**Bo Cowgill and Midas Oracle are the only media to have published about the Lee–-Moretti paper.** We are awaiting insightful takes from the following prediction market bloggers:

– Freakonomics @ New York Times

– Overcoming Bias – (”-the future of humanity”-)

– Odd Head

– Computational Complexity

– Caveat Bettor

– Mike Linksvayer Blog

– NewsFutures Blog

– Inkling Markets Blog

– Consensus Point Blog

– Xpree Blog

– George Tziralis Blog

– Chris Hibbert Blog

– Jason Ruspini Blog

– John Delaney Blog

– James Surowiecki Blog @ New Yorker

– Felix Salmon @ Portfolio – Market Movers

– Zubin Jelveh @ Portfolio – Odd Numbers

**If you are a reader of one of the blogs listed above, do e-mail their owners to demand that they feature a piece on the Lee–-Moretti paper.**

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**Learning in Investment Decisions: Evidence from Prediction Markets and Polls** – (PDF file) – David S. Lee and Enrico Moretti – 2008-12-XX

In this paper, we explore how polls and prediction markets interact in the context of the 2008 U.S. Presidential election. We begin by presenting some evidence on the relative predictive power of polls and prediction markers. 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. 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, we find that the latter view appears more consistent with the data. **Rather than anticipating significant changes in voter sentiment, the market price appears to be reacting to the release of the polling information.**

We then outline and test a more formal model of investor learning. In the model, investors have a prior on the probability of victory of each candidate, and in each period they update this probability after receiving a noisy signal in the form of a poll. This Bayesian model indicates that the market price should be a function of the prior and each of the available signals, with weights reflecting their relative precision. It also indicates that more precise polls (i.e. polls with larger sample size) and earlier polls should have more effect on market prices, everything else constant. The empirical evidence is generally, although not completely, supportive of the predictions of the Bayesian model.

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Let’s not conflate the different questions here.

Can poll analysis do better than markets? Yes, sometimes.

Can markets work well where (timely) polls and other rich data are lacking? Yes, sometimes.

Do markets have utility beyond forecasting? Yes.

Should markets incorporate poll data and poll analysis? Of course, now let’s consider Lee/Moretti paper. If you think this paper says anything deep, you are just being fooled by academic signals and terms like “autoregressive”, “exogenous” and “Bayesian updating”. Markets should incorporate poll data and all this paper says is that, yes, markets smooth noisy poll data. (It doesn’t mention that this also reduces error). The paper doesn’t actually go anywhere interesting with the math. The idea that earlier polls should have more impact on one’s outlook as this model predicts is absurd, and doesn’t hold as the authors conclude. The paper is underdeveloped and based on a single event.

The Lee–Moretti paper leads me to think that the social utility of the prediction markets will not emerge if we focus on comparing them with polls.

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(I agree with many of your points.)

hear-hear.

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You’re getting smarter every day Chris.

There’s too much focus on politics/polls anyway. Why ?

Don’t answer that, I already know the answer.