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A Novel Framework for Portfolio Optimization Based on Modified Simulated Annealing Algorithm Using ANN, RBFN, and ABC Algorithms

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Abstract

The objective of the portfolio optimization problem is to search an optimal solution for investing an amount as a stipulated value if a set of assets or securities is given. This paper has a description of a new approach for a framework that has two nascent basal parameters which are derived from return values obtained from the basic mean-variance model and another significant parameter conditional value at risk. This framework is capable of finding an optimal solution for the cost involving quadratic equations using these basal parameters, and an illustration of the approach based on modified stimulated annealing (SA) is offered in the framework, which uses a significant parameter, viz. modified step and computes optimal values of the cost. The computation of the value of the parameter modified step uses another parameter radius. Two approaches for computing the value of parameter radius are provided, which are based on ABC algorithm or by applying RBFN. ABC algorithm uses two significant parameters which are used for binding maximum and minimum values for computing the value of radius. RBFN uses three different functions, and the value of radius is computed from the maximum and minimum value of points in these functions. Lastly, the value of step is modified by multiplying it by another factor which is computed from ANN structure. Finally, the modified SA algorithm is applied, such that an optimal value of the cost, as well as the optimal value of the basal parameter, may be obtained using this modified value of step. The intention is to minimize the overall cost which is computed from quadratic equations based on these basal key parameters. A comparison of both the approaches which are based on either modified ABC or modified RBFN is shown in the paper. The results obtained show the validation of the two schemes adopted in the paper for computing the optimal solutions of the basal parameters used in the solution of portfolio optimization.

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Authors and Affiliations

  1. Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India

    Chanchal Kumar & M. N. Doja

  2. Department of Economics, Jamia Millia Islamia, New Delhi, India

    Mirza Allim Baig

Authors
  1. Chanchal Kumar
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  2. M. N. Doja
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  3. Mirza Allim Baig
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Corresponding author

Correspondence to Chanchal Kumar .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India

    Shampa Chakraverty

  2. SAP Canada, Waterloo, ON, Canada

    Anil Goel

  3. Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria

    Sanjay Misra

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Kumar, C., Doja, M.N., Baig, M.A. (2018). A Novel Framework for Portfolio Optimization Based on Modified Simulated Annealing Algorithm Using ANN, RBFN, and ABC Algorithms. In: Chakraverty, S., Goel, A., Misra, S. (eds) Towards Extensible and Adaptable Methods in Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-2348-5_13

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  • DOI: https://doi.org/10.1007/978-981-13-2348-5_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2347-8

  • Online ISBN: 978-981-13-2348-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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