Bond Rating with piGrammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{Brabazon:2008:K-DC,
  author =       "Anthony Brabazon and Michael O'Neill",
  title =        "Bond Rating with {piGrammatical} Evolution",
  booktitle =    "Knowledge Engineering and Intelligent Computations",
  publisher =    "Springer",
  year =         "2008",
  editor =       "C. Cotta and S. Reich and R. Schaefer and A. Ligeza",
  volume =       "102",
  series =       "Studies in Computational Intelligence",
  chapter =      "2",
  pages =        "17--30",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  isbn13 =       "978-3-540-77474-7",
  DOI =          "doi:10.1007/978-3-540-77475-4_2",
  abstract =     "Most large firms use both share and debt capital to
                 provide long-term finance for their operations. The
                 debt capital may be raised from a bank loan, or may be
                 obtained by selling bonds directly to investors. As an
                 example of the scale of US bond markets, the value of
                 new bonds issued in 2004 totaled $5.48 trillion, and
                 the total value of outstanding marketable bond debt at
                 31 December 2004 was $23.6 trillion [1]. In comparison,
                 the total global market capitalisation of all companies
                 quoted on the New York Stock Exchange (NYSE) at
                 31/12/04 was $19.8 trillion [2]. Hence, although
                 company stocks attract most attention in the business
                 press, bond markets are actually substantially
                 larger.

                 When a company issues traded debt (e.g. bonds), it must
                 obtain a credit rating for the issue from at least one
                 recognised rating agency (Standard and Poor's (S&P),
                 Moody's and Fitches'). The credit rating represents an
                 agency's opinion, at a specific date, of the credit
                 worthiness of a borrower in general (a bond-issuer
                 credit-rating), or in respect of a specific debt issue
                 (a bond credit rating). These ratings impact on the
                 borrowing cost, and the marketability of issued bonds.
                 Although several studies have examined the potential of
                 both statistical and machine-learning methodologies for
                 credit rating prediction [3-6], many of these studies
                 used relatively small sample sizes, making it difficult
                 to generalise strongly from their findings. This study
                 by contrast, uses a large dataset of 791 firms, and
                 introduces pi GE to this domain.",
}

Genetic Programming entries for Anthony Brabazon Michael O'Neill

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