Do U.S. Financial Regulators Listen to the Public? Testing the Regulatory Process with the RegRank algorithm
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| Description: |
Kirilenko, A (Massachusetts Institute of Technology)
Thursday 18 December 2014, 16:30-17:15 |
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| Created: | 2014-12-22 17:03 |
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| Collection: | Systemic Risk: Mathematical Modelling and Interdisciplinary Approaches |
| Publisher: | Isaac Newton Institute |
| Copyright: | Kirilenko, A |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | 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. |
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