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Welcome to the Prediction Markets wiki of the Graduate School of Management, University of Haifa

This wiki grows out of ongoing research work in the field of Prediction Markets and Collective Intelligence which is performed at the Graduate School of Management of the University of Haifa, under the supervision of Prof. Sheizaf Rafaeli and Dr. Daphne Raban. At its core lies a mapping of domain vocabulary to market implementations which is presented in a paper "P-MART: Towards a Classification of Online Prediction Markets", currently under review.

The wiki is structured as a cross reference of Prediction Market implementations and market attributes. The description of each market is contained in a separate wiki article and tagged with its attributes (in the form of wiki categories). In this way it is possible to view the property of each market, and access any market from any of its market property. It is also possible to access all markets and market properties from the sidebar. In the main page you will find an overview of the Prediction Markets classification, where each of the market attribute is listed in bold and links to its category page.

The objective of this wiki is to act as a knowledge repository for researchers and practitioners in the field of Prediction Markets. True to our belief in the value of collaboration we encourage you to contribute to this wiki. It is expected that through on-going contribution the domain vocabulary will be fine-tuned as the domain knowledge expands, and the implementation repository will grow as new examples emerge. Contributors need to register first. In order to maintain the structure of this wiki please follow editing guidelines.

For more details contact Dorit Geifman



We can trace the origins of the Prediction Markets concept to the works of Friedrich Hayek, the winner of the Nobel Prize in Economics, and a social and political philosopher. Hayek claimed that the price mechanism serves to share and synchronize local and personal knowledge, allowing society's members to achieve diverse, complicated ends through a principle of spontaneous self-organization.

We are now starting to witness the proliferation of Information Markets into the corporate decision-making environment. One of the pioneering organizations to use Prediction Market as an internal decision-support tool is Hewlett Packard, which is using it as a sales forecasting tool. Intel too is using Information Markets for managing products forecast and production plans. There is evidence that many other organizations like Google, Eli Lilly and Microsoft are jumping into the water, but most of this activity is still in an experimental stage. Organizations are still trying to figure out the kind of incentives to be used, how to make sure that established hierarchies are not threatened and how to maintain a cost effective process etc.

What are Prediction Markets used for?

Prediction Markets are used to reveal, and aggregate information and opinion from dispersed audiences by utilizing the mechanism of financial markets. Implementations of Prediction Markets address the following goals:

  • Aggregate information - collect factual, unknown information which is segmented and / or scattered
  • Predict uncertain future events
  • Elicit opinion - obtain and evaluate preferences or new ideas from a large and distributed group of people

Prediction Markets cover a wide range of topics. These may be in the areas of Current or recreation events, Business topics, Policy issues, Synthetic events etc.

In what setting are markets used?

Prediction markets are implemented in a variety of environments

  • Public domain – open to the general public. May take the form of stand-alone web-sites e.g. Intrade or add-ons to larger web-sites e.g. FTPredict of the Financial Times.
  • Corporate - may be internal, limited to the employees, or external, open to other stakeholders such as customers or suppliers
  • Laboratory – a closed and controlled environment, set up specifically for academic experiments
  • Field experiments – controlled experiments in real-world settings

How do Prediction Markets work?

Prediction Markets differ in their internal design, operational model and structure of assets. In this section we will provide an overview and references for more detailed information.

Market models

The mechanism behind the market exchange and pricing may be:

  • Continuous Double-Auction (CDA) - buyers bid prices up and sellers bid prices down until a consensus is reached. CDA can be implemented with or without an automatic market maker.
  • Market Scoring Rules (MSR) – all traders can continuously update their score rating i.e. the estimates of the probabilities of the event, and the underlying pricing function calculates the new probabilities (price). The model implements an automatic market maker.
  • Dynamic Pari-Mutuel (DPM) – in a pari-mutuel model, all bets are placed in a pool which is later divided between the winners. This model takes the form of a continuous pari-mutuel system.

Security types

The security type defines the payoff model. It can be one of the following:

  • Winner-takes-all – the security is cashed out at a predefined price if its underlying event occurred, and is nullified otherwise. This model has been the most commonly used for binary (Yes / No) or multiple-option contracts.
  • Index – also known as linear security. In this case the price of the security reflects the estimated value of the outcome and its payoff is tied to the actual outcome of the event.
  • Volume-Weighted Average Price – the security is cashed out according to its average price weighted by transaction volume in X days prior to its expiration date. This method is used mostly in idea / opinion markets, when there is no absolute truth that can be used as a reference for cashing out the market
  • Conditional – the outcome of these securities is evaluated according to a combination of events i.e. the security pays $1 if some event happens conditionally to the occurrence of some other event.

Trading currency

Trading may be executed through the use of Real-money, i.e. trader’s money or play-money with a well defined monetary exchange rate, or Play-money, which is not directly tied to the final reward.

Trading funds

The source of the funds which are available to the traders may be:

  • Limited own – traders invest their own money, but the amount is limited by the market institution
  • Unlimited own – traders invest their own money and the amount is unlimited
  • Initial capital endowment – traders are endowed by market institution with initial capital, usually play-money.
  • Initial securities endowment – traders' accounts are initialized with a number of securities.

Market size and duration

The market size is defined by the number of active traders during market activity period. This may range from small, 15 or less, through medium – 15 – 50 traders, to large – larger than 50, common in public markets but can also be found in corporate settings

Markets should be open as long as new information streams in, whether information that is exogenous to the market or information that is inferred from market price, and as long as the actual outcome is yet unknown. Here their duration is arbitrarily defined as:

  • Short – does not exceed one day, in a laboratory setting usually lasts a few minutes
  • Medium – a few days, does not exceed one week
  • Long – longer than a week
  • Variable – the market closing date is defined ad-hoc, and is dictated by the topic of the market and the external events which affect the subject of trade.

Interaction means

Does the market platform or trading procedure enable complementary information exchange between traders?

  • No interaction – platform does not facilitate interaction among traders. Traders are instructed to refrain from off platform interaction.
  • On-platform – the platform provides for online interaction means such as chat or forums.
  • Off-platform – traders may exchange views outside the market about the events being traded.

The traders

The traders may self-select to join the trading activity, usually when they perceive themselves knowledgeable or interested in the topic of the market. Alternatively, they may be invited by the market owner according to the estimated potential of their contribution and expertise.

The traders may be stakeholders, having vested interest in the outcome of the event being traded, or they may have no-stake in the outcome, being fully neutral with respect to the outcome.

The information which is available to the group of traders may be perfect, i.e. the all information which is needed exists but is private to a subset of the traders, complete, i.e. each trader only holds uncertain or incomplete information but the combination of the information which is accessible to all traders forms a complete picture of the subject of trade, or incomplete, the available information, private or public, contains a variable level of uncertainty.

Why do traders participate?

Market institutions use a variety of incentive schemes in order to encourage participation. These may take the following forms:

  • Performance - payoffs are directly tied to the performance of the trader, i.e. gains profits from trading.
  • Performance rank – reward is associated with the rank of the trader in the descending list of performance.
  • Participation – reward is tied to participation. Can be associated with participation level or drawn by lottery.
  • Contribution – participants are rewarded for their contribution to the marketplace, e.g. creation of new markets.

The traders may be awarded a cash payment, a material, non-monetary prize, bonus shares or an intangible reward such as increased reputation or intrinsic satisfaction.

Is the market outcome accurate?

Does traders' behavior affect market accuracy?


Judgment biases


References & Bookmarks


  • Abramowicz, M. (2008). Predictocracy: Market Mechanisms for Public and Private Decision Making. Yale University Press.
  • Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Doubleday.

Wikis & Blogs

Research Data

Public exchanges that open their data for academic research

  • In Iowa Electronic Markets (IEM) you can find historical data and research papers.
  • In TradeSports you can gain access to historical data (2004-2006), mostly sports, some political and entertainment.


  • Ask (הוראת מכירה) - an order to sell a specified number of contracts at a specified price over a specified number of days
  • Bid (הוראת קניה) - an order to buy a specified number of contracts at a specified price over a specified number of days
  • Contract (חוזה) - the financial instrument being traded in the markets. It is the trading unit and is uniquely defined by a name, fundamental and expiration date.
  • Exchange (בורסה) - a set of markets.
  • Expiration date (תאריך פקיעה) – a pre-specified date, or a date of an event, at which the liquidation value of the contract is calculated.
  • Fundamental – a set of statistics which can objectively be measured
  • Liquidation value (שווי מימוש) – the value of the contract calculated based on the value of the fundamental on the expiration date.
  • Market (שוק) - a set of trading rules, traders, information and portfolios with a common area of interest.
  • Portfolio (תיק נכסים) - a set of contracts based on the same fundamental and expiration date.
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