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You are looking at 1 - 2 of 2 items for :

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Cover IMF Working Papers
The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning
Mizuho Kida and Simon Paetzold
The Financial Action Task Force’s gray list publicly identifies countries with strategic deficiencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the financing of terrorism). How much gray-listing affects a country’s capital flows is of interest to policy makers, investors, and the Fund. This paper estimates the magnitude of the effect using an inferential machine learning technique. It finds that gray-listing results in a large and statistically significant reduction in capital inflows.
Keywords:
capital flows; AML/CFT; gray list; machine learning; emerging market economies; inferential machine learning technique; gray-listing affect; analysis using machine learning; coefficient estimate; Capital flows; Capital inflows; Anti-money laundering and combating the financing of terrorism (AML/CFT); Machine learning; Foreign direct investment; Global
Cover IMF Working Papers
Front Matter
Mizuho Kida and Simon Paetzold

Copyright Page IMF Working Paper Finance Department The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning Prepared by Mizuho Kida and Simon Paetzold 1 Authorized for distribution by Olaf Unteroberdoerster May 2021 The IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate . The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. Abstract

Keywords:
gray list; Anti-money laundering and combating the financing of terrorism (AML/CFT)