This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-domi...

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...

Using Metaheuristics models and Evolutionary Algorithms for solving portfolio problem has been considered in recent years.In this study, by using particles swarm optimization and tabu search algorithms we  optimized two-sided risk measures . A standard exact penalty function transforms the considered portfolio selection problem into an equivalent unconstrained minimization problem. And in final...

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...

Multistage portfolio optimization models are difficult to solve when market risk is measured by Value-at-Risk (VaR), this paper proposes a soft method for solving VaR-based portfolio optimization models based on a soft optimization approach. In order to demonstrate the validity of the proposed soft method, we perform portfolio management experiments with real data from the New York stock market...

In this study, a double-stage genetic optimization algorithm is proposed for portfolio selection. In the first stage, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the second stage, investment allocation in the selected good quality assets is optimized using a genetic algorithm based on Markowitz’s theory. Through the two-stage genetic optimization pr...

Portfolio optimization using private equity is typically based on one of three indices: listed private equity, transaction-based private equity, or appraisal value-based private equity indices. However, we show that none of these indices is fully suitable for portfolio optimization. We introduce here a new benchmark index for venture capital and buyouts, which is updated monthly, adjusted for a...

in order to use an islamic financial instrument, this paper intends to measure and evaluate negative and positive deviations from target rate of return in investment opportunity evaluation,that leads to presenting an upside potential- adjusted risk measure. this risk measure named upside potential adjusted risk measure (alpm) is generally applicable and provides assumptions of variance, downsid...