Barry Ritzhold of the Big Picture has had a run of posts lately on topics near and dear to my heart. He has written before on the subject of prediction markets, and his latest comment, on a particularly wrongheaded story in the New York Times, makes some important observations about markets in general.
This topic is more important than it might seem. Policy makers are looking to carbon trading to help contain emissions, and various carbon markets have been established and more are being planned. But if trading is fragmented among many markets, or there are many contracts relative to the volume of trading, there will be a lot of inefficient pockets where speculators can take advantage of sporadic and inexperienced counterparties. The Ritzhold piece gives some practical input.
For those who may not be familiar with the concept, prediction markets are exchanges where people buy and sell contracts that reflect the outcome of a particular event. There are sports markets, political markets, economic markets, and so on. One of the best know is the Iowa Electronic Markets. For example, in the last US Congressional election, it had prospectuses for contracts such as “RS.gain06” whose short description was, “Republicans hold more than 55 Senate seats after 2006 election.” Prediction markets enthusiasts claim that the Iowa Electronic Market has been more accurate than polling companies in predicting elections.
There is a widespread belief that these market are a superior means of aggregating and distilling information (really, opinions) on relevant topics. For example, the Pentagon launched a Policy Analysis Market in 2003 and indicated that they might add instruments for terrorist attacks. The resulting hue and cry led them to close down the experiment.
The problem is that if these markets worked as well as people seem to believe they do, every favorite horse would win the race. But it doesn’t work that way. But the Friedmanite orthodoxy assigns a special, almost magical role to markets, when real world market participants like Ritzhold will tell you that markets send complex, sometimes contradictory signals (for example, it isn’t unheard of for junk bonds to trade at high spreads, suggesting the economy is weakening, when the equity market is rising, which would point to improving business conditions).
Ritzhold points out when markets don’t work as their enthusiasts advertise, specifically, when markets are thinly traded (another example is when pricing is opaque. Recall what is was like to be a consumer before you could comparison shop on the Internet).
Every now and again, I read something that simply makes my head spin. This morning’s NYT article on prediction markets was just such a head-spinning column. It reached such a bizarre conclusion that it begs for comment.
Longtime readers are familiar with my views on prediction markets. I believe they have some value, when applied correctly. Where prediction markets excel is in acting as a collective real time polling mechanism for their participants. The closer the collective group is to the broader population, the more accurate these markets tend to be. Where they do poorly is when the collective attempt to “pool their ignorance” and forecast the random or the unknowable.
There is strength in any mechanism that can factor a large pool of participants collective experience and knowledge, discount several possibilities into the most likely outcome. These market mechanisms are hardly the Oracles of Delphi their supporters make them out to be. It is only by din of Humans’ even worse forecasting abilities (with experts leading the way down) that these markets garner so much respectability in the first place. Blame fuzzy thinking for placing too much credibility on a mechanism with so mixed a track record. There is also a subtle but important distinction between forecasting the future versus discounting various outcomes.
0214bizleonhardt_1Which leads me to today’s NYT column. In a bizarre twist of logic, the massive failure of the prediction markets in the US 2006 mid-term elections somehow gets credit for being right. They were not a day late and a dollar short, they were completely, totally and incontrovertibly wrong.
Except in the NYTimes.
Consider the following: As of 11:50 p.m. on the evening of Election day, with the voting completed (except for Hawaii) and the majority of the ballots counted, Intrade gave the Republicans an 85 percent chance of retaining the Senate. Of course, we know that’s not at all what happened. If that’s not a forecasting failure, then what is?
If forecasting the results of an election held yesterday is considered prescience, then sign me up — and find me a bookie who will take my Superbowl bets on the Monday after the game. I suspect I will at least cover the spread.
To reiterate my views, these markets — thinly traded, easily manipulated, poorly administered — do have some real value. However, they have hardly been “remarkably clairvoyant,” as the Times describes them. Consider these recent acts of “clairvoyance:”
GOP Retention of Senate
Michael Jackson Trial Results
Morgan Stanley CEO Purcell resignation
Howard Dean’s Iowa Primary
Election day trading frenzy
There are some interesting explanations for some of these errors: Pooling the collective ignorance of millions does not produce Wisdom. The Michael Jackson Trial and Morgan Stanley’s CEO are perfect examples of that.
As to the prediction market’s failure of the Howard Dean primary and the 2006 mid-term elections, I’ve been toying with a theory. Since the political affiliation of Wall Street tends toward Republican, and since there is a big overlap between Wall Street and the participants of the prediction markets, the collective had an inherent bias built into it. Remember, where these markets excel is when they act as a realtime polling mechanism of their particpants. If the pool has a bias, the outcome may very well too. Hence, these widescale failures.
Bottom line: While having value, prediction markets are subject to error and bad outcomes. Some of this is due to their relative thinness of the markets; some is due to the inherent biases of the participants, and their failure to parallel the population at large.
They should be considered with the recognition of their fallibility.