'Maximal' ban on insider trading would hurt prediction markets, says researcher

A maximal ban on insider trading could harm the accuracy and participation in prediction markets, according to research by Balbinder Singh Gill. Gill, a researcher at the University of British Columbia, stated that the same insider trading that enhances price accuracy in the short term can diminish market participation, thereby reducing the informativeness of prices in the long run. His analysis suggests that a complete prohibition on insider trading, while seemingly beneficial for fairness, might inadvertently lead to less efficient price discovery in these markets. Prediction markets, which allow individuals to bet on the likelihood of future events, rely on diverse information to set accurate prices. Gill's work highlights a potential trade-off between strict regulatory enforcement and the functional efficiency of these specialized financial instruments. The research, presented at the National Bureau of Economic Research (NBER) conference, explores the complex dynamics of information flow and market behavior.