Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest.
[2] According to Paul Rhode and Koleman Strumpf, who have researched the history of prediction markets, there are records of election betting in Wall Street dating back to 1884.
[3] Rhode and Strumpf estimate that average betting turnover per US presidential election is equivalent to over 50 percent of the campaign spend.
[6] Prediction markets are championed in James Surowiecki's 2004 book The Wisdom of Crowds, Cass Sunstein's 2006 Infotopia, and Douglas Hubbard's How to Measure Anything: Finding the Value of Intangibles in Business.
[7] The research literature is collected together in the peer-reviewed The Journal of Prediction Markets, edited by Leighton Vaughan Williams and published by the University of Buckingham Press.
For instance, according to the efficient-market hypothesis, existing share prices always include all the relevant related information for the stock market to make accurate predictions.
[16] James Surowiecki raises three necessary conditions for collective wisdom: diversity of information, independence of decision, and decentralization of organization.
Based mostly on an idea in 1945 by Austrian economist Friedrich Hayek, prediction markets are "mechanisms for collecting vast amounts of information held by individuals and synthesizing it into a useful data point".
[25] One way the prediction market gathers information is through James Surowiecki's phrase, "The Wisdom of Crowds", in which a group of people with a sufficiently broad range of opinions can collectively be cleverer than any individual.
Oftentimes, the people in these crowds are skewed in their independent judgements due to peer pressure, panic, bias, and other breakdowns developed out of a lack of diversity of opinion.
One of the main constraints and limits of the wisdom of crowds is that some prediction questions require specialized knowledge that majority of people do not have.
In early 2017, researchers at MIT developed the "surprisingly popular" algorithm to help improve answer accuracy from large crowds.
For example, in the year 2000 IEM presidential futures markets, seeming "inaccuracy" comes from buying that occurred on or after Election Day, 11/7/00, but, by then, the trend was clear.
[30] Clouded by the similar mindset of users in prediction markets, they created a paradoxical environment where they began self-reinforcing their initial beliefs (in this case, that the UK would vote to remain in the EU).
[33] Koleman Strumpf, a University of Kansas professor of business economics, also suggests that a bias effect took place during the US elections; the crowd was unwilling to believe in an outcome with Donald Trump winning and caused the prediction markets to turn into "an echo chamber", where the same information circulated and ultimately lead to a stagnant market.
[37] Because online gambling is outlawed in the United States through federal laws and many state laws as well, most prediction markets that target US users operate with "play money" rather than "real money": they are free to play (no purchase necessary) and usually offer prizes to the best traders as incentives to participate.
[48] The advantage of making bets on combinations of outcomes is that, in theory, conditional information can be better incorporated into the market price.
New York was the hub, with activity shifting from poolrooms to the Curb Exchange (precursor to AMEX) and Wall Street offices.
In some elections, the volumes traded rivaled those of stocks and bonds, with daily odds reported in major newspapers like The New York Times.
[51] The CFTC has attempted to restrict election markets, arguing they resemble gaming rather than the financial derivatives it oversees.
The CFTC also targeted Polymarket, a crypto-based prediction market, resulting in the company moving offshore and paying a $1.4 million fine.