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Metaheuristics for Portfolio Optimization An Introduction using MATLAB von Pai, G. A. Vijayalakshmi (eBook)

  • Verlag: Wiley-ISTE
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Metaheuristics for Portfolio Optimization

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.

Produktinformationen

    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 316
    Sprache: Englisch
    ISBN: 9781119482796
    Verlag: Wiley-ISTE
    Größe: 19815 kBytes
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Metaheuristics for Portfolio Optimization

Preface

Portfolio Optimization, that deals with the choice and appropriate allocation of capital over assets comprising a portfolio so that it is better off than any other, given the investment objectives and preferences of the investor, has been a traditional and hardcore discipline of Finance in general or Financial Engineering, in particular.

Modern Portfolio Theory (MPT) - a theory pioneered by Harry Markowitz in his paper "Portfolio Selection" published in the Journal of Finance in 1952 and expounded in his book Portfolio Selection: Efficient Diversification, in 1959, which eventually won him the Nobel Prize in Economics in 1990, did make a huge impact on the discipline, despite its shortcomings pointed out by its critics. MPT harped on the expected risk and return of an asset, the benefits of diversification where one avoids putting all eggs in one basket, the categorization of risks as systematic and unsystematic, the role played by efficient frontier and risk-free assets in determining the expected portfolio returns and so on, which "...for over six decades... provided money managers and sophisticated investors with a tried-and-true way to select portfolios." ("Harry Markowitz Father of Modern Portfolio Theory Still Diversified", The Finance Professionals' Post, December 28, 2011). This book subscribes to MPT and all the portfolio optimization models discussed in it are built over the MPT framework.

Nevertheless, Markowitz's framework assumed a market devoid of transaction costs or taxes or short selling, to list a few, that resulted in simple portfolio optimization models that could be easily solved using a traditional method such as Quadratic Programming, to yield the optimal portfolios desired. However, markets in reality are not as naïve as they were assumed to be. In practice, market frictions, investor preferences, investment strategies, company policies of investment firms etc., have resulted in complex objectives and constraints that have made the problem of portfolio optimization difficult, if not intractable. The complex mathematical models defining the portfolio have found little help from traditional or analytical methods in their efforts to arrive at optimal portfolios, forcing the need to look for non-traditional algorithms and non-orthodox approaches from the broad discipline of Computational Intelligence. Fortunately, the emerging and fast-growing discipline of Metaheuristics, a sub discipline of Computational Intelligence, has refreshingly turned out to be a panacea for all the ills of such of these notorious problem models. Metaheuristics has not just turned out to be a viable alternative for solving difficult optimization problems, but in several cases has turned out to be the only alternative to solve the complex problem models concerned.

Metaheuristic approaches represent efficient ways to deal with complex optimization problems and are applicable to both continuous and combinatorial optimization problems. Nature-inspired Metaheuristics is a popular and active research area which relies on natural systems for the solution of optimization problem models and one of its genres, Evolutionary Algorithms, which is inspired by biological evolution, is what has been applied to solve the portfolio optimization problem models discussed in the book.

Objectives of the book

Metaheuristics for Portfolio Optimization elucidates Portfolio Optimization problems/models that employ metaheuristics for their effective solutions/decisions and demonstrates their application and results using MATLAB®. The book views a traditional hardcore finance discipline from an interdisciplinary perspective, with the cornerstones of:

  • - finance (Portfolio Optimization, in particular);
  • - metaheuristics (Evolutionary Algorithms, in pa

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