Multi-period Possibilistic Mean Semivariance Portfolio Selection with Cardinality Constraints and its Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 239-253 ◽  
Author(s):  
Peng Zhang
Keyword(s):  
Portfolio Selection ◽  
Cardinality Constraints

scholarly journals CONSTRAINT ROBUST PORTFOLIO SELECTION BY MULTIOBJECTIVE EVOLUTIONARY GENETIC ALGORITHM

2012 ◽  
pp. 57-62
Author(s):  
S.K. MISHRA ◽  
G. PANDA ◽  
S. MEHER ◽  
R. MAJHI
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Selection ◽  
Critical Issue ◽  
Total Risk ◽  
Cardinality Constraints ◽  
Real World Data ◽  
Robust Portfolio ◽  
Total Profit ◽  
Non Gaussian ◽  
Nondominated Sorting

The problem of portfolio selection is a very challenging problem in computational finance and has received a lot of attention in last few decades. Selecting an asset and optimal weighting of it from a set of available assets is a critical issue for which the decision maker takes several aspects into consideration. Different constraints like cardinality constraints, minimum buy in thresholds and maximum limit constraint are associated with assets selection. Financial returns associated are often strongly non-Gaussian in character, and exhibit multivariate outliers. Taking these constraints into consideration and with the presence of these outliers we consider a multi-objective problem where the percentage of each available asset is so selected that the total profit of the portfolio is maximized while total risk is minimized. Nondominated Sorting Genetic Algorithm-II is used for solving this multiobjective portfolio selection problem. Performance of the proposed algorithm is carried out by performing different numerical experiments using real-world data.

Portfolio Optimization and Asset Allocation With Metaheuristics

2021 ◽  
pp. 78-96
Author(s):  
Jhuma Ray ◽  
Siddhartha Bhattacharyya ◽  
N. Bhupendro Singh
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Asset Allocation ◽  
Value At Risk ◽  
Optimal Allocation ◽  
Short Note ◽  
Objective Functions ◽  
Cardinality Constraints ◽  
Distribution Of Wealth ◽  
Risk Optimization

Portfolio optimization stands to be an issue of finding an optimal allocation of wealth to place within the obtainable assets. Markowitz stated the problem to be structured as dual-objective mean-risk optimization, pointing the best trade-off solutions within a portfolio between risks which is measured by variance and mean. Thus the major intention was nothing else than hunting for optimum distribution of wealth over a specific amount of assets by diminishing risk and maximizing returns of a portfolio. Value-at-risk, expected shortfall, and semi-variance measures prove to be complex for measuring risk, for maximization of skewness, liquidity, dividends by added objective functions, cardinality constraints, quantity constraints, minimum transaction lots, class constraints in real-world constraints all of which are incorporated in modern portfolio selection models, furnish numerous optimization challenges. The emerging portfolio optimization issue turns out to be extremely tough to be handled with exact approaches because it exhibits nonlinearities, discontinuities and high-dimensional, efficient boundaries. Because of these attributes, a number of researchers got motivated in researching the usage of metaheuristics, which stand to be effective measures for finding near optimal solutions for tough optimization issues in an adequate computational time frame. This review report serves as a short note on portfolio optimization field with the usage of Metaheuristics and finally states that how multi-objective metaheuristics prove to be efficient in dealing with portfolio selection problems with complex measures of risk defining non-convex, non-differential objective functions.

scholarly journals Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 944 ◽  
Author(s):  
Jun Zhang ◽  
Qian Li
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Selection ◽  
Hybrid Algorithm ◽  
Empirical Studies ◽  
Background Risk ◽  
Cardinality Constraints ◽  
Nsga Ii ◽  
Entropy Model ◽  
Dragonfly Algorithm ◽  
Multi Objective

In financial markets, investors will face not only portfolio risk but also background risk. This paper proposes a credibilistic multi-objective mean-semi-entropy model with background risk for multi-period portfolio selection. In addition, realistic constraints such as liquidity, cardinality constraints, transaction costs, and buy-in thresholds are considered. For solving the proposed multi-objective problem efficiently, a novel hybrid algorithm named Hybrid Dragonfly Algorithm-Genetic Algorithm (HDA-GA) is designed by combining the advantages of the dragonfly algorithm (DA) and non-dominated sorting genetic algorithm II (NSGA II). Moreover, in the hybrid algorithm, parameter optimization, constraints handling, and external archive approaches are used to improve the ability of finding accurate approximations of Pareto optimal solutions with high diversity and coverage. Finally, we provide several empirical studies to show the validity of the proposed approaches.

Portfolio Optimization with Metaheuristics

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Georgios Mamanis
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Objective Functions ◽  
Cardinality Constraints ◽  
Risk Optimization ◽  
Measures Of Risk ◽  
Mean Risk

<p>Portfolio optimization is the problem ofsearching foran optimal allocation of wealth to put in the available assets. Since the seminalworkdoneby Markowitz, the problem is codifiedas a two-objective mean-risk optimization problem where the best trade-off solutions (portfolios) between risk (measured by variance) and mean are hunted. Complex measures of risk (e.g., value-at-risk, expected shortfall, semivariance), addedobjective functions (e.g., maximization of skewness, liquidity, dividends) and pragmatic, real-worldconstraints (e.g., cardinality constraints, quantity constraints, minimum transaction lots, class constraints) that are included in recent portfolio selection models, provide many optimization challenges. The resulting portfolio optimizationproblem becomes very hard to be tackledwith exact techniquesas it displaysnonlinearities, discontinuities and high dimensional efficient frontiers. These characteristics prompteda lot ofresearchers to explorethe use of metaheuristics, which are powerful techniquesfor discoveringnear optimal solutions (sometimes the real optimum) for hard optimization problems in acceptable computationaltime. This report provides a briefnoteon the field of portfolio optimization with metaheuristics and concludes that especially Multiobjectivemetaheuristics (MOMHs) provide a natural background for dealing with portfolio selection problems with complex measures of risk (which define non-convex, non-differential objective functions), discrete constraints and multiple objectives.</p>

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