Prediction Markets for Corporate Governance
George Washington University Law School
M. Todd Henderson
University of Chicago - Law School
Notre Dame Law Review, Vol. 82, No. 4, 2007
U Chicago Law & Economics, Olin Working Paper No. 307
GWU Law School Public Law Research Paper No. 221
GWU Legal Studies Research Paper No. 221
Building on the success of prediction markets at forecasting political elections and other matters of public interest, firms have made increasing use of prediction markets to help make business decisions. This Article explores the implications of prediction markets for corporate governance. Prediction markets can increase the flow of information, encourage truth telling by internal and external firm monitors, and create incentives for agents to act in the interest of their principals. The markets can thus serve as potentially efficient alternatives to other approaches to providing information, such as the Sarbanes-Oxley Act's internal controls provisions. Prediction markets can also produce an avenue for insiders to profit on and thus reveal inside information while maintaining a level playing field in the market for a firm's securities. This creates a harmless way around existing insider trading laws, undercutting the argument for the repeal of these laws. In addition, prediction markets can reduce agency costs by providing direct assessments of corporate policies, thus serving as an alternative or complement to shareholder voting as a means of disciplining corporate boards and managers. Prediction markets may thus be particularly useful for issues where agency costs are greatest, such as executive compensation. Deployment of these markets, whether voluntarily or perhaps someday as a result of legal mandates, could improve alignment between shareholders and managers on these issues better than other proposed reforms. These markets might also displace the business judgment rule because they can furnish contemporaneous and relatively objective benchmarks for courts to evaluate business decisions.
Number of Pages in PDF File: 55Accepted Paper Series
Date posted: September 8, 2006 ; Last revised: October 8, 2007
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