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Pareto‐archived evolutionary wavelet network for financial constrained portfolio optimization

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  • N. C. Suganya
  • G. A. Vijayalakshmi Pai

Abstract

The multi‐objective portfolio optimization problem is too complex to find direct solutions by traditional methods when constraints reflecting investor's preferences and/or market frictions are included in the mathematical model and hence heuristic approaches are sought for their solution. In this paper we propose the solution of a multi‐criterion (bi‐objective) portfolio optimization problem of minimizing risk and maximizing expected return of the portfolio which includes basic, bounding, cardinality, class and short sales constraints using a Pareto‐archived evolutionary wavelet network (PEWN) solution strategy. Initially, the empirical covariance matrix is denoised by employing a wavelet shrinkage denoising technique. Second, the cardinality constraint is eliminated by the application of k‐means cluster analysis. Finally, a PEWN heuristic strategy with weight standardization procedures is employed to obtain Pareto‐optimal solutions satisfying all the constraints. The closeness and diversity of Pareto‐optimal solutions obtained using PEWN is evaluated using different measures and the results are compared with existing only solution strategies (evolution‐based wavelet Hopfield neural network and evolution‐based Hopfield neural network) to prove its dominance. Eventually, data envelopment analysis is also used to test the efficiency of the non‐dominated solutions obtained using PEWN. Experimental results are demonstrated on the Bombay Stock Exchange, India (BSE200 index: period July 2001–July 2006), and the Tokyo Stock Exchange, Japan (Nikkei225 index: period March 2002–March 2007), data sets. Copyright © 2010 John Wiley & Sons, Ltd.

Suggested Citation

  • N. C. Suganya & G. A. Vijayalakshmi Pai, 2010. "Pareto‐archived evolutionary wavelet network for financial constrained portfolio optimization," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(2), pages 59-90, April.
  • Handle: RePEc:wly:isacfm:v:17:y:2010:i:2:p:59-90
    DOI: 10.1002/isaf.313
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    References listed on IDEAS

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    1. Schlottmann, Frank & Seese, Detlef, 2004. "A hybrid heuristic approach to discrete multi-objective optimization of credit portfolios," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 373-399, September.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Gabor Papp & Szilard Pafka & Maciej A. Nowak & Imre Kondor, 2005. "Random Matrix Filtering in Portfolio Optimization," Papers physics/0509235, arXiv.org.
    4. Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
    5. Basalto, N. & Bellotti, R. & De Carlo, F. & Facchi, P. & Pascazio, S., 2005. "Clustering stock market companies via chaotic map synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 196-206.
    6. Malgorzata Snarska & Jakub Krzych, 2006. "Automatic Trading Agent. RMT based Portfolio Theory and Portfolio Selection," Papers physics/0608293, arXiv.org.
    7. G. Bonanno & G. Caldarelli & F. Lillo & S. Micciché & N. Vandewalle & R. Mantegna, 2004. "Networks of equities in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 363-371, March.
    8. Bernaschi, Massimo & Grilli, Luca & Vergni, Davide, 2002. "Statistical analysis of fixed income market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 308(1), pages 381-390.
    9. Galluccio, Stefano & Bouchaud, Jean-Philippe & Potters, Marc, 1998. "Rational decisions, random matrices and spin glasses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 259(3), pages 449-456.
    10. Tola, Vincenzo & Lillo, Fabrizio & Gallegati, Mauro & Mantegna, Rosario N., 2008. "Cluster analysis for portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 235-258, January.
    11. L. Kullmann & J. Kertesz & K. Kaski, 2002. "Time dependent cross correlations between different stock returns: A directed network of influence," Papers cond-mat/0203256, arXiv.org, revised May 2002.
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