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Concentration Measures in Portfolio Management

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Advances in Computational Intelligence (IPMU 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 300))

Abstract

The allocation of wealth in an investment portfolio can be viewed as finding a proper weight allocation vector while obeying investment constraints and preferences, as risk aversion, expected returns, investment sector preferences and regulatory and/or preferential limits on allocation per stock. It is these constraints and preferences that make the stock (or wealth) allocation problem a multiple criteria problem.

We link these allocations to the theory of inequality and related concentration measurements, focusing on weight concentration as an other criterion for investment portfolio management. In this paper, we will discuss the Gini and the Herfindhal concentration measures of weights and relate them to the portfolio allocation problem using a hypothetical investment portfolio.

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Author information

Authors and Affiliations

  1. Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands

    Ghassan Chammas & Jaap Spronk

Authors
  1. Ghassan Chammas
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  2. Jaap Spronk
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco  & Benedetto Matarazzo  & 

  2. CNRS UMR 7606, DAPA, LIP6 8, Université Pierre et Marie Curie - Paris6, rue du Capitaine Scott, F-75015, Paris, France

    Bernadette Bouchon-Meunier

  3. Dip. Matematica e Informatica, Università di Perugia, 06123, Perugia, Italy

    Giulianella Coletti

  4. Department of Computer and Management Science, University of Trento, Via Inama 5, 38122, Trento, Italy

    Mario Fedrizzi

  5. Machine Intelligence Institute — IONA College,, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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© 2012 Springer-Verlag Berlin Heidelberg

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Chammas, G., Spronk, J. (2012). Concentration Measures in Portfolio Management. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 300. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31724-8_11

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  • DOI: https://doi.org/10.1007/978-3-642-31724-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31723-1

  • Online ISBN: 978-3-642-31724-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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