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...

1. Abstract A product portfolio can be defined as the set of artifacts marketed by a corporation in order to meet its customer’s needs. The proliferation of products in a company’s portfolio can create inefficiencies due to the greater complexity and the corresponding effort required to design and manufacture the set of products. A methodology for efficiently reducing the number of products thr...

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...

Modular product portfolio architecture to maximize shared systems/components among products is increasingly popular in product development for reasons of economy of scale and scope. Product development is no longer sufficient to consider only a single product. Instead, it must also consider the needs of a brand family or portfolio. One of the main challenges in designing multiple brands from th...

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...

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive mo...