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Steepest Ascent Hill Climbing for Portfolio Selection

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Applications of Evolutionary Computation (EvoApplications 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7248))

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Abstract

The construction of a portfolio in the financial field is a problem faced by individuals and institutions worldwide. In this paper we present an approach to solve the portfolio selection problem with the Steepest Ascent Hill Climbing algorithm. There are many works reported in the literature that attempt to solve this problem using evolutionary methods. We analyze the quality of the solutions found by a simpler algorithm and show that its performance is similar to a Genetic Algorithm, a more complex method. Real world restrictions such as portfolio value and rounded lots are considered to give a realistic approach to the problem.

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Author information

Authors and Affiliations

  1. Tecnológico de Monterrey, Campus Monterrey, Av. Eugenio Garza Sada 2501, C.P. 64849, Monterrey, N.L., México

    Jonathan Arriaga & Manuel Valenzuela-Rendón

Authors
  1. Jonathan Arriaga
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  2. Manuel Valenzuela-Rendón
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. Financial Mathematics and Computation Cluster Natural Computing Research and Applications Group Complex and Adaptive Systems Laboratory, University College, Dublin, Ireland

    Alexandros Agapitos

  3. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  4. ETSI Informática, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  5. Centro Universitario de Merida, Universidad de Extremadura, Spain

    Francisco Fernández de Vega

  6. Dalle Molle Institute for Artificial Intelligence (IDSIA), Lugano, Switzerland

    Gianni A. Di Caro

  7. DFKI Bremen, Cyber-Physical Systems, Bremen, Germany

    Rolf Drechsler

  8. Knowledge Engineering Research Group, Aston University, B4 7ET, Aston Triangle, Birmingham, UK

    Anikó Ekárt

  9. Instituto Tecnológico de Informática, Edif. 8G Acceso B, Universidad Politécnica de Valencia, 46022, Valencia, Spain

    Anna I. Esparcia-Alcázar

  10. Next Generation Intelligent Networks Research Center (nexGIN RC), National University of Computer & Emerging Sciences (FAST-NU), Islamabad, Pakistan

    Muddassar Farooq

  11. Department of Computer Science,, University College London, Gower Street, WC1E 6BT, London, UK

    William B. Langdon

  12. Depto. Arquitectura y Tecnologa de Computadores, ETS Ingeiera Informtica, C/Daniel Saucedo Aranda, s/n, 18071, Granada, Spain

    Juan J. Merelo-Guervós

  13. Chair of Algorithm Engineering, Computational Intelligence Group, Dept. of Computer Science, Technische Universität Dortmund, Germany

    Mike Preuss

  14. HTWK Leipzig, Fakultät Elektrotechnik und Informationstechnik, Institut Mess–, Steuerungs– und Regelungstechnik, Postfach 30 11 66, D–04251, Leipzig, Germany

    Hendrik Richter

  15. INESC-ID Lisboa, Rua Alves Redol 9,, 1000-029, Lisboa, Portugal

    Sara Silva

  16. Centre for Informatics and Systems, University of Coimbra, Portugal

    Anabela Simões

  17. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  18. Institute of High Performance Computing and Networking, ICAR-CNR, Naples, Italy

    Ernesto Tarantino

  19. Dipartimento di Tecnologie dell’Informazione, Universita’ degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  20. IT University of Copenhagen, Rued Langaards Vej 7, 2300, Copenhagen, Denmark

    Julian Togelius

  21. Centre for Emergent Computing, Edinburgh Napier University, Edinburgh,, UK

    Neil Urquhart

  22. Department of Computer Engineering, Istanbul Technical University, 34490, Maslak, Turkey

    A. Şima Uyar

  23. Center For Computer Games Research, IT University Of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen, Denmark

    Georgios N. Yannakakis

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Arriaga, J., Valenzuela-Rendón, M. (2012). Steepest Ascent Hill Climbing for Portfolio Selection. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_15

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  • DOI: https://doi.org/10.1007/978-3-642-29178-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29177-7

  • Online ISBN: 978-3-642-29178-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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