Do U.S. Financial Regulators Listen to the Public? Testing the Regulatory Process with the RegRank algorithm

Add to your list(s) Download to your calendar using vCal

• Kirilenko, A (Massachusetts Institute of Technology)
• Thursday 18 December 2014, 16:30-17:15
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact Mustapha Amrani.

Systemic Risk: Mathematical Modelling and Interdisciplinary Approaches

Co-authors: Shawn Mankad (University of Maryland), George Michailidis (University of Michigan)

We examine the notice-and-comment process and its impact on influencing regulatory decisions by analyzing the text of public rule-making documents of the Commodity Futures Trading Commission (CFTC) and associated comments. For this task, we develop a data mining framework and an algorithm called RegRank, which learns the thematic structure of regulatory rules and public comments and then assigns tone weights to each theme to come up with an aggregate score for each document. Based on these scores we test the hypothesis that the CFTC adjusts the final rule issued in the direction of tone expressed in public comments. Our findings strongly support this hypothesis and further suggest that this mostly occurs in response to comments from the regulated financial industry. We posit that the RegRank algorithm and related text mining methods have the potential to empower the public to test whether it has been given the “due process” and hence keep government agencies in chec k.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Featured lists
• INI info aggregator
• Interested Talks
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.