Can prediction markets help improve economic forecasts?

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At VOX, David Hendry and James Reade examine the question, “-How should we make economic forecasts?”- Among the ideas discussed is whether prediction markets could be used to improve economic forecasting. Interesting suggestion and seeming to be worthy of additional exploration, but the authors don’-t go too deep here.  Instead, they assert that “-prediction markets can be viewed as a form of …- model averaging,”- and then drift into a discussion of forecast averaging. I’-m not sure that forecast averaging is a good way to look at prediction markets.

Here is what they say:

Prediction markets can be viewed as a form of forecast pooling or model averaging, a common forecast technique (Bates and Granger 1969, Hoeting et al 1999 and Stock and Watson 2004). That is, forecasts from different models are combined to produce a single forecast. In prediction markets, each market participant makes a forecast based on his or her own forecasting model, and the market price is some function of each of these individual forecasts.

Since the “-prediction”- implied by a prediction market is set by the marginal transaction, it depends not at all on the distribution of earlier trades, nor on the valuations of parties priced out of the market at the current price.

For example, consider two event markets: in the first 999 contracts trade at $0.50 and the 1000th and final trade is at $0.75- in the second 999 contracts trade at $0.76 and the 1000th and final trade is at $0.75.  In the typical interpretation of prediction markets, the event is “-predicted”- to result with a 75 percent probability in both cases.  However, averaging among the different predictions doesn’-t get you that result.

(Well, strictly speaking the market price is “-some function”- of the prices –- namely, one in which all trades but the last are weighted zero and the last trade is weighted one. You can call this “-averaging,”- but that isn’-t the most useful explanation of the function.)

I’-m not arguing that forecast averaging might not be a good idea in many situations, just that averaging doesn’-t seem like a good way to explain what a prediction market is doing.


Prediction Market Definition -now updated with the name of Chris Hibbert and Eric Cramptons cult leader built into.

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Prediction markets produce dynamic, objective probabilistic predictions on the outcomes of future events by aggregating disparate pieces of information that the traders bring when they agree on prices. These event derivative traders feed on the primary indicators —-i.e., the primary sources of information. (Garbage in, garbage out…- Intelligence in, intelligence out…-) Hence, prediction markets are meta forecasting tools.

Each prediction exchange organizes its own set of real-money and/or play-money markets, using either a CDA or a MSR mechanism.

A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election. The price of this event derivative can be interpreted as the objective probability of the future outcome (i.e., its most statistically accurate forecast). A 60% probability means that, in a series of events each with a 60% probability, then 60 times out of 100, the favored outcome will occur- and 40 times out of 100, the unfavored outcome will occur.

The value of a set of prediction markets consists in the added accuracy that these prediction markets provide relative to the other forecasting mechanisms, times the value of accuracy in improved decisions, minus the cost of maintaining these prediction markets, relative to the cost of the other forecasting mechanisms. According to Robin Hanson, a highly accurate prediction market has little value if some other forecasting mechanism(s) can provide similar accuracy at a lower cost, or if very few substantial decisions are influenced by accurate forecasts on its topic.