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Books > Business & Economics > Business & management > Management & management techniques > Operational research
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Multicriteria Portfolio Construction with Python (Paperback, 1st ed. 2020)
Loot Price: R3,477
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Multicriteria Portfolio Construction with Python (Paperback, 1st ed. 2020)
Series: Springer Optimization and Its Applications, 163
Expected to ship within 10 - 15 working days
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This book covers topics in portfolio management and multicriteria
decision analysis (MCDA), presenting a transparent and unified
methodology for the portfolio construction process. The most
important feature of the book includes the proposed methodological
framework that integrates two individual subsystems, the portfolio
selection subsystem and the portfolio optimization subsystem. An
additional highlight of the book includes the detailed,
step-by-step implementation of the proposed multicriteria
algorithms in Python. The implementation is presented in detail;
each step is elaborately described, from the input of the data to
the extraction of the results. Algorithms are organized into small
cells of code, accompanied by targeted remarks and comments, in
order to help the reader to fully understand their mechanics.
Readers are provided with a link to access the source code through
GitHub. This Work may also be considered as a reference which
presents the state-of-art research on portfolio construction with
multiple and complex investment objectives and constraints. The
book consists of eight chapters. A brief introduction is provided
in Chapter 1. The fundamental issues of modern portfolio theory are
discussed in Chapter 2. In Chapter 3, the various multicriteria
decision aid methods, either discrete or continuous, are concisely
described. In Chapter 4, a comprehensive review of the published
literature in the field of multicriteria portfolio management is
considered. In Chapter 5, an integrated and original multicriteria
portfolio construction methodology is developed. Chapter 6 presents
the web-based information system, in which the suggested
methodological framework has been implemented. In Chapter 7, the
experimental application of the proposed methodology is discussed
and in Chapter 8, the authors provide overall conclusions. The
readership of the book aims to be a diverse group, including fund
managers, risk managers, investment advisors, bankers, private
investors, analytics scientists, operations researchers scientists,
and computer engineers, to name just several. Portions of the book
may be used as instructional for either advanced undergraduate or
post-graduate courses in investment analysis, portfolio
engineering, decision science, computer science, or financial
engineering.
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