December 10, 2005

Prediction Market Summit roundup

Prediction Markets By: ams

I have posted my slides from my talk about the Prediction Market summit, so I now feel free to bug others to post theirs. The Prediction Market Summit last week was quite a success. We had quite a variety of speakers. I led off the day by summarizing Zocalo’s current status, and showed a replay of an experimental session. I then pitched the idea that Prediction market operators should make their sites more searchable. This would raise the visibility of prediction markets generally, as well as their specific markets.

I spent the rest of my time explaining a way that Prediction markets based on claims with multiple exclusive outcomes should be presenting better prices to traders. These markets maintain a separate double auction for each outcome, segmenting the available order volume into non-interacting submarkets. Arbitrageurs are not a substitute for the market in offering these trading opportunities, since riskless arbitrage can’t take advantage of interest that never appears in the order book. After a few hallway conversations I think most people understood the argument.

Bo Cowgill talked about Google’s internal markets. They wrote their own software from scratch, following the IEM model. Traders buy a basket of claims in order to sell a claim. Usability and politics were the biggest issues for them. The company contributes money in an account for each Google employee, which they can then trade. Each participant’s account is cashed out at the end of each quarter. They get as many lottery tickets as they had cash from liquidating claims. (i.e. money you don’t invest doesn’t earn anything in the lottery.) Prizes are then awarded to lottery winners; this eliminates the incentive (common in play money markets with prizes for top performers) to take extreme chances in order to boost your odds of being the single top performer. A legal issue they had to contend with in selecting claims to bet on was that information on some outcomes is controlled by the SEC. If employees find out what is happening in some areas, they might become subject to SEC rules for insiders, and their stock trading restricted. The operators of the market chose subjects where that likelihood seemed small.

Bernardo Huberman presented work at HP on techniques for using games to elicit trader’s risk preferences and subject knowledge, and then use nonlinear functions to aggregate their predictions. This approach can address problems due to illiquidity when there are few traders, and prevent manipulation. Bernardo reported that HP is starting to use some internal markets to predict price and availability of various PC components on which the company depends.

Emile Servan-Schreiber reported that the prediction market business climate is improving. NewsFutures has been getting better responses as they sell the concept within businesses. He listed Dentsu, Lilly, Mars, Abbot, Yahoo, and the World Economic Forum as customers who are currently using their software.

Mike Knesevitch talked about TEN’s (Trade Exchange Network, also TradeSports, and InTrade) markets. The fact that they trade in real money allows investors to hedge. Mike said their sports markets are extremely efficient because there are a number of arbitrageurs who know the statistical relations between various outcome, and take advantage whenever prices exceed certain bounds. Since they don’t take a position in any of the trades, Mike said they aren’t subject to the 1961 Wire act, which regulates interstate gambling in the US. It also means they couldn’t get a bookie’s license in the UK if they wanted one. TEN has approval from the CFTC to operate as an exempt Board of Trade. eBOTs can only support trading between certain large firms and qualified investors. My understanding of what Mike said is that TEN intends to operate a separate exchange for these large players, allowing them to hedge positions by trading in prediction markets. One area TEN intends to grow in is weather contracts. Mike said TEN, as one of their criteria for approving a new claim, looks for natural trading partners who would take opposite positions. He pointed out that Ski resorts (whose business falls in a light snow year) have exposure that is opposite to that of big cold weather cities (whose snow removal expenses grow in a heavy snow year.)

Russell Anderson talked about HedgeStreet’s markets. One factoid I picked up is that HedgeStreet gets their piece by charging a percentage of the winning side of contracts. TEN charges a commission to traders who buy at the market price. Orders entered into the book are free.

Eric Zitzewitz presented his paper with Justin Wolfers on Interpreting Prediction Market Prices as Probabilities. This paper is a response to Charles Manski’s earlier paper arguing that prices are more likely to constitute a weighted average of beliefs. Eric argued that a more general model of trader’s preferences and budget leads to a model in which prices are very close to probabilities, and that traders have practical incentives to move the prices closer to probabilities. The paper shows that their model also predicts price curves that match the results seen in actual markets more closely than Manski’s do. One good line that Eric used in explaining the uses for Prediction Markets is that they allow us to study the likelihood of events even when the events never actually take place.

Todd Proebsting has been proselytizing for Prediction Markets inside Microsoft for a couple of years. He set up markets that continue to be used for internal decision making, though not in any formal routine process. The first market they ran quickly showed that a project was in serious schedule trouble, even though the management team had been reassured (by the same employees who participated in the market) that the project was on track. Unfortunately for the people who predicted the schedule problem, the project manager changed the milestones in response to the dire predictions, eliminating the value of the investments based on the completion date. Since then, Todd has been more careful about how questions are phrased. Microsoft’s markets uses a market maker and doesn’t support limit orders because their initial informal survey showed that people were hesitant to leave book orders. Todd recently saw Edward Tufte’s presentation on the evils of PowerPoint, so he spoke without slides which might have been illegal if he tried to do it at Microsoft.

Dave Pennock gave the audience a choice among a few short presentations he had prepared. The audience opted first for his presentation on Search Futures and the Tech Buzz game. The Tech Buzz forecasts got better as deadlines drew nearer. Some traders were able to figure out that the prices in the Dynamic Parimutuel market should be proportional to the square root of the underlying index value (search terms on Yahoo); the best traders pushed prices closer to that level. The second section of the talk covered the paper Pennock wrote with Servan-Schreiber, Wolfers, and Galebach. They analyzed data from real money and play money prediction markets for NFL football and showed that the prices on both were much better predictions than those of most bettors. (NewsFutures and TradeSports came in 6th and 8th out of about 2000 contestants.)

The day ended with a panel that I moderated. I didn’t get a chance to take notes, so all I can say was that it was congenial, and the panelists addressed all the questions.

The format was generous, with plenty of time between sessions for people to talk. The location at UCSF’s Mission Bay campus worked quite well.

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