Three academics have developed RegRank, an algorithm that measures how well public feedback is received and incorporated into the rules for Wall Street traders.
RegRank was recently unveiled in a new paper authored by Andrei Kirilenko, the U.S. Commodity Futures Trading Commission's former chief economist; University of Maryland professor Shawn Mankad, and University of Michigan professor George Michailidis. The academics note that what makes the algorithm unique is how it can measure "regulatory sentiment" and test the impact of the public comment process on rule-making. Kirilenko says they realized that all of the legal jargon often used in the comments could be condensed to clusters of "basic words."
RegRank works by mining regulatory text, which can span hundreds of pages, searching for certain clusters of key words that are deemed "pro-regulation" or "anti-regulation." The algorithm keeps track of how often these pro- or anti-regulatory words appear, which enables researchers to compare how a proposed rule evolves into a final one and whether the public's comments were taken into account.
"The results show that the government listens, but more research would be needed for deeper insights," Mankad says.
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Abstracts Copyright © 2014 Information Inc., Bethesda, Maryland, USA
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