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Metaheuristics for portfolio optimization : an introduction using MATLAB® / G.A. Vijayalakshmi Pai.

By: Material type: TextTextSeries: Computer engineering series (London, England). Metaheuristics set ; ; 11.Publisher: London, UK : ISTE, Ltd. ; Hoboken, NJ : John Wiley & Sons, Inc., 2018Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781119482840
  • 1119482844
  • 9781119482789
  • 111948278X
Subject(s): Genre/Form: DDC classification:
  • 332.6 23
LOC classification:
  • HG4529.5
Online resources:
Contents:
PART 1. Introduction to Portfolio Optimization -- A Brief Primer on Metaheuristics -- PART 2. Heuristic Portfolio Selection -- Metaheuristic Risk-Budgeted Portfolio Optimization -- Heuristic Optimization of Equity Market Neutral Portfolios -- Metaheuristic 130-30 Portfolio Construction -- Metaheuristic Portfolio Rebalancing with Transaction Costs -- Conclusion.
Summary: The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.
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PART 1. Introduction to Portfolio Optimization -- A Brief Primer on Metaheuristics -- PART 2. Heuristic Portfolio Selection -- Metaheuristic Risk-Budgeted Portfolio Optimization -- Heuristic Optimization of Equity Market Neutral Portfolios -- Metaheuristic 130-30 Portfolio Construction -- Metaheuristic Portfolio Rebalancing with Transaction Costs -- Conclusion.

Includes bibliographical references and index.

Online resource; title from PDF title page (John Wiley, viewed January 4, 2018).

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.

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