Stock selection poses a challenge for both the investor and the finance researcher. In this paper, a hybrid approach is proposed for asset allocation, offering a combination of several methodologies for portfolio selection, such as investor topology, cluster analysis, and the analytical hierarchy process (AHP) to facilitate ranking the assets and fuzzy multiobjective linear programming (FMOLP). This paper considers some important factors of stock, like relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), and price to earnings growth ratio (PEG ratio), apart from the risk and return and stocks which are included within these same factors. Employing fuzzy multiobjective linear programming, optimization is performed using seven objective functions viz., return, risk, relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), price to earnings growth ratio (PEG ratio), and AHP weighted score. The FMOLP transforms the multiobjective problem to a single objective problem using the “weighted adaptive approach” in which the weights are calculated by AHP or choices by the investors. The FMOLP model permits choices in solution.

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.

This paper presents an experimental study with the objective’s functions of a portfolio optimization problem. This study is done by three optimization problems with a different number of objectives. A hybrid approach has been adopted for this which is a combination of a few methods, such as investor topology, cluster analysis, analytical hierarchy process (AHP), and optimization techniques. Teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), and fuzzy multi-objective linear programming (FMOLP) are compared in this paper for portfolio optimization. From this research, the conclusion comes that there should not be more options in the objective functions, otherwise the motive of the portfolio becomes misleading, but many more parameters can be used for stock valuation.