Art Hutchinson pointed me to his blog, Mapping Strategy, and the collection of articles he’s been writing about prediction markets since last September. Here are my notes:”The strong consensus – supported by a compelling body of academic research – is that these mechanisms deliver uncannily accurate forecasts across a wide range of topics, time horizons, and approaches to participation. Even more interesting is that they appear to do so at a fraction of the cost of conventional techniques for generating business foresight, (e.g., trend extrapolation, market research, polls, expert opinion and even sophisticated models and simulations).”

“They need not be perfect in order to be compelling. Compared to any pragmatic forecasting alternative, prediction markets remain remarkably resilient to manipulation, and uniquely (if not perfectly) efficient at assessing the impact and importance of vast amounts distributed information.”

Examples:

• The Iowa Electronic Markets (IEM). IEM traders with real money at stake called the presidential race for Bush in September.
  “This is the fifth straight presidential election that the Iowa Electronic Markets has called correctly. Other markets did as well or better – including forecasting the tightness of the race.What I find even more fascinating is that each candidate took every single one of the states in which he was running over 50% likelihood according to a smoothing formula I applied to pricing data gleaned from Tradesports in the final days. Every one. As I watched it play out through the night, it felt like I’d been let in on prophecy.“
• The Des Moines Register carried this story two weeks ago, describing how the Iowa Electronic Markets are involving physicians in markets to predict the location, timing, and severity of flu outbreaks this Winter. …It was 90 percent accurate during a short pilot project last year

Following are from an article in the Australian paper, The Age

• The Hollywood Stock Exchange, an affiliate of online trading firm Cantor Index Ltd, allows people to buy and sell virtual shares in movies, celebrities and music. To pay for pseudo-shares, they use pseudo-money in the form of “Hollywood Dollars”. This allows people to bet on such questions as total box office returns and Oscar winners. Because the data outperforms industry forecasts, it is also syndicated as market research.
• Three years ago, Goldman Sachs and Deutsche Bank launched a market for economic statistics futures including employment, industrial output, retail sales and inflation. The Chicago Mercantile Exchange now trades in inflation futures contracts.
• Some companies have also experimented with prediction markets. Hewlett-Packard, for example, set one up that reportedly generated more accurate forecasts of sales than its own internal processes. Siemens had one that predicted the German conglomerate would fail to deliver on a software project in time, in defiance of its established management systems that insisted the deadline would be met. Management was wrong.

Prediction markets for business

• “By accepting the superiority of managed, credential-based, hierarchical information flows across the board, organizations are handicapping themselves in evaluating early signals on some of the most important open ended strategic questions they need to confront. And while such mechanisms are powerful and necessary for many kinds of tasks, they’re poorly suited to the task of harvesting, assimilating, and assessing distributed intelligence in the near real-time. I.e., on questions with high uncertainty and little or no past precedent, it’s only smart business to acknowledge the possibility that the best answers may sometimes arrive from truly unexpected sources.“
• “…where I think prediction markets will find traction in a corporate context is in highlighting where management teams ought to pay more attention (i.e., do more research), and in making groupthink denials of emerging external trends and inflection points far more difficult to sustain. This will be particularly true of prediction markets that are populated across corporate boundaries -e.g., including customers.“

Comparing a prediction market to the stock market: the importance of making contracts specific
“I suspect that other hypothetical contracts, written so as to avoid clear moral hazard issues could have (and still might) shed strategically important light on how the market for over-the-counter pain medications will evolve. For example, a winner-takes-all contract for 2006 stating that: “Two companies hold 90% share in Cox-2 therapies”, might have signaled much earlier that this was a market bound to consolidate. The reasons could have had nothing to do with the safety of any particular drug (e.g., difficulty in establishing a compelling third brand, patient-perceived efficacy, mergers and acquisitions, etc.) What keeps the idea of trading in such specific contracts interesting vs. simply watching the share price of major pharmaceutical companies on Wall Street is that…such markets can highlight the potential for specific change events with much greater precision.”

Critical success factors

• Select the right kinds of (non-random) questions
• recruit/attract a solid pool of informed/active tradesr
• Only rely upon the predictions of markets that show robust and honest trading activity
• Limit the stakes so as not to create moral hazard. There’s plenty of evidence to suggest that web games and carefully constrained real money markets are as or more effective than high stakes wagers at predicting the future outcome of uncertain but non-random events
• Make it easy to figure out what’s going on
• Be clear about trading parameters, e.g., market open and close
• Make it obvious how trading values translate into probabilities or vote shares
• Make it easy to compare the prices of the underlying commodities

Problems with Prediction Markets

• Skewed participation
• Poor question articulation
• Inadequate information input
• Thin floats and big spreads.
• Can prediction markets be deliberately influenced?
  “…prediction markets are proving as resilient to deliberate influence as theorists have long said they would be.” Example: The Bush relection futures at Tradesports survived a speculative attack: “‘There is now no question whatsoever that the Bush re-election futures contract at Tradesports.com is being manipulated. Yesterday the price of the futures were sold down from about 55 (indicating the market’s estimate of a 55% probability of Bush’s re-election) to 10 (indicating on a 10% probability) with a single 10,000-lot order entered by a single trader. An order that size represents twice the normal volume of an entire typical day’s trading’….The ‘price’ of a Bush future quickly rebounded to the mid 50’s.“

Robin Hanson on “Manipulators increase information market accuracy“.“Why does… manipulation seem to be less of a problem than many fear it should be? One possible explanation is the view that a manipulative trader is in essence a ‘noise’ trader in the sense that his trades are based on considerations other than his best estimate of asset value… when potentially informed traders have deep pockets relative to the volume of noise trading, increases in trading noise do not directly effect price accuracy… by inducing more traders to become better informed, an increase in noise trading indirectly improves the accuracy of market prices.” (emphasis added)

How prediction markets can separate the wheat from the chaff
“…let prediction markets directly incent information gathering and sharing, highlighting those individuals with the best grip on reality in particular areas, i.e., those with knowledge that’s objectively valuable to the enterprise. Mary Murphy-Hoye of Intel (a pioneer in using prediction markets) made this point directly in a Time Magazine feature article last summer entitled “The End of Management”:

‘I can now tell if planners are any good, because they’re making money or they’re not making money.’

She highlights a little talked-about, but important flip side to the whole debate: shouldn’t good knowledge management discipline marginalize those who obfuscate, dilute or detract from institutional knowledge-building to the same degree that it elevates (and enriches) those who add to it?“