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The Accuracy of Prediction Markets

The experience with the Iowa Electronic Markets for vote share suggests that such markets have relatively strong accuracy. In a paper considering fifteen elections, Joyce Berg and coauthors show that the markets on election eve were more accurate on average than were final preelection polls.40 The average absolute error of a vote share forecast derived from the market price at midnight before the election was 1.49 percent,41 whereas the average absolute error from the polls, averaged across all candidates and all polls, was 1.93 percent. Berg and her coauthors conclude that the markets therefore appear to be more accurate than the most widely available set of forecasting tools for elections.42 Another study suggests that participants in the Iowa Electronic Markets respond rationally to new information.43 Although the pattern of participants’ responses indicates that their original assessments may have been biased, this pattern is insufficient to create a profitable trading opportunity. One might tentatively infer that prediction markets will give reasonably accurate predictions, at least in the election context, though some imperfections may remain.

Robert Erikson and Christopher Wlezien have sought to debunk the claimed superiority of prediction markets to polls, at least well in advance of elections.44 Although on average the markets beat a naïve reading of polls, a more sophisticated methodology, discounting the favorite’s lead on the basis of research indicating that leads tend to shrink, makes polls better than the market. One danger of this conclusion is that it is often possible after the fact to construct a model that produces high levels of accuracy. We cannot be sure that Erikson and Wlezien would have chosen the same methodology if forced to announce a prediction algorithm before the elections they surveyed. Their approach, however, does not appear particularly complicated, and it at least suggests that we should not be confident that market prices are better than any that might be derived using alternative techniques, such as the regression analysis that Erikson and Wlezien provide.

It would be a mistake, however, to conclude from Erikson and Wlezien’s analysis that markets did a poor job of aggregating predictions. Their analysis of discounting of leads, after all, had not been performed prior to the election, and if it had been, reasonable observers might have differed about the extent to which past trends would continue into later elections. Erikson and Wlezien point out that given the volume of trading on the Iowa markets, their profit from trading on the strategy would have been quite low. Should they or others make available predictions based on their methodology in future elections, other traders making assessments might take these numbers into account in their own trading. Erikson and Wlezien say that polls “that are properly discounted for the favorite’s inflated lead outperform the market,” but the properly discounted numbers were not widely available at the time.

Prediction markets cannot necessarily be counted on to beat any other method of aggregating data and predictions. Laboratory experiments involving securities markets show that sometimes these markets do not reflect “rational expectations” by perfectly incorporating information.45 For instance, Joyce Berg and Thomas Rietz found small inefficiencies in the Iowa Electronic Markets that could serve as the basis of a profitable trading strategy.46 What makes prediction markets particularly useful for the media is that they are relatively objective while still accurate compared to other approaches to aggregation. Someone with an agenda, for example, promoting a particular candidate, might devise a statistical methodology that makes that candidate appear stronger than previously thought. And usually it is easy to find some basis for defending the methodology on the merits. A journalist is not in a good position to make decisions about whether the creators of a methodology were motivated by a desire to promote particular results or simply by a desire to develop the best predictions possible. And even if a journalist can be confident about objectivity, different objective people might reasonably take different approaches to arrive at different conclusions.

Of course, a prediction market can be seen as just another methodology, but at least it will not be easy to create a prediction market to promote a particular agenda. Robert Forsythe and coauthors have shown that although the participants in an early election market were predominantly Republicans, and though many participants only traded in favor of their preferred candidates, this did not produce a biased prediction.47 Rather, enough people traded both for and against their preferred candidates that the demographics of the trader community did not have much effect. The traders who were particularly influential were more likely than other traders to place limit orders at prices slightly different from the market price. This suggests that if one opens a prediction market to a modestly large group of traders, one will not be able to affect the market price much by choosing a demographically unrepresentative sample. It will be particularly difficult to skew a prediction market generally available to traders on the Internet.

The great virtue, then, of prediction markets is not that they will always be more accurate than alternative future methodologies or existing ones. Rather, it is that prediction markets are relatively objective. At the same time, there is a strong theoretical reason to believe that prediction market participants will seek to take into account at least easily accessible data such as opinion polls.48 By citing prediction market forecasts, a journalist can give a concise, consensus prediction, whether about an upcoming election or about any other event being forecast by a prediction market.

January 24th, 2008 Overview Comments ( 9 )

 

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