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The focus of this article is to present a hybrid approach for asset allocation as well as for forecasting the future stock price. In this approach multiple methodologies like investor behavioural survey and cluster analysis are used for grouping similar stocks, ranking of stocks is done by analytical hierarchy process (AHP) with additional criteria...
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In the existing literature, little is known about the dynamic behaviour of the optimal portfolio in terms of market inputs and arbitrary stochastic factor dynamics in an incomplete market with a stochastic volatility. In this paper, to study optimal portfolio behaviour, we compute and analyze the mean and the variance of the optimal portfolio and o...
Citations
... 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. ...
Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A great deal of improvement in portfolio optimization models has been made, including the addition of practical constraints. As the number of shares traded grows, the problem becomes dimensionally very large. In this paper, we propose the usage of modified biogeography-based optimization to solve the large-scale constrained portfolio optimization. The results indicate the effectiveness of the method used.
... [12] used a variant of BBO called laplacian biogeogeographybased optimization (LX-BBO) to find porfolio allocation from 10 assets in MV model. [13] used BBO to solve constrained MV model and applied the results in forecasting via Monte Carlo. The number of asset used in that research was 15. ...
Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A lot of improvement in the model has been made, including adding practical constraints. With the growing of shares trading, the problem becomes dimensionally very large. In this paper, we propose the usage of modified Biogeography-Based Optimization to solve the large scale constrained portfolio optimization. Results indicate the effectiveness of the method used.
... This article involves all three optimization techniques for portfolio selection TLBO [17], FMOLP [18], and BBO [19]. TLBO The TLBO algorithm was presented by Rao et. ...
Teaching-learning based optimization (TLBO), biogeography-based optimization (BBO) and fuzzy multi-objective linear programming (FMOLP) are compared in this paper for portfolio optimization. A hybrid approach has been adopted for this comparative study which is a combination of a few methods, such as investor topology, cluster analysis, analytical hierarchy process (AHP) and optimization techniques. Return, risk, liquidity, coefficient of variation (CV) and AHP weighted scores are used as the objective function for optimization.
... The problem of portfolio selection was initially presented by Professor Harry Markowitz [9]. He proposed a Markowitz model for portfolio selection that is because investing in more than one stock is less risky than investing in a single stock. ...
... max (8) subject to (9) , ...
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, comoment 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.
