Structuring your market
March 11th, 2007When creating a new prediction market, or series of prediction markets, the creator must consider the structure of how forecasts are made. The structure used can affect both the accuracy of the prediction, potential financial losses for the market institution, as well as how quickly the market will react to new information.
I will broadly generalise the existing structures into three categories: CDA’s, Algorithms, and (for lack of a better term) Others. This categorisation is not based on the mathematics of the structures, but how they work when implemented.
(Origins of these thoughts were started at this post on Midas Oracle.)
CDA’s
These are the classic prediction markets, and are derived directly from how financial markets operate. Orders are placed to purchase a futures contract (bid) and other orders are placed offering to sell futures contracts (asks). When these match, and exchange is made. Exchanges operating like this are the traditional CME/CBOT, the Iowa Electronic Markets and InTrade/TradeSports.
The reason these are used for real-money prediction markets is because they offer no risk to the market institution. All trades are between individuals on the exchange; the exchange itself doesn’t offer liquidity as a market-maker. The drawback to this system is that in order for the market to work, there must be sufficient liquidity. If just a few people are trading, they simply may not come to agreement on a price and thus exchange contracts. Without liquidity, the market will just languish and be unsuccessful.
There are two particularly great benefits to a CDA market, however. One is that prices can make instantaneous jumps. If a presidential candidate makes a huge gaffe, a contract that represents their election chances can dive from 60 to 20 in one trade, something that is difficult to achieve with an algorithm. The second is that traders specify their opinions exactly, by placing bids or asks at specific prices.
Algorithms
For the purposes of this post, I classify both Robin Hanson’s MSR (Market Scoring Rules) and David Pennock’s DPM (Dynamic Pari-Mutuel Market) in the same category. In both structures, traders purchase shares in an outcome, and with each share purchased the price of the contract changes. (The magnitude of the price change depends on the number of contracts already purchased.)
There are drawbacks to algorithms, however, and they mirror the advantages of CDA’s. Firstly, it takes them more time and trading to respond to instantaneous price jumps. In thick markets, this largely doesn’t matter. But in thinner markets with less volume (such as many corporate markets), this could certainly become an issue. A second drawback is that to make the algorithm work it may take some financial subsidisation. In a play-money market this really shouldn’t matter, and specifically applies only to the MSR structure, which is currently a bit more common than the DPM structure. Finally, while algorithms deal nicely with contracts that range from 0-100, they can be difficult to use in other ranges because of how the prices are set to move with each share purchase.
The huge advantage to Algorithms is that they offer infinite liquidity to traders. This can be a very important factor for new exchanges, particularly as new users want to get up and running immediately. Trading immediately, and seeing the effect of a price change, can be a very valuable tool in order to get prediction markets adopted in an organisation.
Others
NewsFutures has innovated other types of structures that can be considered prediction markets, but are structured quite differently. The most prominent example of these can be seen in their Global Risks Prediction Market, run for the World Economic Forum. This is their Competitive Forecasting structure. Traders earn points based on how early they make their predictions, the spread of their predictions and if the bounds of their prediction contained the final answer. This kind of solution works for companies that have specific concerns about the standard trading mechanisms, such as the time (from work) that users take to trade on the market, the desire for more data on trader’s opinions, etc. More can be read on the site regarding the specifics of how it works.
Summary
This is just a summary of the variety of structures that a prediction market can utilise. Each solution has it’s advantages and drawbacks, and should be considered within the context of how it will be used within the organisation, and the organisational needs. I will be posting more in-depth descriptions and analysis of each of these in the future, and look forward to your comments.
