Abstract
In previous studies, nature-inspired algorithms have been implemented in order to tackle hard NP-optimization problems, in the financial domain. Specifically, the task of finding optimal combination of assets with the aim of efficiently allocating your available capital is of major concern. One of the main reasons, which justifies the difficulties entailed in this problem, is the high level of uncertainty in the financial markets and not only. As mentioned above, artificial intelligent algorithms may provide a solution to this task. However, there is one major drawback concerning these techniques: the large number of open parameters. The aim of this study is twofold. Firstly, results from extended simulations are presented regarding the application of a specific hybrid nature-inspired metaheuristic in a particular formulation of the financial portfolio optimization problem. The main focus is on presenting comparative results regarding the performance of the proposed scheme for various configuration settings. Secondly, it is our intend to enhance the hybrid scheme’s performance by incorporating intelligent searching components such as other metaheuristics (simulated annealing).
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References
Giannakouris, G., Vassiliadis, V., Dounias, G.: Experimental Study on a Hybrid Nature-Inspired Algorithm for Financial Portfolio Optimization. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS, vol. 6040, pp. 101–111. Springer, Heidelberg (2010)
Brabazon, A., O’Neill, M.: Bilogically Inspired Algorithms for Financial Modeling. Springer, Heidelberg (2006)
Shapcott, J.: Index Tracking: Genetic Algorithms for Investment Portfolio Selection. EPCC-SS92-24, pp. 1–24 (1992)
Streichert, F., Ulmer, H., Zell, A.: Evolutionary algorithms and the cardinality constrained portfolio optimization problem. Selected Papers of the International Conference on Operations Research (OR 2003), pp. 253–260 (2003)
Vassiliadis, V., Bafa, V., Dounias, G.: On the performance of a hybrid genetic algorithm: application on the portfolio management problem. In: Proceedings of the 8th International Conference on Advances in Applied Financial Economics (AFE 2011), pp. 70–78 (2011)
Chen, W., Zhang, R.T., Cai, Y.M., Xu, F.S.: Particle swarm optimization for constrained portfolio selection problems. In: 5th International Conference on Machine Learning and Cybernetics, pp. 2425–2429 (2006)
Thomaidis, N.S., Angelidis, T., Vassiliadis, V., Dounias, G.: Active portfolio management with cardinality constraints: an application of particle swarm optimization. New Mathematics and Natural Computation, working paper (2007)
Vassiliadis, V., Thomaidis, N., Dounias, G.: Active Portfolio Management under a Downside Risk Framework: Comparison of a Hybrid Nature – Inspired Scheme. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 702–712. Springer, Heidelberg (2009)
Miller, B.L., Goldberg, D.E.: Genetic algorithms, tournament selection, and the effects of noise. Complex Systems 9(3), 193–212 (1995)
Markowitz, H.: Portfolio Selection. The Journal of Finance 7(1), 77–91 (1952)
Corana, A., Marchesi, M., Martini, C., Ridella, S.: Minimizing Multimodal Functions of Continuous Variables with the “Simulated Annealing” Algorithm. ACM Transactions on Mathematical Software 13(3), 262–280 (1987)
More, J.J.: The Levenberg-Marquardt algorithm: Implementation and Theory. Lecture Notes in Mathematics, vol. 630, pp. 104–116 (1978)
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Vassiliadis, V., Dounias, G., Tzanetos, A. (2012). Parameter Tuning of Hybrid Nature-Inspired Intelligent Metaheuristics for Solving Financial Portfolio Optimization Problems. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_25
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DOI: https://doi.org/10.1007/978-3-642-30448-4_25
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