A Hierarchical Fuzzy Portfolio Selection Process Considering Transaction Costs with a Hybrid Intelligent Algorithm

2013 ◽  
Vol 4 (3) ◽  
pp. 90-108 ◽  
Author(s):  
Masoud Rabbani ◽  
Seyyed Mostafa Bahadornia
Keyword(s):  
Portfolio Selection ◽  
Selection Process ◽  
Least Square ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Fuzzy Portfolio Selection ◽  
De Algorithm ◽  
Fuzzy Environment ◽  
Multiobjective Mathematical Programming ◽  
Pareto Frontiers

This article presents a hierarchical process for multiobjective portfolio selection in fuzzy environment. Methodology proposed in this paper is consist of three main steps; (a) determining weight of each objective including return, risk and liquidity, by fuzzy logarithmic least square according to investors' preferences matrix by the means of DE algorithm, (b&c) assigning pareto frontiers of problem and computing portion of each security by multiobjective mathematical programming in accordance to obtained weights by the means of GA. Also transaction cost related to each security which are rarely considered in previous works are brought in the authors’ model.

A novel probabilistic hesitant fuzzy portfolio selection model with value-at-risk and safety level of score

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue Deng ◽  
Weimin Li
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Selection Model ◽  
Selection Models ◽  
Content Type ◽  
Fuzzy Portfolio Selection ◽  
Fuzzy Environment ◽  
Portfolio Selection Model

Purpose This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment. Design/methodology/approach It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models. Findings The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns. Originality/value This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.

scholarly journals A Hybrid Intelligent Algorithm for Optimal Birandom Portfolio Selection Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Qi Li ◽  
Guo-Hua Cao ◽  
Dan Shan
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Portfolio Selection ◽  
Numerical Experiments ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Selection Problems

Birandom portfolio selection problems have been well developed and widely applied in recent years. To solve these problems better, this paper designs a new hybrid intelligent algorithm which combines the improved LGMS-FOA algorithm with birandom simulation. Since all the existing algorithms solving these problems are based on genetic algorithm and birandom simulation, some comparisons between the new hybrid intelligent algorithm and the existing algorithms are given in terms of numerical experiments, which demonstrate that the new hybrid intelligent algorithm is more effective and precise when the numbers of the objective function computations are the same.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

scholarly journals Modeling, optimization and experimental studies of supported nano-bimetallic catalyst for simultaneous total conversion of toluene and cyclohexane in air using a hybrid intelligent algorithm

RSC Advances ◽  
2018 ◽  
Vol 8 (31) ◽  
pp. 17346-17356 ◽  
Author(s):  
Mohammad Zabihi ◽  
Nasser Babajani
Keyword(s):  
Activated Carbon ◽  
Bimetallic Catalysts ◽  
Bimetallic Catalyst ◽  
Experimental Studies ◽  
Deep Oxidation ◽  
Intelligent Algorithm ◽  
Almond Shell ◽  
Total Conversion ◽  
Hybrid Intelligent Algorithm

This study reveals the simultaneous deep oxidation of toluene and cyclohexane over optimal supported bimetallic catalysts over almond shell based activated carbon.

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