Prediction markets need a measured approach to insider trading

Balbinder Singh Gill, assistant professor of finance at the Stevens Institute of Technology, released a paper on June 2 that uses a formal economic model to assess how strictly prediction market insider trading should be policed. He identifies a paradox: "the same insider trade that improves the accuracy of the price today can reduce the participation that makes the price informative tomorrow." The model showed that prediction market price accuracy is "hump-shaped" in enforcement intensity, with too little enforcement letting insiders crowd out participants, while too much enforcement removes the insider’s genuine informational contribution.

"Tougher enforcement curbs the insider, raising participation, so accuracy is hump-shaped and optimal enforcement is interior, neither laissez-faire nor a ban." Enforcement should vary with the origin of the information.

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