Corporate Prediction Markets
Many people are familiar with the predictive success of small, low-stakes “prediction markets” run by the University of Iowa (predicting political elections better than the pollsters) and firms such as Tradesports.com (predicting everything from whether Hamas will recognize Israel to whether Scooter Libby will be convicted) and the Chicago Board of Trade (predicting U.S. unemployment and other key economic indicators). As colleagues Saul Levmore and Cass Sunstein have pointed out, these markets are routinely better at forecasting what is going to happen than any other available means. The insight here is a Hayekian one: markets, when they work, are the best available mechanism for gathering, aggregating, and processing information.
Those familiar with this literature may also be familiar with the use of these markets by firms such as Hewlett-Packard and Google, which have made increasing use of prediction markets to help make business decisions. Initial studies suggest that these markets provide relatively reliable predictions and are not easily manipulated by those who have a stake in decision making. For example, HP’s market estimated sales of a particular product better than traditional forecasting methods. Other results suggesting the usefulness of simple prediction markets have been seen at Google, Siemens, Intel, and many other firms. More promising still, David Pennock’s patent-pending dynamic pari-mutuel market and Robin Hanson’s market scoring rule make it possible to generate sound predictions even in very thin markets.
In a forthcoming paper, Michael Abramowicz, a law professor at George Washington, and I explore how these markets may help solve a number of corporate law problems.
Conditional prediction markets (e.g.,, what will Firm X’s stock price be in one year if the strategy proposed by a corporate raider is adopted) could allow corporations to estimate the effect of different major decisions, such as whether to acquire a target, whether to adopt a governance reform, or whether to dismiss a CEO, on stock price. If further experimentation shows the technology to be reliable, prediction markets could serve as an alternative or complement to shareholder voting as a means of disciplining corporate boards and managers. Appropriate prediction market design can combat the “rational apathy” that plagues shareholder votes, while market participants would still have incentives to take into account the recommendations of corporate officials, to the extent that those recommendations may reflect information unavailable to market participants. Appropriately designed prediction markets may also serve as a weak substitute for insider trading, producing an alternative avenue for insiders to profit on and thus reveal inside information, without creating an unlevel playing field in the market for a firm’s securities.
To get an idea of where we are going, consider the CEO certification requirements for financial statements imposed by the Sarbanes-Oxley Act, which carry a penalty of up to 25 years in prison. Suppose a CEO, when deciding to sign the document, believes that there is a possibility that certain executives have been shifting sales from one period into another in order to meet sales targets and Wall Street’s expectations about quarterly earnings. The CEO can rely on information to flow to her through the normal hierarchical channels—from line sales persons to sales managers to sales executives to finance and then finally to the COO or CFO—but she may have reason to doubt the veracity or completeness of the information she receives. For one, the individuals in this or any chain of command is subject to their own incentive mix, which may or may not be in accord with that of the CEO or the firm’s owners. Moreover, the incentives have to be perfectly aligned for all links in the chain; any link may be able to distort the information in a way that the other links, even if acting in good faith, cannot know of or be in a position to change. For example, a line sales person may distort the true figures and conceal the deception such that the lie gets reinforced instead of found out, or a senior vice president may be presented with truthful but bad information only to change it at the last minute before it gets to the top. In addition, certain third parties, like the firm’s auditor or lawyers, may know about any misrepresentation but be unable or unwilling to convey it to the CEO because of restrictions on their access to the CEO or because their own (personal or intrafirm) incentives are not perfectly aligned with that of the CEO. To take just one simple example, the partner on the account may not want to tell the CEO the truth told to him by a lower-level auditor because the partner is worried about alienating a key client and thus reducing his annual bonus.
Instead of relying on command-and-control mechanisms or computerized “internal controls,” which are all expensive, unreliable, and subject to abuse, the CEO could create a prediction market in which firm employees, auditors, and anyone else that the CEO wishes could trade on, say, whether the firm will be required to restate its earnings over some period of time. This internal prediction market, even a thin, private, fake-money market, might give the CEO a better prediction about the veracity of the firm’s earnings than any command-and-control model, either firm created or imposed by regulation. For one, it will allow any link in the chain, either at the firm or at its auditor, to convey its objective estimate of the probability of earnings restatement to the CEO through trading behavior. And, as the theory suggests and practice supports, any attempts to manipulate the market, say by the audit partner or the SVP trying to cover up the true picture of the firm’s earnings, would only increase trading profit opportunities for those with accurate, objective trading information. Especially with a market providing more robust financial incentives, the richness and simplicity conveyed to the CEO through the market price, reflecting the trading behavior of all market participants, cannot be equaled through any non-market means. Just as the price of a pencil conveys simply all of the various costs of every element of its design, construction, and delivery—from tree and graphite mine to box on the store shelf—so too here does the price of a prediction market contract tell the CEO more than she could learn from any other sources.
For the foreseeable future, we imagine that corporations will probably create prediction markets solely for informational purposes. But if prediction markets prove sufficiently successful, one might imagine corporations committing to bind themselves to the decisions of particular prediction markets. Suppose, for example, that a corporation is considering building a new plant. It might launch two heavily subsidized conditional markets predicting the corporation’s stock price several months in the future. One conditional market would predict the stock price if the corporation committed to building the plant, and the other, the stock price if the corporation committed to not building the plant. After about a month (the exact time should be randomized to prevent last-minute manipulation), the corporation would check which prediction was higher, and make its decision accordingly. Because such markets should look after shareholders’ interests, implementation should reduce agency costs. Perhaps someday, corporations will allow shareholders to influence corporate policy by sponsoring prediction markets instead of by voting. This approach might be used not only to resolve specific policy issues (perhaps more than shareholders now are permitted to resolve in votes), but also to make personnel changes in management or the Board of Directors.