Forecast market

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Example GUI of a web-based securities market

Forecast markets are virtual market platforms that predict the outcome of events . Forecast markets exist in the form of online betting exchanges or virtual securities markets, each of which is implemented on an electronic platform and has its own odds or price determination mechanism. They are used as a competitive system to other forecasting instruments.

Types of forecast markets

Virtual securities markets

Virtual securities market using the example of the Federal People's Initiative “for food from GM-free agriculture” , voted on November 27, 2005.
1. Inquiries (Ask)
2. Offers (Bid)
3. Last traded price. Provides the forecast of the voting outcome.
4. Portfolio (one yes and no share at a fixed price of 100)

In contrast to financial markets, no significant amounts of money or legal claims are traded on virtual securities markets. A virtual share represents a future event or a market condition (for example, sales figures for a product in December or goals scored in a football game). The final value of the share depends on the actual outcome of the event, that is, for example 1 (virtual) euro per 100 sales. Participants can then act on their assessments based on this relationship. In contrast to stock market games, which take over the price of real stock exchanges, buy and sell orders are carried out on a forecast market using a separate trading mechanism. Rather, the incentive system is decisive here to make the best forecast. One therefore speaks of gamification .

Betting exchange

Participants place bets on the outcome of sporting competitions and social or political events on online betting exchanges. In contrast to virtual securities markets, real money is used to bet on the outcome of events. A placed bet results in profit or loss of real money. A particular difference to traditional betting with online betting exchanges is that the betting participants bet against each other and not against a bookmaker .

Economics

A central explanation for the precision of forecast markets is the incentive system : actors who buy cheap and sell high are financially rewarded for improving the forecast. Actors who buy high and sell low are thus financially punished for the deterioration of the forecast. This use of incentive mechanisms is also called gamification .

The theoretical justification for the information efficiency of these markets is provided by the Hayek hypothesis, which states that the asymmetrically distributed information of market participants can be most efficiently aggregated through competition in a market.

Areas of application

Forecast markets can be used where it is a matter of predicting uncertain events or where future-oriented answers, analyzes or forecasts are to be given. So far, prediction markets have been used by the US Department of Defense to predict terrorist attacks, in the healthcare industry to predict flu outbreaks and the effectiveness of new drugs, and by various companies to predict sales figures or product quality. Robin Hanson advocates a futarchy in which policy instruments are laid down in forecast markets.

Examples of political elections

The fact that forecast markets can deliver more accurate predictions than opinion polls was shown in the 2005 Bundestag election: The Political Stock Market online election exchange predicted that the CDU / CSU parliamentary group would win with 38.5 percent of the vote. This result is significantly closer to the official final result of 35.2 percent than the average predicted 40 percent of the opinion polls.

The results of the American election exchange Iowa Electronic Market also show that forecast exchanges provide very reliable forecasts. In the four US presidential elections from 1988 to 2000, the Iowa Electronic Market predicted the Republican and Democratic voting share more accurately than the most prestigious public opinion polls. 150 days before the election, predictions by the Iowa Electronic Market deviated only five percentage points from the official final result. In the week leading up to the election, the forecast error was just 1.5 percent. In contrast, the Gallup Institute, for example, erred in the last polls by an average of 2.1 percentage points.

Legal and legality

The use of forecast markets is restricted by legal restrictions in the respective countries. The core point is the classification of the system as a game of skill in contrast to the game of chance . In each country, different legal framework conditions apply or there is no special clarification on the question of classification.

See also

Individual evidence

  1. ↑ Social Forecasting (Part 1): Swarm intelligence and forecast markets reach operational practice . Dirk Elsner. August 4, 2012. Retrieved February 13, 2013.
  2. ↑ Arrow, K., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J., Levmore, S., Litan, R., Milgrom, P., Nelson, F. , Neumann, G., Ottaviani, M., Schelling, T., Shiller, R., Smith, V., Snowberg, E., Sunstein, C., Tetlock, P., Tetlock, P., Varian, H. , Wolfers, J., Zitzewitz, E. (2008): The Promise of Prediction Markets . Science. Vol. 320, pp. 877-878.
  3. ↑ Jörg Hackhausen (2006): Oracle from the Internet - forecast exchanges predict the future , In: WirtschaftsWoche, Handelsblatt publishing group
  4. ↑ Jürgen Kaube: The secret knowledge of gamblers . Frankfurter Allgemeine Sonntagszeitung, November 20, 2008.

literature

  • Joyce E. Berg, Forrest D. Nelson, Thomas A. Rietz: Prediction Market Accuracy in the Long Run . In: International Journal of Forecasting, 24, 2008, pp. 285–298 doi : 10.1016 / j.ijforecast.2008.03.007
  • Reneé Dye: The promise of prediction markets: A roundtable , in: The McKinsey Quartely, Issue 2, 2008
  • Jörg Hackhausen: Oracle from the Internet - forecast exchanges predict the future . In: WirtschaftsWoche, Handelsblatt publishing group, 2006
  • Jürgen Kaube: The secret knowledge of gamblers . In: Frankfurter Allgemeine Sonntagszeitung, November 20, 2008.
  • Ralf Ike: Performance Management, Synergy Potential of Knowledge Management and Business Intelligence as part of a holistic approach to strategic corporate management , 2008
  • Rietz et al. a .: Accuracy and Forecast Standard Error of Prediction Markets , University of Iowa, Working Paper
  • Martin Spann, Bernd Skiera: Internet-Based Virtual Stock Markets for Business Forecasting . In: Management Science, 49, 2003, pp. 1310–1326 PDF ( Memento from August 12, 2011 in the Internet Archive )
  • James Surowiecki: The Wisdom Of Crowds: Why The Many Are Smarter Than The Few And How Collective Wisdom Shapes Business , Economies, Societies And Nations Little, Brown, 2004
  • Justin Wolfers, Eric Zitzewitz: Prediction Markets . In: Journal of Economic Perspectives, 18, 2004, pp. 107–126 doi : 10.1257 / 0895330041371321

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