- author = "N. G. Pavlidis and E. G. Pavlidis and M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis",
- title = "Computational Intelligence Algorithms for Risk-Adjusted Trading Strategies",
- booktitle = "2007 IEEE Congress on Evolutionary Computation",
- year = "2007",
- editor = "Dipti Srinivasan and Lipo Wang",
- pages = "540--547",
- address = "Singapore",
- month = "25-28 " # sep,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- ISBN = "1-4244-1340-0",
- file = "1953.pdf",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/CEC.2007.4424517",
- abstract = "This paper investigates the performance of trading strategies identified through Computational Intelligence techniques. We focus on trading rules derived by Genetic Programming, as well as, Generalised Moving Average rules optimised through Differential Evolution. The performance of these rules is investigated using recently proposed risk-adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but Genetic Programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.",
-
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
