Fuzzy portfolio optimization : advances in hybrid multi-criteria methodologies
著者
書誌事項
Fuzzy portfolio optimization : advances in hybrid multi-criteria methodologies
（Studies in fuzziness and soft computing, v. 316）
Springer, c2014
大学図書館所蔵 件 / 全2件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
This monograph presents a comprehensive study of portfolio optimization, an important area of quantitative finance. Considering that the information available in financial markets is incomplete and that the markets are affected by vagueness and ambiguity, the monograph deals with fuzzy portfolio optimization models. At first, the book makes the reader familiar with basic concepts, including the classical mean-variance portfolio analysis. Then, it introduces advanced optimization techniques and applies them for the development of various multi-criteria portfolio optimization models in an uncertain environment. The models are developed considering both the financial and non-financial criteria of investment decision making, and the inputs from the investment experts. The utility of these models in practice is then demonstrated using numerical illustrations based on real-world data, which were collected from one of the premier stock exchanges in India. The book addresses both academics and professionals pursuing advanced research and/or engaged in practical issues in the rapidly evolving field of portfolio optimization.
目次
Portfolio optimization: an overview.- Portfolio optimization with interval coefficients.- Portfolio optimization in fuzzy environment.- Possibilistic programming approaches to portfolio optimization.- Portfolio optimization using credibility theory.- Multi-criteria fuzzy portfolio optimization.- Suitability considerations in multi-criteria fuzzy portfolio optimization-I.- Suitability considerations in multi-criteria fuzzy portfolio optimization-II.- Ethicality considerations in multi-criteria fuzzy portfolio optimization.- Multi-criteria portfolio optimization using support vector machines and genetic algorithms.
「Nielsen BookData」 より