In portfolio theory, it is well-known that the distributions of stock returns often have non-Gaussian characteristics. Therefore, we need non-symmetric distributions for modeling and accurate analysis of actuarial data. For this purpose and optimal portfolio selection, we use the Tail Mean-Variance (TMV) model, which focuses on the rare risks but high losses and usually happens in the tail of r...

This paper proposes a bacterial foraging based approach for portfolio optimization problem. We develop an improved portfolio optimization model by introducing the endogenous and exogenous liquidity risk and the corresponding indexes are designed to measure the endogenous/exogenous liquidity risk, respectively. Bacterial foraging optimization (BFO) is employed to find the optimal set of portfoli...

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...

Efficient portfolio design is a principal challenge in modern computational finance. Optimization based on Markowitz two-objective mean-variance approach is computationally expensive for real financial world. Practical portfolio design introduces further complexity as it requires the optimization of multiple return and risk measures. Some of these measures are nonlinear and nonconvex. The probl...

‎linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints‎. ‎in this paper‎, ‎to solve this problem‎, ‎we combine a discretization method and a neural network method‎. ‎by a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem‎. ‎then‎, ‎we use...

in this paper, in order to optimize the portfolio consisting of selected industrial stocks of petroleum products, automobiles and parts, electrical industry and extraction of minerals from tehran stock exchange member, first, time – varying conditional covariance matrix has been estimated based on the following multivariate garch models: diagonal-vech (1,1), ccc (1,1) and diagonal -bekk (1,1). ...

The problem of portfolio optimization which deals with the twin objectives of minimizing risk and maximizing expected portfolio return can turn complex when constraints that model investor preferences and market norms such as bounding, cardinality and class constraints, and short sales are included in it. A complex-constrained portfolio optimization such as this has been beyond the reach of sol...

In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...

To find out an effective way to solve the real estate portfolio optimization, an improved Ant Colony Algorithm based on information entropy was proposed. The information entropy was used to control the path selection and evolutional strategy by self-adjusting to overcome the premature convergence problem of the basic Ant Colony Algorithm. Simulation study on Traveling Salesman Problem and the a...