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Neural Computing and Applications

, Volume 31, Supplement 2, pp 931–945 | Cite as

A chance-constrained portfolio selection model with random-rough variables

  • Madjid TavanaEmail author
  • Rashed Khanjani Shiraz
  • Debora Di Caprio
Original Article

Abstract

Traditional portfolio selection (PS) models are based on the restrictive assumption that the investors have precise information necessary for decision-making. However, the information available in the financial markets is often uncertain. This uncertainty is primarily the result of unquantifiable, incomplete, imprecise, or vague information. The uncertainty associated with the returns in PS problems can be addressed using random-rough (Ra-Ro) variables. We propose a new PS model where the returns are stochastic variables with rough information. More precisely, we formulate a Ra-Ro mathematical programming model where the returns are represented by Ra-Ro variables and the expected future total return maximized against a given fractile probability level. The resulting change-constrained (CC) formulation of the PS optimization problem is a non-linear programming problem. The proposed solution method transforms the CC model in an equivalent deterministic quadratic programming problem using interval parameters based on optimistic and pessimistic trust levels. As an application of the proposed method and to show its flexibility, we consider a probability maximizing version of the PS problem where the goal is to maximize the probability that the total return is higher than a given reference value. Finally, a numerical example is provided to further elucidate how the solution method works.

Keywords

Portfolio selection Chance-constrained programming Quadratic programming Random-rough data Rough set theory 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers and the editor for their insightful comments and suggestions. The second author, Dr. Khanjani Shiraz, is grateful to the Iran National Science Foundation for the support he received through grant number 93040776.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Madjid Tavana
    • 1
    • 2
    Email author
  • Rashed Khanjani Shiraz
    • 3
  • Debora Di Caprio
    • 4
    • 5
  1. 1.Business Systems and Analytics Department, Lindback Distinguished Chair of Information Systems and Decision SciencesLa Salle UniversityPhiladelphiaUSA
  2. 2.Business Information Systems Department, Faculty of Business Administration and EconomicsUniversity of PaderbornPaderbornGermany
  3. 3.School of Mathematics ScienceUniversity of TabrizTabrizIran
  4. 4.Department of Mathematics and StatisticsYork UniversityTorontoCanada
  5. 5.Polo Tecnologico IISS G. GalileiBolzanoItaly

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