trades autonomously,

and gather information independently. As such, the IEM occupy a niche between highly

stylized and tightly controlled “laboratory” markets and fully fledged “real” markets.

Owing to their design, the IEM provide researchers with data that are otherwise

unavailable.

In the IEM, data collection and aggregation rely on a mechanism that differs

substantially from that used in opinion polls. Polls are based on representative samples of

potential voters, the reliability of survey responses, and statistical inference. In contrast,

IEM traders face explicit monetary incentives and do not constitute a representative

sample of the electorate. The vast majority of participants are young (with an average age

close to 30), predominantly male, and well educated. Traders are not required to satisfy

the eligibility criteria for voting in elections.

3.1. Market Mechanism

Each market is linked to a specific future event - such as a presidential election - and

allows the trading of contracts whose final value (payoff) is determined by the outcome

of that event. Contracts enter circulation when traders purchase a bundle of contracts from

the exchange, known as unit portfolios. Conversely, contracts are withdrawn from

circulation when unit portfolios are sold back to the exchange.

The maximum investment allowed per trader is $500. Average investment is less than $50. The number

of traders actively participating in a given market typically ranges from a dozen to more than 500. In the

market on the 1992 U.S. presidential elections, 78,007 contracts were traded, for a total notional value of

$21,445.

The Iowa Electronic Markets are composed of several segments: electoral markets based on vote shares

(vote-share markets), markets based on the number of seats (seat-share markets), and markets based on the

election winner (winner-takes-all markets), as well as markets based on other political outcomes, economic

indicators, corporate earnings, and rates of return of selected firms.

The information is drawn from Berg, Forsythe, Nelson, and Rietz (1998).

Emilio Barone & Federico Carli, 2025

Unit portfolios consist of one unit of each contract available in the market. They are

purchased from and sold to the exchange at a fixed price equal to the final value of the

entire portfolio. The rate of return associated with a unit portfolio is equal to the risk-free

interest rate, which is zero in the case of the IEM.

The use of unit portfolios ensures that the market operates as a zero-sum game and that

the supply of contracts is determined endogenously by the net number of unit portfolios

purchased by traders. Unit portfolios serve solely to introduce contracts into circulation.

Transactions among traders take place at prices determined by participants for each

individual contract.

Traders may submit market orders, which require immediate execution at the prevailing

market price, or limit orders, which specify quantities to buy or sell at given bid or ask

prices within a specified time horizon. Limit orders are ranked according to price and the

time at which they are submitted. They may be canceled at any time before being executed

(hit) or before expiration.

Figure 1: IEM: Trading Screen.

Source: Berg et al. (2000).

Note: The trading screen is divided into three sections. The upper section reports - for each contract (e.g., DemVS,

ReformVS, and RepVS) - the best bid and ask quotes, the price of the most recent transaction (last trade price), the trader’s

current holdings (quantity held), and the trader’s outstanding buy (YourBids) and sell (YourAsks) orders. The middle section

contains a menu that allows traders to enter quantities (Qty) for market orders or limit orders, specifying, in the latter case,

both the price (Price) and the expiration date (Expire). The lower section provides a menu enabling traders to access other

activities.

Emilio Barone & Federico Carli, 2025

The information set made available by the exchange to traders consists of the most

recent transaction price (last trade price) and the best bid and ask quotes. The screen that

appears to traders is shown in Error! Reference source not found..

Traders do not observe the quantities available at the best bid and ask prices, nor do

they have access to the remainder of the order book, which comprises all outstanding

limit orders. With respect to historical data, traders may access daily information on

trading volume and contract value, as well as daily minimum, maximum, average, and

closing prices.

3.2. Types of Markets

Winner-Takes-All Markets

Contracts traded in winner-takes-all markets pay $1 if the associated event occurs. For

example, if an election is won by a given candidate, the contract linked to that candidate

yields a payoff of $1.

Vote-Share Markets

In vote-share markets, the final value of each contract is determined by the share of

votes received by the corresponding candidate. The final value of a contract equals the

product of $1 and the candidate’s vote share. To ensure that the sum of vote shares equals

unity, one of two methods is employed:

1. A contract associated with the residual vote share (rest-of-the-field

contract) is traded; or

2. Vote shares are computed as fractions of a restricted total (for example,

the Democratic vote share is calculated as the number of votes received by

the Democratic candidate divided by the total votes received by

Democratic and Republican candidates).

The method adopted in each case is described in the prospectus prepared for the specific

market.

Although markets operate continuously, information is aggregated and reported over daily 24-hour

periods.

Emilio Barone & Federico Carli, 2025

Figure 2: IEM Election Futures.

Source: Wolfers, & Zitzewitz (2004).

Seat-Share Markets

In seat-share markets, the final value of contracts is determined by the number of

legislative seats obtained by each party in an election. The payoff of each contract equals

the product of $1 and the party’s share of seats.

3.3. Efficiency

A recurring question in the analysis of these new markets concerns their efficiency. Are

prediction markets truly efficient? Do they effectively aggregate information? What is

the informational content of market prices? Are the prices generated in these markets

genuinely useful for forecasting purposes? According to several empirical studies, the

answer appears to be affirmative.

Berg et al. (2000) argue that prices observed in the Iowa Electronic Market yield highly

accurate forecasts, outperforming those derived from opinion polls.

Error! Reference source not found. reports data for four U.S. presidential elections

(1988-2000). The horizontal axis shows the number of days remaining until election day,

See also Granberg & Brent (1983), Oliven & Rietz (1995), Bondarenko & Bossaerts (1999), Slemrod

& Greimel (1999).

Emilio Barone & Federico Carli, 2025

while the vertical axis reports the mean absolute forecasting error computed on the basis

of IEM prices. As the figure shows, the accuracy of market-based forecasts improves as

election day approaches, in parallel with the arrival of new information. Error!

Reference source not found. also indicates that, during the final week before the

election, markets predicted the vote shares of Democratic and Republican candidates with

an average absolute error of approximately 1.5 percentage points. By comparison, the

forecasting error of the final Gallup polls for the same four elections was 2.1 percentage

points (Error! Reference source not found.).

Table 1: Final Gallup Polls: Forecasting Errors (1988-2000).

3.3.1. Bush vs. Kerry

The outcome of the most recent U.S. presidential election was accurately predicted by

the IEM. In the 2004 U.S. Presidential Vote Share Market (Pres04_VS), two contracts

were traded: BU|KERR, which paid $1 if Bush won, and KERR, which paid $1 if Kerry

won. The election took place on November 2, 2004. On the day preceding the election,

the quoted prices were as follows:

Year

Candidates

Final

Poll

(%)

Election

Outcome

(%)

Forecasting

Error

(%)

Average

Forecasting Error

(%)

2000

Bush

48.0

47.9

0.1

Gore

46.0

48.4

-2.4

Nader

4.0

2.7

1.3

1996

Clinton

52.0

50.1

1.9

Dole

41.0

41.4

-0.4

Perot

7.0

8.5

-1.5

1.3

1992

Clinton

49.0

43.3

5.7

Bush

37.0

37.7

-0.7

Perot

14.0

19.0

-5.0

3.8

1988

Bush

56.0

53.9

2.1

Dukakis

44.0

46.1

-2.1

2.1

Total

2.1

Symbol

Units

Volume ($)

Low

High

Average

Last

BU|KERR

215

109,249

0.480

0.519

0.508

0.504

KERR

337

337,490

0.475

0.510

0.492

0.495

Emilio Barone & Federico Carli, 2025

It should be noted that the sum of the average prices of the two contracts (column

“Average”) equals 1 (= 0.508 + 0.492). This is not the case for closing prices (column

“Last”), which may reflect slight timing mismatches. A graphical representation of the

“Last” prices is provided in Figure 1.

Figure 1: IEM Election Futures: Bush vs. Kerry.

Source: Wolfers, & Zitzewitz (2004).

In the second market (Pres04_WTA), four contracts were traded: DEM04_G52 and

DEM04_L52 (Kerry receiving more or less than 52% of the vote), and REP04_G52 and

REP04_L52 (Bush receiving more or less than 52% of the vote). The quoted prices were

as follows:

By summing the “Average” prices of the first two contracts, one obtains the market-

implied probability of a Kerry victory: 49.5% (= 14.7% + 34.8%). Summing the

“Average” prices of the last two contracts yields the probability of a Bush victory: 50.3%

(= 15.8% + 34.3%). The resulting values do not sum to one and are marginally different

from those obtained previously. This inconsistency would likely not have arisen had mid-

market prices - that is, the averages of bid and ask quotes - been used; however, historical

series of such prices are not available on the IEM website.

Symbol

Units

Volume ($)

Low

High

Average

Last

DEM04_G52

9,227

1,358,635

0.130

0.164

0.147

0.155

DEM04_L52

10,365

3,606,583

0.300

0.474

0.348

0.330

REP04_G52

13,069

2,067,415

0.070

0.239

0.158

0.150

REP04_L52

11,609

3,978,678

0.309

0.393

0.343

0.362

Emilio Barone & Federico Carli, 2025

3.3.2. Vote-Share and Seat-Share Markets

Figure 2, electoral forecasts based on 237 contracts (49 markets across 13 countries)

are compared with actual election outcomes. In the figure, the horizontal axis measures

actual outcomes, while the vertical axis reports predicted outcomes. If forecasts were

perfectly accurate, all points would lie on the 45-degree line. Forecasts that overestimate

outcomes appear above the line, while underestimates appear below it.

Figure 2: IEM: Comparison Between Forecasts and Actual Outcomes.

Source: Berg et al. (2000).

Note: VOTE-SHARE MARKETS: Austria (Federal Parliamentary Elections ’95; Styria ’95; Vienna ’95; European

Parliamentary Elections ’96), Canada (Parliamentary Elections ’93, ’96), Korea (Presidential Election ’92), Denmark

(Parliamentary Election ’91), Finland (Presidential Elections × 2 markets), France (Presidential Election ’95), Germany

(Parliamentary Elections ’90 × 3 markets - Bonn, Frankfurt, and Leipzig; Federal Elections ’91, ’94, and ’98; State Election

’98), Norway (Parliamentary Election ’95), Netherlands (Parliamentary Elections ’91, ’94 - Second Chamber; European

Parliamentary Election ’94; Municipal Council Election ’94), Sweden (European Parliamentary Election ’94), Turkey

(Parliamentary Election ’91), United States - Gubernatorial Elections (NY ’94, TX ’94), United States - House of

Representatives (UT ’94), United States - Presidential Elections (’88, ’92, ’96 × 2 markets, ’00), United States - Presidential

Primaries (IL ’92, MI ’92), United States - Senate Elections (IL ’90, IA ’90, AZ ’94, NJ ’94, PA ’94, TX ’94, VA ’94).

SEAT-SHARE MARKETS AND OTHERS: Australia (Parliamentary Election ’93), Canada (Parliamentary Elections ’93,

’96), Netherlands (Parliamentary Election ’94 - Second Chamber; European Parliamentary Election ’94), United States -

House of Representatives (’94), United States - Senate (’94).

Emilio Barone & Federico Carli, 2025

Figure 2 shows no significant forecasting errors. On average - particularly in markets

related to major U.S. elections - forecast accuracy is very high.

The variability in forecasting accuracy across U.S. elections can be explained by three

main factors:

1. markets associated with presidential elections perform better than markets

associated with lower-profile elections at the congressional, state, or local

level;

2. markets with higher trading volume close to election day perform better

than those with lower trading volume;

3. markets with a smaller number of contracts (i.e., fewer candidates or

parties) perform better than those with a larger number of contracts.

3.3.3. Opinion Polls

Figure 3 compares the relative performance of prediction markets and opinion polls.

Because market prices change continuously, a choice must be made regarding which price

should be used for forecasting purposes.

Emilio Barone & Federico Carli, 2025

Figure 3: Presidential Elections (Vote-Share Markets): Forecasting Errors (Polls and Markets).

Source: Berg et al. (2000).

Note: The forecasts derived from the major opinion polls conducted during the week preceding the presidential elections are

compared with those generated by the vote-share markets over the same week and at midnight on the day preceding the

election.

Two measures are reported in Figure 3:

(i) the price observed at midnight on the day preceding the election; and

(ii) the average price (weighted by trading volume) observed during the week

preceding the election.

The first measure incorporates all information available to traders as of midnight on

the day before the election, but it is subject to high variability due to the thinning of the

order book on the final trading day.

The second measure reflects trades occurring contemporaneously with major opinion

polls.

Market performance - measured using both pricing methods - was superior to that of

opinion polls in 9 out of 15 cases. The mean absolute forecasting error of opinion polls

was 1.93 percent, while that of markets was 1.49 percent using the first measure and 1.58

percent using the second.

In some cases - specifically, the U.S. presidential elections of 1988 and 1992 - the

performance of prediction markets was clearly superior to that of opinion polls. In most

other cases, markets performed approximately on par with polls, sometimes slightly

worse but more often slightly better.

3.4. Educational Uses

The IEM have been used to study a wide range of issues. They help bridge the gap

between traditional laboratory markets and real-world markets and provide information

not typically available in financial markets, including individual traders’ orders, detailed

transaction data, complete order-book information, portfolio compositions of individual

traders, and demographic characteristics of participants.

The IEM also allow researchers to interview traders at any point in time, record their

responses, and link those responses to other available information, thereby

complementing existing research methodologies.

4. Trade-Sports

4.1. Elections

Emilio Barone & Federico Carli, 2025

Prediction markets allow researchers to assess the relative popularity of potential

candidates in elections. In this respect, contracts on prospective Democratic and

Republican candidates for the 2008 U.S. presidential election traded on Trade-Sports are

particularly informative. For example, at the end of August 2005, the contract on Hillary

Clinton traded in the range of 41.5-41.9 (Figure 4), significantly outperforming other

potential candidates.

Figure 4: Contracts on Democratic Candidates in the 2008 U.S. Presidential Election.

Source: Trade-Sports, August 23, 2005 (2:24 pm).

4.1.1. Arnold Schwarzenegger

Figure 5 reports bid and ask quotes recorded at four-hour intervals on two markets –

Trade-Sports and the World Sports Exchange – both of which traded a contract on the

election of Arnold Schwarzenegger as Governor of California. Opportunities for risk-free

arbitrage, achieved by purchasing the contract at a lower ask price on one market and

selling it at a higher bid price on the other, were virtually nonexistent.

Emilio Barone & Federico Carli, 2025

Figure 5: 2003 California Gubernatorial Election.

Source: Wolfers, & Zitzewitz (2004).

4.2. Current Events

4.2.1. Saddam Hussein

An example of contracts linked to current events is provided by the so-called Saddam

securities. These contracts, traded on Trade-Sports, paid a payoff of $100 if Saddam

Hussein were removed from power by the end of June 2003.

Figure 6 shows that the price of these securities moved closely in line with two other

indicators: the “Saddameter”, developed by journalist William Saletan (Slate.com) to

measure the perceived probability of a U.S. war with Iraq, and the price of oil, an obvious

barometer of geopolitical tension in the Middle East.

Emilio Barone & Federico Carli, 2025

Figure 6: Risk of War in Iraq.

Source: Wolfers, & Zitzewitz (2004).

On March 19, 2003, U.S. President George W. Bush announced the beginning of the

military campaign against Iraq. On April 5, U.S. forces entered Baghdad, and on May 1

- 43 days after the start of the war - Bush announced that “major combat operations in

Iraq have ended.” As shown in Figure 6, the Saddameter rose to levels close to 100

already at the beginning of March, while the probability of Saddam Hussein’s removal

by June exceeded 90 percent immediately after the start of the military campaign.

4.2.2. Bin Laden / Al-Zarqawi

Contracts similar to those written on Saddam Hussein were subsequently traded on

Osama Bin Laden and Abu Mus’ab Al-Zarqawi (Figure 7). At the end of February 2005,

the higher prices of contracts linked to Al-Zarqawi relative to those linked to Bin Laden

indicated that the market assigned a relatively low probability to the capture of either

individual by June 2005.

Emilio Barone & Federico Carli, 2025

Figure 7: Contracts on Bin Laden and Al-Zarqawi.

Source: Trade-Sports, February 25, 2005.

4.2.3. Weapons of Mass Destruction in Iraq

In some cases, an entire family of contracts written on the same underlying event but

with different maturities is traded. This is the case for contracts linked to the discovery of

weapons of mass destruction in Iraq. Figure 8 shows prices for four contracts traded on

Trade-Sports, with maturities in May, June, July, and September 2003. Prices moved

closely together, reflecting the diffusion of a common stream of information, and

converged to zero as maturity approached.

Figure 8: Will Weapons of Mass Destruction Be Discovered in Iraq?

Source: Wolfers & Zitzewitz (2004).

Emilio Barone & Federico Carli, 2025

4.2.4. Olympic Games

Contracts have also been traded on the selection of host cities for major international

events, such as the 2012 Olympic Games. On February 25, 2005 - several months before

the decision by the International Olympic Committee (July 6, 2005) - Trade-Sports prices

favored Paris over London, which ultimately won the bid (Figure 9).

Figure 9: Current Events.

Source: Trade-Sports, February 25, 2005.

4.2.5. Palestinian State

On the same date (February 25, 2005), the contract linked to the establishment of a

Palestinian state by the end of 2005 traded in the range of 11-13 (Figure 9). By August

30, 2005, the same contract traded at 5.0-5.1.

4.3. Federal Reserve

Prediction markets also provide a useful alternative to opinion polls in the context of

appointments to high-level positions in institutions of international relevance. Table 1

reports prices for contracts linked to potential successors to Alan Greenspan as Chairman

of the Federal Reserve, effective from January 2006.

Emilio Barone & Federico Carli, 2025

Table 1: Next Chairman of the Federal Reserve.

Source: Trade-Sports (August 23, 2005, 1:59 pm).

Error! Reference source not found. shows prices for the Trade-Sports contract on

Ben Bernanke becoming Chairman of the Federal Reserve. On Monday, October 24,

2005, at 1:00 p.m., U.S. President George W. Bush announced Bernanke’s nomination as

Greenspan’s successor. Prices remained below $40 until 9:49 a.m., then rose rapidly to

reach $99.5 by noon. In slightly more than two hours, 1,250 contracts were traded,

corresponding to a notional value of approximately $10,000.

Figure 10: Ben Bernanke as Chairman of the Federal Reserve.

Source: Trade-Sports, October 23, 2005 - October 24, 2005.

4.4. Weather

4.4.1. Hurricane Activity

Trade-Sports also traded contracts linked to Hurricane Katrina. Five contracts were

listed, paying $1 if the hurricane’s intensity - measured on a scale from 0 to 5 - reached

level 3 or higher upon impact in Louisiana (LA), Mississippi (MS), Alabama (AL),

Florida (FL), or in none of these locations. From late morning on August 28 through the

morning of the following day, prices rose steadily from above $70 to $100.

Name

Bid

Ask

Last

Volume

Change

Ben Bernanke

34.0

39.0

37.0

458.0

0.0

Martin Feldstein

20.0

26.5

24.0

153.0

0.0

Lawrence Lindsay

20.0

25.5

25.0

328.0

-0.5

R. Glenn Hubbard

18.0

18.8

18.5

255.0

0.0

Robert McTeer

4.1

2.0

12.0

0.0

Roger Ferguson

4.0

2.0

4.0

0.0

Donald Kohn

1.0

4.0

3.0

27.0

0.0

John Taylor

3.9

2.0

12.0

0.0

Manuel H. Johnson

3.5

1.0

0.0

Emilio Barone & Federico Carli, 2025

5. Hollywood Stock Exchange

The Hollywood Stock Exchange (HSX), founded in 1996, is a subsidiary of Cantor

Index Holdings, which is part of the Cantor Fitzgerald group. Among the contracts traded

on the HSX are movie stocks, star bonds, movie options, and award options.

Prices of movie stocks reflect market expectations regarding box-office revenues

during the first four weeks of theatrical release. For example, a quoted price of 75

(expressed in Hollywood dollars, H$) corresponds to expected box-office revenues of $75

million.

Trading in movie stocks begins when a film’s “shares” are offered to the public through

an Initial Public Offering (IPO), typically several months before the film is released. For

instance, trading in the film Vanilla Sky (ticker symbol VNILA) began on July 26, 2000,

at a price of H$11. Upon registration, each trader receives an initial endowment of H$2

million and may hold no more than 50,000 shares of any single film. Trading activity

generally peaks in the days preceding a film’s release. In the case of Vanilla Sky,

approximately 22 million shares were traded on the day prior to its release.

Trading is halted on the day the film opens in theaters, in order to prevent unfair

advantages for participants who might have access to box-office data before such

information becomes public. Consequently, the halt price - the final price observed prior

to the trading halt - represents a point estimate of the film’s expected success before

release.

For Vanilla Sky, the halt price was H$59.71 (Error! Reference source not found.).

Trading resumes immediately after the opening weekend. The reopening price is

determined on the basis of actual box-office revenues, using a conversion factor. When a

film opens on a Friday, opening-weekend box-office revenues (in millions of dollars) are

multiplied by 2.9 to compute the adjusted price (adjust price). This multiplicative factor

is based on the assumption that total box-office revenues during the first four weeks of

release amount to 2.9 times the revenues earned during the opening weekend. In the case

of Vanilla Sky, opening-weekend revenues were approximately $25 million, implying an

adjusted price of H$72.5 (= $25 × 2.9). Movie stocks are delisted after four weeks of

theatrical release, at which point the delist price is calculated. For Vanilla Sky, which was

delisted on January 7, 2002, the delist price was H$81.1, reflecting total box-office

revenues of $81.1 million during the first four weeks.

Emilio Barone & Federico Carli, 2025

Figure 11: Hollywood Stock Exchange: Vanilla Sky.

Source: Elberse & Bharat (2005).

Star bonds represent actors and directors. Prices of star bonds reflect both the box-

office performance of the films in which they appear - measured by the Trailing Average

Gross (TAG) - and their future potential, as assessed by HSX traders. If an actor or

director ends his or her career (due to death, retirement, or other reasons), the nominal

value at which the corresponding star bonds are redeemed equals the TAG. The TAG

measures a star’s average box-office performance based on the five most recent films.

Bond prices are adjusted whenever one of the films exits the market.

Movie options, both calls and puts, are written on a film’s box-office performance

during its opening weekend. For example, an H$15 call option on the film Jillian in June

has a strike price of H$15 and pays the maximum of zero and the difference between the

film’s actual opening-weekend box-office revenues and $15 million.

Award options, associated with the Annual Academy Awards (Oscars), have final

payoffs equal to either H$0 or H$25. Five options are traded - one for each nominee - in

each of the eight major award categories: Best Picture, Best Actor, Best Actress, Best

Supporting Actor, Best Supporting Actress, Best Director, Best Original Screenplay, and

Best Adapted Screenplay.

Forecasts generated by the Hollywood Stock Exchange regarding box-office

performance have proven to be highly accurate, as shown in Figure 12.

Market prices

have also been used to evaluate the effectiveness of advertising campaigns. The HSX has

likewise demonstrated considerable accuracy in predicting Oscar winners.

See also Pennock et al (2001).

Emilio Barone & Federico Carli, 2025

Figure 12: Hollywood Stock Exchange: Box-Office Revenues of 489 Films, 2000-2003.

Source: Wolfers & Zitzewitz, 2004.

6. Economic Derivatives

Economic derivatives are derivatives whose payoffs depend on the release of

macroeconomic data. For example, options written on nonfarm payrolls are settled when

the employment report is released and the corresponding payroll figure becomes public.

The most common instruments traded in these markets are digital (binary) options. A

digital call (put) pays $1 if the macroeconomic outcome exceeds (falls below) the strike.

Typically, between 10 and 20 options - of both call and put types - are traded, each with

a different strike price. In addition to digital options, digital ranges are also traded; these

pay $1 if the macroeconomic outcome lies within a specified interval (range) bounded by

two strike prices. Other contracts traded in this market - such as capped vanilla options

or forwards - are simple portfolios of digital ranges.

Figure 13 reports prices of digital ranges written on the monthly rate of change in retail

sales, observed during the auction held on May 12, 2005 (conducted shortly before the

official retail sales data were released).

Emilio Barone & Federico Carli, 2025

Figure 13: Economic Derivatives: Retail Sales.

Source: Gürkaynak, & Wolfers (2005).

The Economic Derivatives market operated by Goldman Sachs, Deutsche Bank, and

ICAP is distinguished by its specific market design, which is based on proprietary

software developed by Longitude, a firm headquartered in New Jersey. Whereas most

prediction markets rely on a continuous double-auction mechanism, the economic

derivatives market is organized around a discrete sequence of auctions, with the objective

of maximizing liquidity.

Each individual auction is conducted using a mutualistic system (pari-mutuel system),

similar to the totalizator used in horse racing. The aggregate prize pool, net of a

management fee, is distributed among the winning positions. The price at which

participants enter the various contracts is not known at the time orders are submitted, but

only once the auction closes. During the period preceding the auction’s close, only

indicative prices are communicated; these coincide with actual transaction prices only if

no further orders are submitted.

Emilio Barone & Federico Carli, 2025

Market participants may submit limit orders. The Longitude software determines

equilibrium prices that maximize traded quantities. As in uniform-price auctions, all

contracts of the same type are executed at the same price, regardless of the individual

limit prices specified by participants.

Prices reflect the relative demand for the different contracts. The auction mechanism

always allows a price to be determined for each contract, even when there is trading

interest on only one side of the market (i.e., when all participants wish to buy or all wish

to sell). An instructive analogy can be drawn with horse racing: even if no one is willing

to “sell” a particular horse, odds are nevertheless determined by the totalizator. Selling

one horse is equivalent to buying all the others.

7. Policy Analysis Market

In July 2003, the press began reporting on a project promoted by the Defense Advanced

Research Projects Agency (DARPA), an agency of the U.S. Department of Defense. The

project aimed to establish a Policy Analysis Market (PAM) for trading contracts linked

to geopolitical risks. The proposed contracts were based on indicators of economic well-

being, civil stability, military capabilities, and measures of conflict, and - looking forward

- also on specific discrete events.

For example, the design of these contracts sought to address questions such as: “At

what rate will Egypt’s non-oil production grow next year?” or “Will the U.S. military

withdraw from Country A within the next two years?” In addition, the exchange would

have offered combinations of contracts, allowing economic and political events to be

linked together. The underlying objective was to determine whether the existence of such

markets could (i) facilitate the prediction of future events and (ii) help to clarify

perceptions of the interconnections among different events.

The publication of press articles on the DARPA initiative was followed by a sharp

political backlash. Several critics strongly attacked DARPA, accusing it of proposing

“futures on terrorism.” Rather than committing its political capital to defending a

relatively small project, DARPA ultimately chose to withdraw the proposal.

Ironically, in the aftermath of the DARPA controversy, prediction markets themselves

provided a striking illustration of their ability to generate information about the

probabilities of future events. Trade-Sports introduced a new contract that would pay

$100 if the head of DARPA, Admiral John Poindexter, were removed from office by the

end of August 2003. Early trading suggested that the probability of Poindexter’s dismissal

by the end of August was approximately 40 percent. Price fluctuations closely tracked the

evolution of news.

On July 31, around midday, the press began citing reliable Pentagon sources indicating

that Poindexter’s resignation was imminent. Within minutes of the first newswire reports

- and several hours before the information became widely disseminated - the contract

price jumped from $40 to $80. News agencies did not specify the exact date of

Poindexter’s resignation, which explains why prices did not immediately approach $100.

Emilio Barone & Federico Carli, 2025

In early August, the price gradually declined toward $50. On August 12, Poindexter

submitted a letter of resignation indicating that he would step down on August 29. On the

same day, the contract price surged and reached a level of $96.

8. Subjective Probabilities and Risk-Neutral Probabilities

Mark Rubinstein (University of California at Berkeley) has emphasized the close

correspondence between winner-takes-all markets and markets for state-contingent

claims, which form the foundations of modern “core” financial economics. In particular,

he has noted that the state-contingent price equals the present value of the product of the

(discounted) subjective probability and a risk-adjustment factor:

“The winner-takes-all market at the University of Iowa, an Internet-based market on

U.S. presidential elections, immediately comes to mind as a concrete example. In 2000,

participants could purchase at price PB a contract that would pay X=$1 if Bush were

elected and $0 otherwise. Alternatively, they could purchase at price PG a contract that

would pay X=$1 if Gore were elected and $0 otherwise. Ignoring the small probability

of a third candidate winning and ignoring interest, the absence of arbitrage opportunities

requires that the sum of prices satisfy PB+ PG=$1. In fact, this condition held. One is then

naturally led to ask (in the spirit of Huygens): is PB the subjective probability that Bush

will win, and is PG the subjective probability that Gore will win? No, this is not the case.

For example, if a risk-averse individual expects economic conditions to be better under

Bush than under Gore, the utility derived from receiving an additional dollar if Gore is

elected will be greater than the utility derived from receiving an additional dollar if Bush

is elected. Alternatively, it may be that an individual bets on Bush but would feel so

disappointed if Bush were elected that he would be unable to enjoy the extra dollar in the

same way as he would if he had bet on Gore and Gore had been elected. As a result,

prices of contracts on Bush and Gore are influenced not only by subjective probabilities

but also by the utilities associated with the respective events. In conclusion, the price PB

of the contract on Bush will be slightly lower than the probability of a Bush victory, while

PG will be correspondingly higher. Their sum will nevertheless equal $1.”

For further clarification, consider the following numerical example provided by

Rubinstein.

The case examined is that of an insurance policy against earthquake risk

(Figure 14).

See Rubinstein (2005). See also Kahneman & Tversky (1979).

See Rubinstein (1999).

Emilio Barone & Federico Carli, 2025

Figure 14: Present Value of an Insurance Policy Against Earthquake Risk.

Source: Rubinstein (1999).

The payoff of the policy varies as a function of the damage incurred, which is correlated

with earthquake intensity (measured on the Richter scale). The subjective probability that,

over the next year, no earthquake occurs or that any earthquake is of negligible intensity

(between 0 and 4.9 on the Richter scale) is equal to 85 percent. The remaining 15 percent

probability is assigned to earthquakes of greater intensity: 10 percent to earthquakes of

mild intensity (5.0-5.4), 3 percent to those of moderate intensity (5.5-5.9), 1.5 percent to

those of medium intensity (6.0-6.9), and 0.5 percent to the most severe earthquakes (7.0-

8.9).

Subjective probabilities must then be transformed into risk-neutral probabilities by

multiplying them by appropriate coefficients reflecting risk aversion. The next step

consists of multiplying the risk-neutral probabilities by the corresponding policy payoffs.

This yields the expected values of the individual state-contingent claims - that is, the

expected values of securities that pay off if, and only if, an earthquake of a given intensity

occurs (mild, moderate, medium, or severe). Discounting these expected values at the

one-year risk-free interest rate produces the state-contingent prices, i.e., the current prices

of the state-contingent claims. Finally, the sum of the state-contingent prices yields the

current value of the insurance policy.

9. Regulation

Prediction markets have been expanding, and their diffusion raises new regulatory

challenges. The Commodities Futures Trading Commission (CFTC) - which in the past

Emilio Barone & Federico Carli, 2025

effectively authorized the Iowa Electronic Market through a no-action letter - has since

been required to examine a growing number of applications for authorization.

In a recent paper, two criteria for the regulatory admissibility of contracts traded on

information markets were proposed:

Criterion 1. The contract traded on the information market must be capable of offering

significant opportunities for financial hedging.

Criterion 2. Prices generated by contracts traded on the information market must be

capable of providing relevant information that improves economic decision-making.

The authors of that paper subsequently attempted to assess whether contracts traded on

Trade-Sports satisfy these two criteria. Their findings are reported in Table 2.

Table 2: Categories of Contracts Traded on Trade-Sports (2004).

10. Conclusion: Reimagining State Legitimacy in the Shadows

In a recent article published in Business Week, it was argued that prediction markets

rank among the ten new technologies that should appear on the radar of every chief

executive.

These markets can, in fact, be used to assess potential demand for new

“On the other hand, if event markets were outside of the CFTC’s jurisdiction, then they would have

to deal with 50 different state regulatory schemes. CFTC staff has been approached by several entities

interested in becoming designated contract markets and listing event-type contracts, so resolving this issue

has become a high priority.” See Gorham (2004).

See Hahn & Tetlock (2004).

See Kharif, Helm, and Lacy (2005).

Type of Contract

Examples

CFTC

Jurisdiction

Economic Purpose

Sports Events

Basketball, football, baseball, boxing, golf,

soccer, horse racing

Sports betting contracts typically do not

satisfy either of the two criteria.

Current Events

2012 Olympic Games (host country), level of

security in the United States, events related to

the Middle East (such as the capture of Bin

Laden)

Yes

Contracts related to national security and the

organization of the Olympic Games may

contribute to improving economic policy

decisions.

Economic and

Financial Indicators

Indices, commodities, currencies

Yes

These contracts satisfy both criteria.

Judicial Matters

Supreme Court decisions, legal case against

Michael Jackson

Depends

Legal cases involving public figures are

unlikely to satisfy either criterion. The

contract related to the Supreme Court is

likely to satisfy the second criterion.

Politics

United States: presidential election, Senate

election

Yes

Contracts linked to electoral outcomes are

likely to satisfy both criteria.

Emilio Barone & Federico Carli, 2025

products, as illustrated by the case of the Hollywood Stock Exchange in the motion

picture industry.

More generally, prediction markets move in the direction envisioned by Arrow and

Debreu, Nobel Prize laureates in economics, insofar as they offer new hedging

opportunities for market participants and thereby enhance the efficiency of the economic

system. From this perspective, prediction markets represent a valuable complement to

derivatives markets.

Acknowledgement

Heartfelt thanks to Paolo Savona and Massimiliano Talli for their helpful comments.

Funding

No funding was received for conducting this study.

Conflicts of interest/Competing interests

The author states that there is no conflict of interest. The funders had no role in the design of

the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or

in the decision to publish the results.

Bibliography

Angrist, Stanley W. 1995. “Iowa Market Takes Stock of Presidential Candidates.” The Wall Street Journal,

August 28.

Beckmann, K., and M. Werding. 1996. “Passauer Wahlbörse: Information Processing in a Political Market

Experiment.” Kyklos 49: 171–204.

Berg, Joyce, Robert Forsythe, Forrest Nelson, and Thomas Rietz. 2000. “Results from a Dozen Years of

Election Futures Markets Research.” University of Iowa, November.

———. 1997. “The Iowa Electronic Markets.” In Blackwell Encyclopedic Dictionary of Finance, edited

by D. Paxson and D. Wood. Oxford, UK: Blackwell.

———. 1996. “What Makes Markets Predict Well? Evidence from the Iowa Electronic Markets.” In

Understanding Strategic Interaction: Essays in Honor of Reinhard Selten, edited by W. Albers,

W. Güth, P. Hammerstein, B. Moldovanu, and E. Van Damme, 444–63. New York: Springer.

———. 1998. “Election Forecasts from a Futures Market.” Unpublished manuscript, University of Iowa.

Bondarenko, O., and P. Bossaerts. 1999. “Expectations and Learning in Iowa.” Unpublished manuscript,

California Institute of Technology. Forthcoming in Journal of Banking and Finance.

Brüggelambert, G. 1988. “Passauer Wahlbörse: A Comment.” Unpublished manuscript, University GH

Essen, Essen, Germany.

Elberse, Anita, and Anand Bharat. 2005. “The Effectiveness of Pre-Release Advertising for Motion

Pictures.” Working paper, Harvard Business School, March 5.

Emilio Barone & Federico Carli, 2025

Forsythe, Robert, Forrest Nelson, G. R. Neumann, and J. Wright. 1991. “Forecasting Elections: A Market

Alternative to Polls.” In Contemporary Laboratory Experiments in Political Economy, edited by

T. R. Palfrey, 69–111. Ann Arbor: University of Michigan Press.

———. 1992. “Anatomy of an Experimental Political Stock Market.” American Economic Review 82:

1142–61.

Forsythe, Robert, T. Rietz, and T. W. Ross. 1999. “Wishes, Expectations and Actions: Price Formation in

Election Stock Markets.” Journal of Economic Behavior and Organization 39: 83–110.

Goldfinger, Charles. 2004. “Evolving Exchanges, Evolving Instruments: What Are We Going to Trade?”

Discussion paper, 2004 European Financial Markets Convention, Frankfort, June 2–4.

Gorham, Michael. 2004. “Event Markets Campaign for Respect.” Futures Industry Magazine,

January/February.

Granberg, D., and E. Brent. 1983. “When Prophecy Bends: The Preference-Expectation Link in U.S.

Presidential Elections.” Journal of Personality and Social Psychology 45: 477–91.

Gürkaynak, Refet S., and Justin Wolfers. 2005. “Macroeconomic Derivatives: An Initial Analysis of

Market-Based Macro Forecasts, Uncertainty, and Risk.” Working paper, May 24.

Hahn, Robert W., and Paul C. Tetlock. 2004. “A New Approach for Regulating Information Markets.”

Working paper, American Enterprise Institute–Brookings Joint Center for Regulatory Studies,

December.

Jacobsen, B., J. Potters, A. Schram, F. Van Windem, and J. Wit. 2000. “(In)accuracy of a European Political

Stock Market: The Influence of Common Value Structures.” European Economic Review 44: 205–

30.

Kahneman, Daniel, and A. Tversky. 1979. “Prospect Theory: An Analysis of Decision Under Risk.”

Econometrica 47: 263–91.

Kharif, Olga, Burt Helm, and Sarah Lacy. 2005. “The CEO’s Tech Toolbox.” BusinessWeek Online, July

26.

Oliven, K., and T. Rietz. 1995. “Suckers are Born but Markets are Made: Individual Rationality, Arbitrage

and Market Efficiency on an Electronic Futures Market.” Unpublished manuscript, Department of

Finance, University of Iowa.

Ortner, G. 1996. “Forecasting Markets: An Industrial Application.” Unpublished manuscript, Technical

University of Vienna.

Pennock, David M., Steve Lawrence, Finn Årup Nielsen, and C. Lee Giles. 2001. “Extracting Collective

Probabilistic Forecast from Web Games.” In Proceedings of the 7th ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining, 174–83. New York: ACM.

Rietz, T. 1998. “Enforcing Arbitrage Restrictions in Experimental Asset Markets.” Unpublished

manuscript, University of Iowa.

Rubinstein, Mark. 1999. Rubinstein on Derivatives: Futures, Options and Dynamic Strategies. Risk Books,

November 22.

———. 2005. “Great Moments in Financial Economics: IV. The Fundamental Theorem (Part I).” Journal

of Investment Management, forthcoming.

Slemrod, J., and T. Greimel. 1999. “Did Steve Forbes Scare the Municipal Bond Market?” Journal of Public

Economics 74: 81–96.

Smith, Vernon L. 2002. “Constructivist and Ecological Rationality in Economics.” Nobel Prize lecture,

December 8.

Wolfers, Justin, and Andrew Leigh. 2001. “Three Tools for Forecasting Federal Elections: Lessons from

2001.” Research Paper No. 1723, Stanford University, November.

Wolfers, Justin, and Eric Zitzewitz. 2004. “Prediction Markets.” Discussion Paper No. 03-25, Stanford

Institute for Economic Policy Research (SIEPR), Stanford University, April 19.

Emilio Barone & Federico Carli, 2025

Appendix A: Prediction markets

Market

Events

Austrian Electronic Markets

http://www.imw.tuwien.ac.at/apsm/

(Technische Universität Wien)

Elections

Betfair

www.betfair.com

Financial Bets, Politics, Special Bets, Sports (American football, Australian rules, Baseball,

Basketball, Boxing, Cricket, Cycling, Gaelic Games, Golf, Greyhound Racing, Horse

Racing, Ice Hockey, Motor Sport, Poker, Rugby league, Rugby union, Soccer, Tennis)

Centrebet

www.centrebet.com

Elections, Sports (American football, Australian rules, Baseball, Basketball, Boxing,

Cricket, Darts, Football, Handball, Ice Hockey, Motor Racing, Rugby league, Rugby union,

Speedway, Surfing, Tennis, Trotting, Greyhound racing, Harness racing, Horse racing)

Economic Derivatives

www.economicderivatives.com

(Goldman Sachs, Deutsche Bank, ICAP)

Macroeconomic events (Nonfarm Payrolls, International Trade Balance, US GDP, ISM

Manufacturing PMI, US Initial Jobless Claims, Retail Sales less Autos, Eurozone HICP)

Election Stock Market

esm.ubc.ca

(University of British Columbia)

Elections

Foresight Exchange

www.ideosphere.com

Arts and Entertainment (Entertainment Technology, Literature, Movies, Television),

Finance (US Finance, World Finance), Misc (Religion, New Age, etc.), News (Disasters, US

News, World News), Politics (UK Politics, US Politics), Science and Technology (Computer

Industry, Computer Technology and Benchmarks, Computing Theory, Encryption,

Factoring, General Science, Idea Futures and Experimental Claims, Internet, Math,

Medicine, Biochemistry, Physics, Space)

Hedgestreet

www.hedgestreet.com

Commodities (Gold, Silver), Crop Production (Corn, Soybean), Currencies (EUR/USD,

GBP/USD, USD/CHF, USD/YEN), Economic Indicators (ISM Manufacturing PMI, Retail

Sales), Employment (Initial Claims, Nonfarm Payrolls), Fuel (California Gasoline, Crude

Oil, Crude Oil Inventory, Diesel, Gasoline, Natural Gas, Natural Gas Inventory), Housing

Prices (Chicago, Los Angeles, Miami, New York, San Diego, San Francisco), Inflation

(CPI), Interest Rates (Fed Funds Rate), Mortgage Rates (1-yr ARM, 30-yr FRM)

Hollywood Stock Exchange

www.hsx.com

(Cantor Fitzgerald)

Entertainment (Celebrities, Movies)

Innovation Futures

innovationfutures.com/

Business and Technology Trends (Technology Tipping Points, UK Innovation, Economy and

Growth, Financial Markets)

Iowa Electronic Markets

http://www.biz.uiowa.edu/iem/

(University of Iowa)

Political Markets (US Presidential Winner Takes All Market, US Presidential Vote Share

Market, Federal Reserve Monetary Policy Market), Economic Indicator Markets, Classroom

Markets (Computer Industry Returns Market, Microsoft Price Level Market)

Net Exchange

www.nex.com

Corporate events

News Futures

(www.newsfutures.com)

News (World, Nation, Challenges, Tech), Money (Financial Markets, Companies, Beyond

Numbers), Sports (Major League Baseball, College Football, NFL Football, Tennis, Auto

racing, Soccer), Entertainment (Video Games, Movies, Television, Travel)

TradeSports

www.tradesports.com

Current Events, Entertainment, Financial, Legal, Politics, Sports (Auto Racing, Baseball,

Basketball - NBA, Basketball - NCAA, Boxing, Cricket, Football - NCAA, Football - NFL,

Golf, Hockey, Horse Racing, Soccer - UK, Soccer- South America, Tennis)

World Sports Exchange

www.wsex.com

Entertainment, Sports (Pro Football, College Football, Baseball, Canadian Football, Women

Basketball, Boxing, Golf, Tennis, Soccer, Horses, Auto Racing)

Emilio Barone & Federico Carli, 2025

APPENDIX B: Foresight Exchange: Contracts