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DTSTART:20190310T070000
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DTSTART;TZID=America/Toronto:20191011T103000
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/distinguis
 hed-lecture-paul-glasserman-columbia-university
LOCATION:MC - Mathematics & Computer Building 200 University Avenue West Ro
 om: 4020 Waterloo ON N2L 3G1 Canada
SUMMARY:Distinguished lecture by Paul Glasserman\, Columbia University
CLASS:PUBLIC
DESCRIPTION:PRECISION FACTOR INVESTING: AVOIDING FACTOR TRAPS BY PREDICTING
 \nHETEROGENEOUS EFFECTS OF FIRM CHARACTERISTICS\n\n-----------------------
 --\n\nWe apply ideas from causal inference and machine learning to estimat
 e\nthe sensitivity of future stock returns to observable characteristics\n
 like size\, value\, and momentum. By analogy with the informal notion of\n
 a \"value trap\,\" we distinguish \"characteristic traps\" (stocks with\nw
 eak sensitivity) from \"characteristic responders\" (those with strong\nse
 nsitivity). We classify stocks by interpreting these distinctions as\nhete
 rogeneous treatment effects (HTE)\, with characteristics\ninterpreted as t
 reatments and future returns interpreted as responses.\nThe classification
  exploits a large set of stock features and recent\nwork applying machine 
 learning to HTE. Long-short strategies based on\nsorting stocks on charact
 eristics perform significantly better when\napplied to characteristic resp
 onders than traps. A strategy based on\nthe difference between these long-
 short returns profits from the\npredictability of HTE rather than from fac
 tors associated with the\ncharacteristics themselves. This is joint work w
 ith Pu He.
DTSTAMP:20260415T075506Z
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