scholarly journals A Firefly Algorithm for Portfolio Optimization

2019 ◽  
Vol 25 (3) ◽  
pp. 282-291
Author(s):  
Indana Lazulfa
Keyword(s):  
Financial Markets ◽  
Portfolio Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Risk Measures ◽  
Variance Model ◽  
Multi Objective ◽  
Risk Return ◽  
Portfolio Optimization Model ◽  
Mean Variance

Portfolio optimization is the process of allocating capital among a universe of assets to achieve better risk – return trade-off. Portfolio optimization is a solution for investors to get the return as large as possible and make the risk as small as possible. Due to the dynamic nature of financial markets, the portfolio needs to be rebalanced to retain the desired risk-return characteristics. This study proposed multi objective portfolio optimization model with risk, return as the objective function. For multi objective portfolio optimization problems will be used mean-variance model as risk measures. All these portfolio optimization problems will be solved by Firefly Algorithm (FA).

Portfolio optimization with transaction costs: a two-period mean-variance model

2014 ◽  
Vol 233 (1) ◽  
pp. 135-156 ◽  
Author(s):  
Ying Hui Fu ◽  
Kien Ming Ng ◽  
Boray Huang ◽  
Huei Chuen Huang
Keyword(s):  
Transaction Costs ◽  
Portfolio Optimization ◽  
Variance Model ◽  
Mean Variance

A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

2017 ◽  
pp. 491-521 ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
Aboul Ella Hassanien
Keyword(s):  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

scholarly journals Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrea Rigamonti
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measures ◽  
Traditional Approach ◽  
Real Data ◽  
Downside Risk ◽  
Good Choice ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Allocation Decisions

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified.

Portfolio Optimization Model for Asset Allocation Problem Based on Alternative Risk Measures

2021 ◽  
pp. 673-693
Author(s):  
Anna Andreevna Malakhova ◽  
Elena Nikolaevna Sochneva ◽  
Svetlana Anatolyevna Yarkova ◽  
Anastasiya Vladimirovna Yarkova ◽  
Olga Valeryevna Starova ◽  
...  
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Optimization Model ◽  
Risk Measures ◽  
Allocation Problem ◽  
Portfolio Optimization Model

scholarly journals An Entropy-Based Approach to Portfolio Optimization

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 332 ◽  
Author(s):  
Peter Joseph Mercurio ◽  
Yuehua Wu ◽  
Hong Xie
Keyword(s):  
Objective Function ◽  
Portfolio Optimization ◽  
Historical Data ◽  
Optimization Problems ◽  
Improved Method ◽  
Financial Industry ◽  
New Family ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance

This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with the mean-variance portfolio optimization (MVPO). Pioneered by Harry Markowitz, MVPO revolutionized the financial industry as the first formal mathematical approach to risk-averse investing. REPO uses a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. REPO and MVPO were compared by emulating competing portfolios over historical data and REPO significantly outperformed MVPO in a strong majority of cases.

Firefly Algorithm for Optimization Problem

2013 ◽  
Vol 421 ◽  
pp. 512-517 ◽  
Author(s):  
Nur Farahlina Johari ◽  
Azlan Mohd Zain ◽  
Mustaffa H. Noorfa ◽  
Amirmudin Udin
Keyword(s):  
Computer Science ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Science And Engineering ◽  
Multi Objective

This paper reviews the applications of Firefly Algorithm (FA) in various domain of optimization problem. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. Several categories of optimization problem such as discrete, chaotic, multi-objective and many more are addressed by inspiring the behavior of fireflies as mentioned in the literatures. Literatures found that FA was mostly applied by researchers to solve the optimization problems in Computer Science and Engineering domain. Some of them are enhanced or hybridized with other techniques to discover better performance. In addition, literatures found that most of the cases that used FA technique have outperformed compare to other metaheuristic algorithms.

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