Quantitative models for stock selection and portfolio management face the challenge of determining the most efficacious factors, and how they interact, from large amounts of financial data. Genetic programming using “simple objective” fitness functions has been shown to be an effective technique for selecting factors and constructing multi-factor models for ranking stocks, but the resulting models can be somewhat unbalanced in satisfying the multiple objectives that portfolio managers seek: large excess returns that are consistent across time and the cross-sectional dimensions of the investment universe. In this study, we implement and evaluate three multi-objective algorithms to simultaneously optimize the information ratio, information coefficient, and intra-fractile hit rate of a portfolio. These algorithms — the constrained fitness function, sequential algorithm, and parallel algorithm — take widely different approaches to combine these different portfolio metrics. The results show that the multi-objective algorithms do produce well-balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel multi-objective algorithms. Moreover, this algorithm generalizes to the held-out test data set much better than any of the single fitness algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allen, F. and Kajalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51:245-271.
Becker, Ying, Fei, Peng, and Lester, Anna M. (2006). Stock selection : An innovative application of genetic programming methodology. In Riolo, Rick L., Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 12. Springer, Ann Arbor.
Bleuler, Stefan, Brack, Martin, Thiele, Lothar, and Zitzler, Eckart (2001). Multiobjective genetic programming: Reducing bloat using SPEA2. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pages 536-543, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea. IEEE Press.
Caplan, Michael and Becker, Ying (2004). Lessons learned using genetic programming in a stock picking context. In O’Reilly, Una-May, Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice II, chapter 6, pages 87-102. Springer, Ann Arbor.
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2002). A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: Nsgaii. IEEE Transactions of Evolutionary Computation, 6(2):182-197.
Holland, J.H. (1975). Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.
Lawrenz, C. and Westerhoff, F. (2003). Modeling exchange rate behavior with a genetic algorithm. Computational Economics, 21:209-229.
Li, J. and Tsang, E.P.K (1999). Improving technical analysis predictions: an application of genetic programming. In Proceedings of Florida artificial intelligence research symposium.
Neely, Christopher J., Weller, Paul A., and Dittmar, Rob (1997). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. The Journal of Financial and Quantitative Analysis, 32(4):405-426.
Schaffer, J.D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In Genetic Algorithms and Their Applications: Proceedings of the First International Conference on Genetic Algorithms, pages 93-100.
Wall, M. (2000). Galib: A c++ library of genetic algorithm components.
Wang, J. (2000). Trading and hedging in s&p 500 spot and futures markets using genetic programming. Journal of Futures Markets, 20(10):991-942.
Zhou, A. (2003). Enhanced emerging market stock selection. In Riolo, Rick L. and Worzel, Bill, editors, Genetic Programming Theory and Practice I. Kluwer.
Zitzler, E, Laumans, M, and Theile, L (2001). Spea2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Swiss Federal Institute of Technology (ETH), Zurich. Computer Engineering and Networks Labratory.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Becker, Y.L., Fox, H., Fei, P. (2008). An Empirical Study of Multi-Objective Algorithms for Stock Ranking. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice V. Genetic and Evolutionary Computation Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76308-8_14
Download citation
DOI: https://doi.org/10.1007/978-0-387-76308-8_14
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-76307-1
Online ISBN: 978-0-387-76308-8
eBook Packages: Computer ScienceComputer Science (R0)