- author = "Victor Parque and Shingo Mabu and Kotaro Hirasawa",
- title = "Genetic network programming with changing structures for a novel stock selection model",
- booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation",
- year = "2011",
- editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
- isbn13 = "978-1-4503-0690-4",
- keywords = "genetic algorithms, genetic programming, genetic network programming, Real world applications: Poster",
- pages = "239--240",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001858.2001992",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "Stock selection involves the continuous quest for the
margin of safety, or a favourable difference between
the stock price and its intrinsic value. Although this
variable might not be quantified with exact precision,
it may be approximated through the underlying
relationships in financial markets and the real
economy.
We propose Genetic Network Programming with changing structures(GNP-cs), a novel evolutionary based algorithm to approximate these relationships through graph networks, and build asset selection models to identify the prospective stocks in the context of changing environments. GNP-cs uses functionally distributed systems to monitor the change of the economic environment and execute the strategy for stock selection adaptively. The comparison shows that the proposed scheme outperforms the standard stock selection styles using the stocks listed in the Russell 3000 Index.
This paper suggests that the use of evolutionary computing techniques is an excellent tool to tackle the stock selection problem, whose advantages imply the usefulness to manage the risk and safeguard investments.",
-
notes = "Also known as \cite{2001992} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
