Abstract
This is a narrative describing the implementation of a genetic programming technique for stock picking in a quantitatively driven, risk-controlled, US equity portfolio. It describes, in general, the problems that the authors faced in their portfolio context when using genetic programming techniques and in gaining acceptance of the technique by a skeptical audience. We discuss in some detail the construction of the fitness function, the genetic programming system’s parameterization (including data selection and internal function choice), and the interpretation and modification of the generated programs for eventual implementation.
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References
Koza, J. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: MIT Press.
Thomas, J.D and K. Sycara (1999). The Importance of Simplicity and Validation in Genetic Programming for Data Mining in Financial Data. Freitas, (Ed.), Data Mining with Evolutionary Algorithms: Research Directions, AAAI Press.
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© 2005 Springer Science+Business Media, Inc.
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Caplan, M., Becker, Y. (2005). Lessons Learned Using Genetic Programming in a Stock Picking Context. In: O’Reilly, UM., Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice II. Genetic Programming, vol 8. Springer, Boston, MA. https://doi.org/10.1007/0-387-23254-0_6
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DOI: https://doi.org/10.1007/0-387-23254-0_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23253-9
Online ISBN: 978-0-387-23254-6
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