Summary
We propose an integrated and interactive procedure for designing an enhanced indexation strategy with predetermined investment goals and risk constraints. It is based on a combination of soft computing techniques for dealing with practical and computation aspects of this problem. We deviate from the main trend in enhanced indexation by considering a) restrictions on the total number of tradable assets and b) non-standard investment objectives, focusing e.g. on the probability that the enhanced strategy under-performs the market. Fuzzy set theory is used to handle the subjectivity of investment targets, allowing a smooth variation in the degree of fulfilment with respect to the value of performance indicators. To deal with the inherent complexity of the resulting cardinality-constraint formulations, we apply three nature-inspired optimisation techniques: simulated annealing, genetic algorithms and particle swarm optimisation. Optimal portfolios derived from “soft” optimisers are then benchmarked against the American Dow Jones Industrial Average (DJIA) index and two other simpler heuristics for detecting good asset combinations: a Monte Carlo combinatorial optimisation method and an asset selection technique based on the capitalisation and the beta coefficients of index member stocks.
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
Bauer, R.J.: Genetic Algorithms and Investment Strategies. Wiley, Chichester (1994)
Beasley, J.E., Meade, N., Chang, T.J.: An evolutionary heuristic for the index tracking problem. European Journal of Operational Research 148, 621–643 (2003)
Canakgoza, N.A., Beasley, J.E.: Mixed-integer programming approaches for index tracking and enhanced indexation. European Journal of Operational Research 196(1), 384–399 (2008)
Chen, S.H.: Genetic Algorithms and Genetic Programming in Computational Finance. Kluwer Academic Publishers, Dordrecht (2002)
Fang, Y., Lai, K.K., Wang, S.: Fuzzy Portfolio Optimization: Theory and Methods. Lecture Notes in Economics and Mathematical Systems, vol. 609. Springer, Heidelberg (2008)
Gilli, M., Këllezi, E.: Threshold accepting for index tracking. working paper (2001)
Gilli, M., Winker, P.: Review of Heuristic Optimization Methods in Econometrics. In: COMISEF Working Papers Series WPS-001 (2008)
Goldberg, D.E.: Genetic Algorithms in Search. In: Optimization and Machine Learning, Kluwer Academic Publishers, Dordrecht (1989)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Maringer, D., Oyewumi, O.: Index tracking with constrained portfolios. Intelligent Systems in Accounting, Finance and Management 15, 57–71 (2007)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Thomaidis, N.S., Angelidis, T., Vassiliadis, V., Dounias, G.: Active Portfolio Management with Cardinality Constraints: An Application of Particle Swarm Optimization. Special Issue on New Computational Methods for Financial Engineering, Journal of New Mathematical and Natural Computation 5(3) (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Thomaidis, N.S. (2011). A Soft Computing Approach to Enhanced Indexation. In: Brabazon, A., O’Neill, M., Maringer, D. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23336-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-23336-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23335-7
Online ISBN: 978-3-642-23336-4
eBook Packages: EngineeringEngineering (R0)