Relevant bibliographies by topics / Mean-variance portfolio optimization

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Academic literature on the topic 'Mean-variance portfolio optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Mean-variance portfolio optimization"

1

Xu, Jonathan. "MEAN VARIANCE PORTFOLIO OPTIMIZATION." European Journal of Economics and Management Sciences, no. 2 (2021): 76–81. http://dx.doi.org/10.29013/ejems-21-2-76-81.

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2

Jena, R. K. "Extended Mean - Variance Portfolio Optimization Model: A Comparative Study Among Swarm Intelligence Algorithms." International Journal of Accounting and Financial Reporting 9, no. 2 (2019): 184. http://dx.doi.org/10.5296/ijafr.v9i2.14601.

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Portfolio optimization is one of the important issues in the effective management of investment. There is plenty of research in the literature addressing these issues. Markowitz’s primary portfolio selection model is a more suitable method to solve the model for obtaining fairly optimum portfolios. But, the problem of portfolio optimization is multi-objective in nature that aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio risk. The computational complexity increases with an increase in the total number of available assets. Therefore heuristic methods are more suitable for portfolio optimization in compare to deterministic methods. This research compares three well-known swarm intelligence algorithms (e.g. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC)) for portfolio optimization. The Sharpe ratio was used as one of the important criteria for this comparison. PSO outperformed other algorithms in portfolio optimization experiments. The results were also showed that the portfolios which were made of monthly data had performed better than the yearly data.
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3

Labbé, Chantal, and Andrew J. Heunis. "Convex duality in constrained mean-variance portfolio optimization." Advances in Applied Probability 39, no. 01 (2007): 77–104. http://dx.doi.org/10.1017/s0001867800001610.

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We apply conjugate duality to establish the existence of optimal portfolios in an asset-allocation problem, with the goal of minimizing the variance of the final wealth which results from trading over a fixed, finite horizon in a continuous-time, complete market, subject to the constraints that the expected final wealth equal a specified target value and the portfolio of the investor (defined by the dollar amount invested in each stock) take values in a given closed, convex set. The asset prices are modelled by Itô processes, for which the market parameters are random processes adapted to the information filtration available to the investor. We synthesize a dual optimization problem and establish a set of optimality relations, similar to the Euler-Lagrange and transversality relations of calculus of variations, giving necessary and sufficient conditions for the given optimization problem and its dual to each have a solution, with zero duality gap. We then solve these relations, to establish the existence of an optimal portfolio.
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4

Labbé, Chantal, and Andrew J. Heunis. "Convex duality in constrained mean-variance portfolio optimization." Advances in Applied Probability 39, no. 1 (2007): 77–104. http://dx.doi.org/10.1239/aap/1175266470.

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We apply conjugate duality to establish the existence of optimal portfolios in an asset-allocation problem, with the goal of minimizing the variance of the final wealth which results from trading over a fixed, finite horizon in a continuous-time, complete market, subject to the constraints that the expected final wealth equal a specified target value and the portfolio of the investor (defined by the dollar amount invested in each stock) take values in a given closed, convex set. The asset prices are modelled by Itô processes, for which the market parameters are random processes adapted to the information filtration available to the investor. We synthesize a dual optimization problem and establish a set of optimality relations, similar to the Euler-Lagrange and transversality relations of calculus of variations, giving necessary and sufficient conditions for the given optimization problem and its dual to each have a solution, with zero duality gap. We then solve these relations, to establish the existence of an optimal portfolio.
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5

Konno, Hiroshi, and Ken-ichi Suzuki. "A MEAN-VARIANCE-SKEWNESS PORTFOLIO OPTIMIZATION MODEL." Journal of the Operations Research Society of Japan 38, no. 2 (1995): 173–87. http://dx.doi.org/10.15807/jorsj.38.173.

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6

Varga-Haszonits, Istvan, Fabio Caccioli, and Imre Kondor. "Replica approach to mean-variance portfolio optimization." Journal of Statistical Mechanics: Theory and Experiment 2016, no. 12 (2016): 123404. http://dx.doi.org/10.1088/1742-5468/aa4f9c.

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7

Tayalı, Halit Alper, and Seda Tolun. "Dimension reduction in mean-variance portfolio optimization." Expert Systems with Applications 92 (February 2018): 161–69. http://dx.doi.org/10.1016/j.eswa.2017.09.009.

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8

Et. al., Adil Moghara. "Mean- Adjusted Variance Model for Portfolio Optimization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 903–17. http://dx.doi.org/10.17762/turcomat.v12i5.1733.

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This paper proposes an operational founded model for portfolio optimization. The procedure used is based on the redacting ofthe asymmetry impact of the variance. This is a new approach that givesassets more accurate risk measures. The risk adjustment is based on the measure of volatility skewness andthe goal here is to eliminate noisy risk.Moreover, we give our risk asymmetrical effect,according to the means of each asset.
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9

Elahi, Younes, and Mohd Ismail Abd Aziz. "Mean-Variance-CvaR Model of Multiportfolio Optimization via Linear Weighted Sum Method." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/104064.

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We propose a new approach to optimizing portfolios to mean-variance-CVaR (MVC) model. Although of several researches have studied the optimal MVC model of portfolio, the linear weighted sum method (LWSM) was not implemented in the area. The aim of this paper is to investigate the optimal portfolio model based on MVC via LWSM. With this method, the solution of the MVC model of portfolio as the multiobjective problem is presented. In data analysis section, this approach in investing on two assets is investigated. An MVC model of the multiportfolio was implemented in MATLAB and tested on the presented problem. It is shown that, by using three objective functions, it helps the investors to manage their portfolio better and thereby minimize the risk and maximize the return of the portfolio. The main goal of this study is to modify the current models and simplify it by using LWSM to obtain better results.
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10

Mercurio, Peter Joseph, Yuehua Wu, and Hong Xie. "An Entropy-Based Approach to Portfolio Optimization." Entropy 22, no. 3 (2020): 332. http://dx.doi.org/10.3390/e22030332.

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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.
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