BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Text Selection
DTSTART:20170511T120000
DTEND:20170511T130000
DTSTAMP:20260410T152249Z
UID:fbf3f90b49ba4246483ee197b2224207296927f3e4617edcbc630bfd
CATEGORIES:Conferences - Seminars
DESCRIPTION:Asaf MANELA (Washington University in St. Louis)\nText data is
  inherently high-dimensional\, which makes machine learning regularization
  techniques natural tools for its analysis. Text is often selected by jour
 nalists\, speechwriters\, and others who cater to an audience with limited
  attention. We show that incorporating structural economic restrictions in
 to machine learning methods can improve out-of-sample prediction and provi
 de causal interpretations. Our highly scalable approach to modeling covera
 ge selection is especially useful in cases where the cover/no-cover choice
  is separate or more interesting than the coverage quantity choice. We app
 ly this framework to option-implied volatility (VIX) prediction using news
 paper coverage\, and find that it substantially improves out-of-sample fit
  relative to alternative state-of-the-art approaches.
LOCATION:UNIL\, Extranef\, room 118 https://planete.unil.ch/plan/?local=EX
 T-118.1
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR