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A comparison of some structural models of private information arrival
Institution:1. Jesse H. Jones School of Business at Rice University, 321 McNair Hall, MS 531, 6100 Main Street, Houston, TX 77005, USA;2. US Securities and Exchange Commission, 100 F St NE, Washington, DC 20549, USA;3. Michael G. Foster School of Business at the University of Washington, 428 PACCAR Hall, Box 353226, Seattle, WA 98195, USA;1. Vanderbilt University. VU Station B, Box 351819, Nashville, TN 37235, USA;2. KAIST College of Business. 85 Hoegiro, Dongdaemun-Gu, Seoul 02455, Republic of Korea;1. CEPR, United Kingdom;2. Institute for Financial Management, University of Bern, Office 214, Engehaldenstrasse 4, Bern CH - 3012, Switzerland;1. University of Toronto, Canada;2. University of Southern California, Marshall School of Business, Los Angeles, CA 90089, United States;3. Harvard Business School, Boston, MA 02163, United States;1. University of Chicago Booth School of Business United States;2. Stanford University Graduate School of Business United States;3. University of North Carolina Kenan-Flagler Business School United States;1. University of Colorado, United States;2. University of Michigan, United States;3. Federal Reserve Bank of Chicago, United States;4. NBER, United States;1. Krannert School of Management, Purdue University, 403W. State Street, West Lafayette, IN 47907, United States;2. Harvard University and NBER, Harvard Business School, Baker Library 359, Boston, MA 02163, United States
Abstract:We show that the PIN and the Duarte and Young (2009) (APIN) models do not match the variability of noise trade in the data and that this limitation has severe implications for how these models identify private information. We examine two alternatives to these models, the Generalized PIN model (GPIN) and the Odders-White and Ready (2008) model (OWR). Our tests indicate that measures of private information based on the OWR and GPIN models are promising alternatives to the APIN’s Adj.PIN and PIN.
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