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New Hampshire – The Democrats

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The Hillary Clinton event derivative was expired to 100.

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New Hampshire – The Republicans

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The John McCain event derivative was expired to 100.

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- How far in advance can prediction markets make accurate predictions?
- How will we know the point in time when a prediction is ‘accurate’?
- Why are there wildly different predictions of the same outcome in different prediction markets?
- How do we know which market is accurate?
- Is it a matter of prediction market efficiency?

The real benefit, in my opinion, is that prediction markets provide a better measurement of uncertainty around the outcome than do traditional forecasting methods.

He was talking about the InTrade market on Health Care Reform. In theory, the market price in such a derivative market should equal the expectation of the underlying event coming true. However, Paul Krugman (and many others) forgot one of the most basic assumptions of the market model! Transaction costs.

When the market price is over 95, InTrade charges a transaction fee of 3 cents per contract (real money). While market prices are quoted in percentages, the payoff for a winning ticket is $10 (real money). Therefore, the transaction fee is 0.3% of the winning payoff. In addition, InTrade charges 10 cents per contract on expiry (if you “win”). That’s another 1.0%.

So, when the market was quoting 97.8% likelihood of the HCR bill passing before June 2010, this didn’t really mean that there was a 2.2% chance of the bill not passing. A winning ticket would be subject to 1.3% transaction fees. The real likelihood of failure was 0.9% – approximating the uncertainty that Obama would be “hit by a bus” before signing the bill into law.

No rational investor would wish to purchase a share for more than 98.7, given the transaction costs. In a sense, this is the market’s “100%”. Interestingly, at 1:49pm GMT today (March 23), there are 695 bids at 99.1 and 413 asks at 99.2. Clearly, some traders are not subject to the full transaction fees at InTrade. More about that here.

[Cross-posted from Toronto Prediction Market Blog.]

1. Which InTrade prediction market(s) has/have been ahead of the Press (rather than the other way around)? What is/are the best (most divergent from the commentary, and correct) InTrade prediction market(s) in people’s memories?
2. Do you sense that the ObamaCare prediction market at InTrade fits these 2 criteria?

UPDATE: I asked The Brain whether he meant generalist media or political media, and he meant “generalist”. That makes all the difference in the world.

ADDENDUM

More info on health care reform on Memeorandum.

Previously: Insider trading in the InTrade prediction market on health care reform?

Robin Hanson is Chief Scientist at Consensus Point. I like his ideas about combinatorial markets and market scoring rules, but I think he over-sells the accuracy and usefulness of prediction markets. His concept of Futarchy is an extreme example of this. Robin loves to cite HP’s prediction markets in his presentations. Emile Servan-Schreiber (Newsfutures) is mostly level-headed but still a big fan of prediction markets. Crowdcast’s Chief Scientist is Leslie Fine; their Board of Advisors includes Justin Wolfers and Andrew McAfee. Leslie seems to have a more practical understanding than most, as evidenced by this response to the types of questions that Crowdcast’s prediction markets can answer well: “Questions whose outcomes will be knowable in three months to a year and where there is very dispersed knowledge in your organization tend to do well.” She gets it that prediction markets aren’t all things to all people.

An Honest Paper

To some extent, all of the researchers over-sell the accuracy and the range of useful questions that may be answered by prediction markets. So, it is refreshing to find an honest article written about the accuracy of prediction markets. Not too long ago, Sharad Goel, Daniel M. Reeves, Duncan J. Watts, David M. Pennock published Prediction Without Markets. They compared prediction markets with alternative forecasting methods for three types of public prediction markets: Football and baseball games and movie box office receipts.

They found that prediction markets were just slightly more accurate than alternative methods of forecasting. As an added bonus, these researchers considered the issue that prediction market accuracy should be judged by its effect on decision-making. So few researchers have done this! A very small improvement in accuracy is not considered material (significant), if it doesn’t change the decision that is made with the forecast. It’s a well-established concept in public auditing, when deciding whether an error is significant and requires correction. I have discussed this concept before.

While they acknowledge that prediction markets may have a distinct advantage over other forecasting methods, in that they can be updated much more quickly and at little additional cost, they rightly suggest that most business applications have little need for instantaneously updated forecasts. Overall, they conclude that “simple methods of aggregating individual forecasts often work reasonably well relative to more complex combinations (of methods).”

For Extra Credit

When we compare things, it is usually so that we can select the best option. In the case of prediction markets it is not a safe assumption that the choices are mutually exclusive. Especially in enterprise applications, prediction markets are heavily dependent on the alternative information aggregation methods as a primary source of market information. Of course, there are other sources of information and the markets are expected to minimize bias to generate more accurate predictions.

In the infamous HP prediction markets, the forecasts were eerily close to the company’s internal forecasts. It wasn’t difficult to see why. The same people were involved with both predictions! The General Mills prediction markets showed similar correlations, even when only some of the participants were common to both methods. The implication of these cases is that you cannot replace the existing forecasting system with a prediction market and expect the results to be as accurate. The two (or more) methods work together.

Not only do most researchers (Pennock et al, excepted) recommend adoption of prediction markets, based on insignificant improvements in accuracy, they fail to consider the effect (or lack thereof) on decision-making in their cost/benefit analysis. Even if some do the cost/benefit math, they don’t do it right.

Where a prediction market is dependent on other forecasting methods, the marginal cost is the total cost of running the market. There is no credit for eliminating the cost of alternative forecasting methods. The marginal benefit is that expected by choosing a different course of action than the one that would have been taken based on a less accurate prediction. That is, a slight improvement in prediction accuracy that does not change the course of action has no marginal benefit.

Using this approach, a prediction market that is only “slightly” more accurate, than those from alternative forecasting approaches, is just not good enough. So far, there is little, if any, evidence that prediction markets are anything more than “slightly” better than existing methods. Still, most of our respected researchers continue to tout prediction markets. Even a technology guru like Andrew McAfee doesn’t get it , in this little PR piece he wrote, shortly after joining Crowdcast’s Board of Advisors.

Is it a big snow job or just wishful thinking?

[Cross-posted from Toronto Prediction Market Blog]

Don’t be fooled by any “research scientist”.

You are giving yourself WAY too much credit. Siskel and Ebert successfully predict these awards 100% year after year. This isn’t a difficult thing to predict. Predicting something like the NCAA tourney, that would be an accomplishment, but if you look at rankings and your prediction market, you will fail just as much as the average bracket.
— Jason

- More at the Next Big Future — (an excellent blog)

UPDATE

Another tried-and-true method is to repeat a statement over-and-over until it becomes a “fact”. The best example of this, regarding prediction markets, is the claim that they are “proven” by virtue of HP’s remarkably lop-sided, unmistakable, trouncing of internal forecasts by a score of 6 – 2. It is usually cited as “6 out of 8″ or 75% better than the official forecasts. The “truth” is that the “wins” were by minuscule margins and many of the forecasts were equally bad. [...]

Read the rest, here.

External Link: Paul Hewitt’s blog on prediction markets