Learning to optimize profits beats predicting returns - Comparing techniques for financial portfolio optimisation

GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Volumn , Issue , 2008, Pages 1681-1688

  Yan, Wei ^a   Sewell, Martin ^a   Clack, Christopher D ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Committee; Diversity; Dynamic environment; Finance; GP; Robustness; SVM; Voting

Indexed keywords

COMMITTEE; DIVERSITY; DYNAMIC ENVIRONMENT; GP; ROBUSTNESS; SVM; VOTING;

COMPUTER PROGRAMMING; GENETIC ALGORITHMS; GENETIC PROGRAMMING;

SUPPORT VECTOR MACHINES;

EID: 57349126929     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. E. Alfaro-Cid, K. Sharman, and A. Esparcia-Alcazar. A genetic programming approach for bankruptcy prediction using a highly unbalanced database. In EvoWorkshops, volume 4448 of LNCS, pages 169-178. Springer, 2007.
2. K.-Y. Chen and C.-H. Ho. An improved support vector regression modeling for Taiwan Stock Exchange market weighted index forecasting. In International Conference on Neural Networks and Brain, ICNN&B '05., volume 3, pages 1633-1638. IEEE, 2005.
3. T. G. Dietterich. Ensemble methods in machine learning. In Proc. of the First International Workshop on Multiple Classifier Systems, volume 1857 of LNCS, pages 1-15. Springer, 2000.
4. A. Fan and M. Palaniswami. Stock selection using support vector machines. In Proc. International Joint Conference on Neural Networks, IJCNN '01., volume 3, pages 1793-1798. IEEE, 2001.
5. A. A. Gaivoronski, S. Krylov, and N. van der Wijst. Optimal portfolio selection and dynamic benchmark tracking. European Journal of Operational Research, 163(1):115-131, Elsevier, 2005.
6. J. Herrmann. A genetic algorithm for minmax optimisation problems. In Proc. of the Congress on Evolutionary Computation, volume 2, pages 1099-1103. IEEE, 1999.
7. J. Kittler and F. Roli. Multiple classifier systems. In Proc. of MCS 2001. Springer Verlag, 2001.
8. H. M. Markowitz. Portfolio selection: efficient diversification of investment. MIT Business Review, 3(1):120-125, 1959.
9. J. F. Miller. Evolving a self-repairing, self-regulating, french flag organism. volume 3102 of LNCS, pages 129-139. Springer, 2004.
10. F. W. Moore and O. N. Gacia. A new methodology for reducing brittleness in genetic programming. In Proc. of the National Aerospace and Electronics 1997 Conferences, NAECON-97. IEEE, 1997.
11. V. Nissen and J. Propach. On the robustness of population-based versus point-based optimisation in the presence of noise. IEEE Transactions on Evolutionary Computation, 2(3): 107-119, 1998.
12. K. J. Oh, T. Y. Kim, and S. Min. Using genetic algorithm to support portfolio optimization for index fund management. Expert Systems with Applications, 28(2):371-379, Elsevier, 2005.
13. S. Salcedo-Sanz, J. L. Fernandez-Villacanas, M. J. Segovia-Vargas, and C. Bousono-Calzon. Genetic programming for the prediction of insolvency in non-life insurance companies. Computers & Operations Research, 32(4):749-765, Elsevier, 2005.
14. C. Schoreels and J. M. Garibaldi. The effect of varying parameter on performance for adaptive agents in techical equity market trading. In Proc. of the 3rd IEEE Internatioal Conference on Computational Cybernetics (ICCC 2005), 2005.
15. W. F. Sharpe. The Sharpe Ratio. J. Portfolio Management, 21:49-58, 1994.
16. T. Soule. Voting teams: A cooperative approach to non-typical problems using genetic programming. In Proc. of the Genetic and Evolutionary Computation Conference, volume 1, pages 916-922. Morgan Kaufmann, 1999.
17. T. Soule. Operator choice and the evolution of robust solutions. In R. L. Riolo and B. Worzel, editors, Genetic Programming Theory and Practice, chapter 16, pages 257-270. Kluwer, 2003.
18. V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1999.
19. A. S. Vieira, B. Ribeiro, S. Mukkamala, J. C. Neves, and A. H. Sung. On the performance of learning machines for bankruptcy detection. In Proc. 2nd IEEE International Conference on Computational Cybernetics, ICCC'04., pages 323-327. IEEE, 2004.
20. L. Wagman. Stock portfolio evaluation: An application of genetic-programming-based technical analysis. In J. R. Koza, editor, Genetic Algorithms and Genetic Programming at Stanford 2003, pages 213-220. Stanford Bookstore, Stanford, California, 94305-3079 USA, 2003.
21. H.-W. Wang. Portfolio selection with fuzzy MCDM using genetic algorithm: application of financial engineering. In MS'06: Proc. of the 17th IASTED international conference on Modelling and simulation, pages 597-602. ACTA Press, 2006.
22. W. Yan and C. D. Clack. Diverse committees vote for dependable profits. In GECCO '07: Proc. 9th annual conference on Genetic and evolutionary computation, volume 2, pages 2226-2233. ACM Press, 2007.
23. W. Yan and C. D. Clack. Evolving robust GP solutions for hedge fund stock selection in emerging markets. In GECCO '07: Proc. 9th annual conference on Genetic and evolutionary computation, volume 2, pages 2234-2241. ACM Press, 2007.
24. B.-T. Zhang and J.-G. Joung. Building optimal committees of genetic programs. In M. Schoenauer and K. D. et al, editors, Parallel Problem Solving from Nature - PPSN VI, pages 231-240. Springer, 2000.
25. D. Zhang, H. Huang, Q. Chen, and Y. Jiang. A comparison study of credit scoring models. In Proc. Third International Conference on Natural Computation (ICNC 2007), pages 15-18. IEEE, 2007.
26. C. Zhou, L. Yu, T. Huang, S. Wang, and K. K. Lai. Selecting valuable stock using genetic algorithm. In Proc. SEAL'06, pages 688-694. Springer, 2006.