Open AccessPortfolio Selection and Post Optimality Test Using Goal Programming
Author(s) -
Darsha Panwar,
M. K. Jha,
Namita Srivastava
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.27.18001
Subject(s) - goal programming , portfolio , analytic hierarchy process , computer science , investment strategy , mathematical optimization , selection (genetic algorithm) , ranking (information retrieval) , modern portfolio theory , operations research , economics , mathematics , machine learning , microeconomics , financial economics , profit (economics)
In a practical portfolio planning process the investment decision to be taken by an investor is not simple and is influenced by several other constraints like stock price, co-moment with market, return with respect to risk, past performance and so many. In this purview, a hybrid approach is employed for portfolio selection which combines multiple methodologies like investor topology, cluster analysis, analytical hierarchy process (AHP) for ranking the assets and biogeographic-based optimization (BBO). Furthermore, with the help of goal programming (GP), performing post optimality test for betterment the result which is obtained by BBO. In the goal programming, objective is to be minimizing the weighted deviations of desire goals. Weighted deviation is known as achievement, which has two branches namely over achievement and under achievement.