The writer B.K. Marcus has written;

"Even if markets can somehow better anticipate the outcome, prediction markets will not achieve their full potential until they incorporate the power of profit-seeking self-interest, which can only be found in the private world of risk and reward. Government-developed "planned markets" such as the Pentagon's PAM, "virtual markets" such as the Hollywood Stock Exchange, and low-risk hampered markets such as the IEM (which only allows $500 trading accounts) will not be able to operate as efficiently or accurately as would true capitalist markets, which allow for real profit and real loss. the power of the market lies in its ability to harness the power of self-interest."

Prediction market desciples often turn a blind eye to a paper by Robert S. Erikson and Christopher Wlezien entitled "Are Political Markets Really Superior to Polls as Election Predictors?

The authors argue that as prediction market prices reflect forecasts of what will happen on Election Day and trial-heat polls only register preferences on the day of the poll, it is inappropriate to naively compare them on any given day in advance of an election.

Transforming raw poll vote divisions into projections of the Election Day outcome and comparing these projections to vote-share prices they find that daily poll projections are superior to IEM prices. Indeed, in three of the five presidential elections with IEM vote share markets, poll projections were more accurate than market prices.

In four of the five elections (with one tie), the week’s average poll projection dominated the daily market price; "We have learned, however, that prices in the IEM vote share market are no better, and in fact a bit worse, at predicting the vote than are projections based on the day’s most recent polls.

Where the market holds to a view of the election at odds with the poll projections, it is somewhat more likely to be wrong than right". The authors conclude that by their tests the IEM prediction markets are not better than trial-heat polls for predicting elections. In fact, they say, by a reasonable as opposed to naïve reading of the polls, the polls dominate the markets as an election forecaster; "The Iowa election market’s performance has not been so special after all. For now, our results suggest the need for much more caution and less naïve cheerleading about election markets on the part of prediction market advocates".

In Interpreting the Predictions of Prediction Markets, CHARLES F. MANSKI notes that participants in prediction markets such as the Iowa Electronic Markets trade all-or-nothing contracts that pay a dollar if and only if specified future events occur. Moreover, he states that researchers engaged in empirical study of prediction markets have argued broadly that equilibrium prices of the contracts traded are 'market probabilities' that the specified events will occur.

Manski shows that if traders are risk-neutral price takers with heterogenous beliefs, the price of a contract in a prediction market reveals nothing about the dispersion of traders' beliefs and partially identifies the central tendency of beliefs.

Most persons have beliefs higher than price when price is above 0.5, and most have beliefs lower than price when price is below 0.5. The mean belief of traders lies in an interval whose midpoint is the equilibrium price. These findings persist even if traders use price data to revise their beliefs in plausible ways.

Manski demonstrates the danger of loosely interpreting prices in prediction markets as “market probabilities” that aggregate the information held by traders. He suggests that his specific findings about the relationship between price and the distribution of traders’ beliefs should be applied cautiously to actual prediction markets, where traders may or may not behave in the manner assumed here. Manski concludes that work by Gjerstad (2005) and Wolfers and Zitzewitz (2005), when taken in conjunction with his own, makes plain that interpretation of prices in actual prediction markets requires knowledge of traders’ risk preferences. (Wolfers himself notes that; "We find that prediction markets prices typically provide useful (albeit sometimes biased) estimates of average beliefs about the probability an event occurs.").

Niall O'Connor, in various posting on the website bettingmarket.com, asks the question "when is a prediction market actually predictive."?

Focusing specifically upon prediction markets on the US presidential primaries, he raises the notion that prediction market advocates are often prone to calling a market predictive, when all it has actually done is respond to stale news.

O'Connor has also called for a more balanced appraisal of prediction markets by prediction market advocates, noting that some are quick to highlight the so called predictiveness of the markets, but slow to repsond when the market seems to get it badly wrong.

He also raises the point that researchers and institutions' financial conflicts of interest in prediction market research may raise serious questions about the objectivity of some research in the area. He has also suggested that whilst non-financial conflicts of interest (e.g., career advancement, peer recognition, garnering grants and publications) and financial ones, can motivate individuals to produce a most objective piece of work, they can also servre to compromise judgment and undermine objectivity.

In that prediction markets, are often markets in which the participants may lose money, he argues that those that write about the industry should give full-disclosure as to their connection/s with the prediction market industry. Moreover, he notes that losely throwing around terms such as predictive, may lead ordinary people into believing that prediction markets represent something of a magic bullet, when of course they do not. O'Connor has gone as far as to state that prediction markets merely mirror the polls, and, often there is a significant time lag before they achieve this, due to weak liquidity.

Paul Robin Krugman, a professor of economics and international affairs at Princeton University recently wrote that "the prediction markets — which you see, again and again, touted as having some mystical power to aggregate information, know no more than the conventional wisdom".

An anonymous poster to Krugman's blog raised the following salient point;

"Prediction markets are bunk. What does the market predict? Is there really a 40% chance of an Obama victory? How do we measure their performance? If the prediction markets were accurate we should expect Obama to win the Presidential election 4 times out of ten and Hillary six. There is only going to be one election so what does that mean? This is not just an untestable theory, it is a totally meaningless one. As Polyani observes, probabilistic outcomes are incommensurate. We cannot test the accuracy of the prediction on one off events. In fact we cannot even test their accuracy on aggregations of events. The best we could do is to evaluate the predictive power of the indicator according to the strength of the probability recorded and whether it was correct. On this measure the prediction markets cannot do better than a positive 10% score at present if they get it right, and what the heck does it mean given the previous prediction."

Gilder and Lerman (2007) hypothesised that past/present events can potentially assist in predicting future prices in prediction markets, and, they empirically revealed that prediction markets are surprisingly predictable, even by purely market-historical techniques.

Taking hold of the baton from Gilder and Lerma, Panos Ipeirotis and George Tziralis developed techniques for extracting news flow signals to see whether they can indeed be utilised to predict the future performance of markets on the InTrade prediction exchange. On the question of whether Hillary Clinton will be the Democratic Presidential Nominee in 2008, they noted;

"Our sentiment index (in maroon) is close to 1 when we predict that the market will move higher, and it is close to 0 when we predict that the market will move down. Typically, it works pretty well for predicting long periods of price increases and declines. To put our money where our mouth is, the signal for the last few days shows that Hillary’s market price will edge lower in the next few days/weeks".

Following on from this, thew good folk at bettingmarket.com looked at the Intrade prediction market and the Betfair markets on whether Hillary Clinton will be the Democratic Presidential Nominee in 2008, as of 10.45 GMT on December 3 2007 and 08.45 GMT on December 7 2007. Nothing that Clinton's price did indeed edge lower during this period, they concluded that all of the evidence suggested that both sets of markets were indeed responding to stale news, with Intrade significantly lagging behind Betfair, as regards its ability to aggregate all of the available news flow.

In a recent paper entitled "Learning in Investment Decisions: Evidence from Prediction Markets and Polls" David S. Lee and Enrico Moretti explored how polls and prediction markets interacted in the context of the 2008 U.S. Presidential election. The authors put forward two premises. Firstly, they argue, if almost all of the information that is relevant for predicting electoral outcomes is not captured in polling, then "there is little reason to believe that prediction market prices should co-move with contemporaneous polling". Second, the authors note that "if, at the other extreme there is no useful information beyond what is already summarized by the current polls, then market prices should react to new polling information in a particular way".

Using both a random walk and a simple autoregressive model, Lee and Morettit found that rather than anticipating significant changes in voter sentiment, the market price in the prediction markets appeared to be reacting to the release of the polling information.

To cite this article: Bettingmarket.Com (2009) "The case against Prediction Markets" (Bettingmarket.Com) .