Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates-Reference-Cited by-同舟云学术

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Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates

Published:2013-11-20 Issue:1 Volume:45 Page:49-89
ISSN:0927-7099
Container-title:Computational Economics
language:en
Short-container-title:Comput Econ

Author:

Deng Shangkun,Yoshiyama Kazuki,Mitsubuchi Takashi,Sakurai Akito

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s10614-013-9407-6.pdf

Reference57 articles.

1. Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51, 245–271.

2. Azoff, E. M. (1994). Neural networks time series forecasting of financial markets. Chichester: Wiley.

3. Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. Proceedings of the 21st International Conference on, Machine Learning (pp. 6–13).

4. Badawy, F. A., Abdelazim, H. Y., & Darwish, M. G. (2005). Genetic algorithms for predicting the Egyptian stock market. Proceedings of ITI Third International Conference on Information and Communication Technology Enabling Technologies for the New Knowledge Society (pp. 109–122).

5. Banzhaf, W., Nordin, P., Keller, R. E., & Francone, F. D. (1998). Genetic Programming—An introduction: On the automatic evolution of computer programs and its applications. San Francisco: Kaufmann.

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