- 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.",
