'Maximal' ban on insider trading would hurt prediction markets, says researcher
“The same insider trade that improves the accuracy of the price today can reduce the participation that makes the price informative tomorrow,” said Balbinder Singh Gill.
Prediction market regulators should consider a measured approach to insider trading enforcement as opposed to an outright ban, according to research from an academic at the Stevens Institute of Technology.
In a paper released on June 2, assistant professor of finance Balbinder Singh Gill developed a formal economic model to answer the question of how strictly insider trading in prediction markets should be policed.
A paradox exists in that “the same insider trade that improves the accuracy of the price today can reduce the participation that makes the price informative tomorrow,” he said.
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