- author = "Wei Yan and Martin V. Sewell and Christopher D. Clack",
- title = "Learning to optimize profits beats predicting returns -: comparing techniques for financial portfolio optimisation",
- booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation",
- year = "2008",
- editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener",
- pages = "1681--1688",
- address = "Atlanta, GA, USA",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "12-16 " # jul,
- keywords = "genetic algorithms, genetic programming, committee, diversity, dynamic environment, finance, robustness, SVM, voting, Real-World application",
- isbn13 = "978-1-60558-130-9",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1681.pdf",
-
DOI = "
doi:10.1145/1389095.1389409",
- size = "8 pages",
-
abstract = "Stock selection for hedge fund portfolios is a
challenging problem that has previously been tackled by
many machine-learning, genetic and evolutionary
systems, including both Genetic Programming (GP) and
Support Vector Machines (SVM). But which is the better?
We provide a head-to-head evaluation of GP and SVM
applied to this real-world problem, including both a
standard comparison of returns on investment and a
comparison of both techniques when extended with a
{"}voting{"} mechanism designed to improve both returns
and robustness to volatile markets. Robustness is an
important additional dimension to this comparison,
since the markets (the environment in which the GP or
SVM solution must survive) are dynamic and
unpredictable.
Our investigation highlights a key difference in the two techniques, showing the superiority of the GP approach for this problem.",
-
notes = "GECCO-2008 A joint meeting of the seventeenth
international conference on genetic algorithms
(ICGA-2008) and the thirteenth annual genetic
programming conference (GP-2008).
ACM Order Number 910081. Also known as \cite{1389409}",
