IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v17y2010i2p59-90.html
   My bibliography  Save this article

Pareto‐archived evolutionary wavelet network for financial constrained portfolio optimization

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.313
    Download Restriction: no

    File URL: https://libkey.io/10.1002/isaf.313?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Malgorzata Snarska & Jakub Krzych, 2006. "Automatic Trading Agent. RMT based Portfolio Theory and Portfolio Selection," Papers physics/0608293, arXiv.org.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Gabor Papp & Szilard Pafka & Maciej A. Nowak & Imre Kondor, 2005. "Random Matrix Filtering in Portfolio Optimization," Papers physics/0509235, arXiv.org.
    9. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Sebastiano Michele Zema & Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2021. "Mesoscopic Structure of the Stock Market and Portfolio Optimization," Papers 2112.06544, arXiv.org.
    3. G.A. Vijayalakshmi Pai & Thierry Michel, 2012. "Integrated Metaheuristic Optimization Of 130–30 Investment‐Strategy‐Based Long–Short Portfolios," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 43-74, January.
    4. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Correlation of financial markets in times of crisis," Papers 1102.1339, arXiv.org, revised Mar 2011.
    5. Peter N. Posch & Daniel Ullmann & Dominik Wied, 2019. "Detecting structural changes in large portfolios," Empirical Economics, Springer, vol. 56(4), pages 1341-1357, April.
    6. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    7. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    8. Justo Puerto & Moises Rodr'iguez-Madrena & Andrea Scozzari, 2019. "Location and portfolio selection problems: A unified framework," Papers 1907.07101, arXiv.org.
    9. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.
    10. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    11. Bommarito, Michael J. & Duran, Ahmet, 2018. "Spectral analysis of time-dependent market-adjusted return correlation matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 273-282.
    12. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    13. Carlos León & Ron J. Berndsen, 2013. "Modular scale-free architecture of Colombian financial networks: Evidence and challenges with financial stability in view," Borradores de Economia 11104, Banco de la Republica.
    14. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    15. Aoki, Masanao & Hawkins, Raymond, 2009. "Macroeconomic Relaxation: Adjustment Processes of Hierarchical Economic Structures," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-21.
    16. León, Carlos & Leiton, Karen & Pérez, Jhonatan, 2014. "Extracting the sovereigns’ CDS market hierarchy: A correlation-filtering approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 407-420.
    17. Urbanowicz, Krzysztof & Richmond, Peter & Hołyst, Janusz A., 2007. "Risk evaluation with enhanced covariance matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 468-474.
    18. Jos'e Vin'icius de Miranda Cardoso & Jiaxi Ying & Daniel Perez Palomar, 2020. "Algorithms for Learning Graphs in Financial Markets," Papers 2012.15410, arXiv.org.
    19. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy (IfW Kiel).
    20. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:isacfm:v:17:y:2010:i:2:p:59-90. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.