Genetic Programming Use in Structural Modeling Applied to the Earnings-Returns Relation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Milev:thesis,
  author =       "Jordan G. Milev",
  title =        "Genetic Programming Use in Structural Modeling Applied
                 to the Earnings-Returns Relation",
  school =       "Yale",
  year =         "2004",
  address =      "USA",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://phdtree.org/pdf/25586585-genetic-programming-use-in-structural-modeling-applied-to-the-earnings-returns-relation/",
  URL =          "http://search.proquest.com/docview/305110188/AD4457348014431PQ/1?accountid=14511",
  size =         "136 pages",
  abstract =     "The selection of appropriate functional form for
                 describing the relation between two economic variables
                 has profound implications about the consistency and
                 significance of estimated model parameters and about
                 the predictions obtained from such a model. Until
                 recently, nonparametric approaches have been the only
                 solution to problems of model identification when the
                 parametric form of the function is unknown. In the
                 first part of the dissertation we develop an
                 implementation of the algorithmic model selection
                 technique of genetic programming (GP). We illustrate
                 how it works and offer a brief comparison with
                 nonparametric estimation methods. In the second part of
                 the dissertation we specifically address a recent issue
                 in the GP literature about overfitting and illustrate
                 how it can be controlled. We also examine GP's ability
                 to recognize a spurious regression and devise an
                 illustrate a metric measuring the predictability of a
                 data set using GP. In the third part of the
                 dissertation we use GP to model how stock prices react
                 to unanticipated accounting earnings. The result is a
                 nonlinear parametric specification of the reaction of
                 excess current-period stock price returns to the
                 unexpected component of quarterly earnings. We confirm
                 the existence of a nonlinear earnings response model
                 that has superior in-sample and out-of-sample
                 predictive power over the traditionally employed linear
                 earnings regression. Our results have several
                 implications: 1) it is important to incorporate
                 forecast revisions in the earnings-returns
                 specification; 2) when the earnings-returns relation is
                 nonlinear, a nonsymmetric response to earnings
                 announcements can be achieved even when the earnings
                 response function itself is symmetric; 3) firms can
                 affect the size of their earnings response coefficient
                 by pre-announcing earnings; 4) appropriately accounting
                 for the nonlinear form of the earnings-returns relation
                 decreases the abnormal returns associated with earnings
                 surprises. Our approach suggests an alternative to the
                 linear earnings-returns relation which may provide
                 suitable framework for future empirical work.",
  notes =        "http://www.genealogy.ams.org/id.php?id=118746
                 Supervisor: Peter Charles Bonest
                 Phillips

                 http://korora.econ.yale.edu/phillips/info/pcbvita-0908.pdf

                 UMI Microform 3152961",
}

Genetic Programming entries for Jordan G Milev

Citations