Portfolio Selection and Post Optimality Test Using Goal Programming

Abstract

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, analyti cal 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.

An Experimental Study with Objectives Functions for Portfolio Optimization Problem

“…Three hybrid approaches are proposed for portfolio selection using investor behavior, cluster analysis, AHP, and optimization technique. The data for an experimental and numerical study has been taken from the Bombay Stock Exchange from February'2016 to January'2017 which is discussed in [ [20]. The genetic algorithm and fuzzy decision theory are applied for portfolio selection.…”

“…High HSI shows a habitat contains many species and low HSI shows that a habitat contains few species. The complete methodology and data analysis are given in [20].…”

Large-Scale Portfolio Optimization Using Biogeography-Based Optimization

“…In Garg and Deep (2019), the authors used a variant of BBO called Laplacian biogeogeographybased optimization (LX-BBO) to find portfolio allocation from 10 assets in an MV model. In Panwar et al (2018), the authors used BBO to solve a constrained MV model and applied the results in forecasting via Monte Carlo. The number of assets used in that research was 15.…”