The point of a market is to have predictive power. Although the play-money markets in that instance were, on average, closer to the final outcome, NF was also orders of magnitude more volatile.

What does a “probability” of 50% mean if it could well be at 70 percent a few days from now, on the basis of a couple of pretty insignificant marginal data, as in NF’s case? You could make an argument that play money markets react more quickly to marginal data, but they also grossly overreacted to it.

Also, to second Jason again, the NFL faceoff between Intrade and NewsFutures concerned information already aggregated. Everyone was trading on the same data; the choice of subject matter was inherently flawed.

As far as I can tell, the play-money markets are much closer to “conventional wisdom,” which vastly overestimates current information relative to historical information, and fluctuates way too much.

If play-money markets sometimes reveal information faster than real-money markets, I don’t think they do so on average, thus making it hard to use that fact. Real-money markets should be closer to risk neutral than play money markets so i would guess they will do better on average.

The Cowgill lottery system helps play money markets. It might also be feasible to calculate the optimal risk preference based on the prize structure and use that value to back-out risk neutral probabilities.

Although I am not a fan of self-arbitraging mechanisms. Those can only arbitrage based on prior information, not current information. During short time intervals of high volatility, when incoming information has more weight than historical information, auto-arbitraging mechanisms are begging to be exploited.

I have also read the SSRNs that argued in favor of play money markets. I remember we had a pretty good debate here several months ago on the very subject, and it ended with a big fat “Unresolved.”

Michael,

No, I adamantly disagree that liquidity should be artificially “facilitated.” There are certainly circumstances in which an exchange might want to temporarily boost liquidity, if they think they have a good opportunity to increase long run trader population… but… Liquidity of a security, in the long run has a lot to do with information awareness relative to other securities. I.e., if traders are staying away from it, that means they are finding easier alpha elsewhere, and the alpha in that particular security, relative to the information discovery/aggregation cost, simply is not worth it to them. If they don’t have a significant alpha edge on the security in question, they shouldn’t trade it.

That was a very crappy explanation. But liquidity generally shouldn’t be subsidized. Just as inflation isn’t good for the market just because it promotes more trading. There is more marginal trading/liquidity, but that is significantly outweighed by inflation’s mispricing effect. (I wish I had more time to give a more thoughtful reply to your question.)

Cheers,
Alex

Liquidity not backed by information may result in prices less useful than having no price. Perhaps confidence bands on prices can be established based on the number of real traders participating in a market.

Implementing auto-arbitrage makes sense although some sites opt against it in order to encourage more participation (but the arbitrage may not be realized if there are many markets, if the arb opportunities swamp real-trader liquidity).

I’m not sure what counts as an “artificial” liquidity booster (or the complementary set - the “natural” liquidity booster), but it seems to me that it is well worth exploring market designs that facilitate liquidity. I know that Ledyard and folks at Caltech have investigated this question in the lab (though in other contexts, not with Hanson’s MSR).

Wouldn’t it make sense that improving liquidity would give the underlying prices/predictions more validity, not less? Poor liquidity just results in some information not coming into the market.

With respect to your remark about “stupidly mispriced offers,” I think one way to frame the story of financial innovation in the 1980s-1990s and continuing today is as a search for mispriced opportunities to provide liquidity to the market (see Richard Bookstaber’s recent book for example). Some of the quants on Wall Street made their money in pure arbitrage (in real money exchanges like the NYSE) and others made money by finding lower-risk ways to provide liquidity to the market.

It may be true that a self arbitraging real money exchange based upon automated market makers can be made to work as a money pump. I think this could be because the arbitrage is not perfect across all dimensions in multi outcome markets - the MSR eliminates the kind of gross arbitrage described in the post by ensuring prices in the market sum to the payoff, but there is no guarantee that the MSR will allocate the price adjustments needed in a way the reflects all implicit in the information that motivated the initial transaction. There may in fact be some residual risk in the way the arbitrage is implemented by Inkling (albeit they are “risking” play money), or maybe the risk is inherent in the AMM design.

But I absolutely disagree that self arbitraging real money markets are a bad idea in general. When the market design can facilitate truly riskless exchanges, doing so enhances the efficiency of the market.

Thank you for coming here, on Midas Oracle, where we like to have a variety of authors and topics. And that includes Barry Ritholtz, who is skeptical about *real-money* prediction markets.
http://www.midasoracle.org/200.....-failures/

The point wasn’t to prove that I could, you know, probably beat a 7-year old at a math test. Perhaps you’re reacting to the somewhat didactic tone. The piece was written with a non-specialist audience in mind (e.g., regular KP readers and Inkling users), but Chris Masse invited me to cross post it here.

Alex, I didn’t make any claims about the predictive power of play money markets. You may or may not be right - I haven’t collected any evidence and can’t give you an informed response.

From memory, the MSR prediction markets need 12 traders —see Jed Christiansen’s paper.