Journal articles: 'Mean-variance portfolio optimization'

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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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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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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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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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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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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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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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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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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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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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Goto, Shingo, and Yan Xu. "Improving Mean Variance Optimization through Sparse Hedging Restrictions." Journal of Financial and Quantitative Analysis 50, no. 6 (2015): 1415–41. http://dx.doi.org/10.1017/s0022109015000526.

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AbstractIn portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.

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Adhikari, Ramesh, Kyle J. Putnam, and Humnath Panta. "Robust Optimization-Based Commodity Portfolio Performance." International Journal of Financial Studies 8, no. 3 (2020): 54. http://dx.doi.org/10.3390/ijfs8030054.

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This paper examines the performance of a naïve equally weighted buy-and-hold portfolio and optimization-based commodity futures portfolios for various lookback and holding periods using data from January 1986 to December 2018. The application of Monte Carlo simulation-based mean-variance and conditional value-at-risk optimization techniques are used to construct the robust commodity futures portfolios. This paper documents the benefits of applying a sophisticated, robust optimization technique to construct commodity futures portfolios. We find that a 12-month lookback period contains the most useful information in constructing optimization-based portfolios, and a 1-month holding period yields the highest returns among all the holding periods examined in the paper. We also find that an optimized conditional value-at-risk portfolio using a 12-month lookback period outperforms an optimized mean-variance portfolio using the same lookback period. Our findings highlight the advantages of using robust optimization for portfolio formation in the presence of return uncertainty in the commodity futures markets. The results also highlight the practical importance of choosing the appropriate lookback and holding period when using robust optimization in the commodity portfolio formation process.

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Rhee, Dong-Woo, Hyoung-Goo Kang, and Soo-Hyun Kim. "Strategic Asset Allocation Of Credit Guarantors." Journal of Applied Business Research (JABR) 31, no. 5 (2015): 1823. http://dx.doi.org/10.19030/jabr.v31i5.9406.

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<p>How to manage the portfolio of credit guarantors is important in practice and public policy, but has not been investigated well in the prior literature. We empirically compare four different approaches in managing credit guarantor portfolios. The four approaches are equal weighted, minimum variance, mean variance optimization and equal risk contribution methods. In terms of risk return ratio, the mean variance optimization model performs best in out-of-sample test. This result contrasts with previous findings against mean variance optimization. Our results are robust. The results do not change as the characteristics of guarantee portfolio vary.</p>

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Dai, Zhifeng, and Fei Wang. "Sparse and robust mean–variance portfolio optimization problems." Physica A: Statistical Mechanics and its Applications 523 (June 2019): 1371–78. http://dx.doi.org/10.1016/j.physa.2019.04.151.

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Cui, Xueting, Shushang Zhu, Duan Li, and Jie Sun. "Mean–variance portfolio optimization with parameter sensitivity control†." Optimization Methods and Software 31, no. 4 (2016): 755–74. http://dx.doi.org/10.1080/10556788.2016.1181758.

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Çela, Eranda, Stephan Hafner, Roland Mestel, and Ulrich Pferschy. "Mean-variance portfolio optimization based on ordinal information." Journal of Banking & Finance 122 (January 2021): 105989. http://dx.doi.org/10.1016/j.jbankfin.2020.105989.

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Ankirchner, Stefan, and Azzouz Dermoune. "Multiperiod Mean-Variance Portfolio Optimization via Market Cloning." Applied Mathematics & Optimization 64, no. 1 (2011): 135–54. http://dx.doi.org/10.1007/s00245-011-9134-0.

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Salah, Hanene Ben, Ali Gannoun, and Mathieu Ribatet. "Conditional mean-variance and mean-semivariance models in portfolio optimization." Journal of Statistics and Management Systems 23, no. 8 (2020): 1333–56. http://dx.doi.org/10.1080/09720510.2020.1721931.

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Das, Sanjiv, Harry Markowitz, Jonathan Scheid, and Meir Statman. "Portfolio Optimization with Mental Accounts." Journal of Financial and Quantitative Analysis 45, no. 2 (2010): 311–34. http://dx.doi.org/10.1017/s0022109010000141.

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AbstractWe integrate appealing features of Markowitz’s mean-variance portfolio theory (MVT) and Shefrin and Statman’s behavioral portfolio theory (BPT) into a new mental accounting (MA) framework. Features of the MA framework include an MA structure of portfolios, a definition of risk as the probability of failing to reach the threshold level in each mental account, and attitudes toward risk that vary by account. We demonstrate a mathematical equivalence between MVT, MA, and risk management using value at risk (VaR). The aggregate allocation across MA subportfolios is mean-variance efficient with short selling. Short-selling constraints on mental accounts impose very minor reductions in certainty equivalents, only if binding for the aggregate portfolio, offsetting utility losses from errors in specifying risk-aversion coefficients in MVT applications. These generalizations of MVT and BPT via a unified MA framework result in a fruitful connection between investor consumption goals and portfolio production.

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Tuan Anh, Le, and Dao Thi Thanh Binh. "Portfolio optimization under mean-CVaR simulation with copulas on the Vietnamese stock exchange." Investment Management and Financial Innovations 18, no. 2 (2021): 273–86. http://dx.doi.org/10.21511/imfi.18(2).2021.22.

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This paper studies how to construct and compare various optimal portfolio frameworks for investors in the context of the Vietnamese stock market. The aim of the study is to help investors to find solutions for constructing an optimal portfolio strategy using modern investment frameworks in the Vietnamese stock market. The study contains a census of the top 43 companies listed on the Ho Chi Minh stock exchange (HOSE) over the ten-year period from July 2010 to January 2021. Optimal portfolios are constructed using Mean-Variance Framework, Mean-CVaR Framework under different copula simulations. Two-thirds of the data from 26/03/2014 to 27/1/2021 consists of the data of Vietnamese stocks during the COVID-19 recession, which caused depression globally; however, the results obtained during this period still provide a consistent outcome with the results for other periods. Furthermore, by randomly attempting different stocks in the research sample, the results also perform the same outcome as previous analyses. At about the same CvaR level of about 2.1%, for example, the Gaussian copula portfolio has daily Mean Return of 0.121%, the t copula portfolio has 0.12% Mean Return, while Mean-CvaR with the Raw Return portfolio has a lower Return at 0.103%, and the last portfolio of Mean-Variance with Raw Return has 0.102% Mean Return. Empirical results for all 10 portfolio levels showed that CVaR copula simulations significantly outperform the historical Mean-CVaR framework and Mean-Variance framework in the context of the Vietnamese stock exchange.

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Sun, Yen. "Optimization Stock Portfolio With Mean-Variance and Linear Programming: Case In Indonesia Stock Market." Binus Business Review 1, no. 1 (2010): 15. http://dx.doi.org/10.21512/bbr.v1i1.1018.

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It is observed that the number of Indonesia’s domestic investor who involved in the stock exchange is very less compare to its total number of population (only about 0.1%). As a result, Indonesia Stock Exchange (IDX) is highly affected by foreign investor that can threat the economy. Domestic investor tends to invest in risk-free asset such as deposit in the bank since they are not familiar yet with the stock market and anxious about the risk (risk-averse type of investor). Therefore, it is important to educate domestic investor to involve in the stock exchange. Investing in portfolio of stock is one of the best choices for risk-averse investor (such as Indonesia domestic investor) since it offers lower risk for a given level of return. This paper studies the optimization of Indonesian stock portfolio. The data is the historical return of 10 stocks of LQ 45 for 5 time series (January 2004 – December 2008). It will be focus on selecting stocks into a portfolio, setting 10 of stock portfolios using mean variance method combining with the linear programming (solver). Furthermore, based on Efficient Frontier concept and Sharpe measurement, there will be one stock portfolio picked as an optimum Portfolio (Namely Portfolio G). Then, Performance of portfolio G will be evaluated by using Sharpe, Treynor and Jensen Measurement to show whether the return of Portfolio G exceeds the market return. This paper also illustrates how the stock composition of the Optimum Portfolio (G) succeeds to predict the portfolio return in the future (5th January – 3rd April 2009). The result of the study observed that optimization portfolio using Mean-Variance (consistent with Markowitz theory) combine with linear programming can be applied into Indonesia stock’s portfolio. All the measurements (Sharpe, Jensen, and Treynor) show that the portfolio G is a superior portfolio. It is also been found that the composition (weights) stocks of optimum portfolio (G) can be used to predict the forward return (5th January – 3rd April 2009). It is shown that the stock portfolio return of 5th January – 3rd April 2009) has exceeded the market return for the same period of time based on Sharpe and Treynor measurement.

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KONNO, HIROSHI, and REI YAMAMOTO. "A MEAN-VARIANCE-SKEWNESS MODEL: ALGORITHM AND APPLICATIONS." International Journal of Theoretical and Applied Finance 08, no. 04 (2005): 409–23. http://dx.doi.org/10.1142/s0219024905003116.

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We will show that a mean-variance-skewness portfolio optimization model, a direct extension of the classical mean-variance model can be solved exactly and fast by using the state-of-the-art integer programming approach. This implies that we can now calculate a portfolio with maximal expected utility for any decreasing risk averse utility function. Also, we will show that this model can be used as a practical tool for constructing a portfolio when the asset returns follow skewed distribution. As an example, we apply this model to construct an index plus alpha portfolio.

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Shi, Yu, Xia Zhao, Fengwei Jiang, and Yipin Zhu. "Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios." Mathematical Problems in Engineering 2020 (July 15, 2020): 1–11. http://dx.doi.org/10.1155/2020/2767231.

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This paper aims to study stable portfolios with mean-variance-CVaR criteria for high-dimensional data. Combining different estimators of covariance matrix, computational methods of CVaR, and regularization methods, we construct five progressive optimization problems with short selling allowed. The impacts of different methods on out-of-sample performance of portfolios are compared. Results show that the optimization model with well-conditioned and sparse covariance estimator, quantile regression computational method for CVaR, and reweighted L1 norm performs best, which serves for stabilizing the out-of-sample performance of the solution and also encourages a sparse portfolio.

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Santos, André Alves Portela. "Desempenho Fora-da-Amostra da Otimização Robusta de Carteiras." Brazilian Review of Finance 8, no. 2 (2010): 141. http://dx.doi.org/10.12660/rbfin.v8n2.2010.1489.

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Robust optimization has been receiving increased attention in the recent few years due to the possibility of considering the problem of estimation error in the portfolio optimization problem. A question addressed so far by very few works is whether this approach is able to outperform traditional portfolio optimization techniques in terms of out-of-sample performance. Moreover, it is important to know whether this approach is able to deliver stable portfolio compositions over time, thus reducing management costs and facilitating practical implementation. We provide empirical evidence by assessing the out-of-sample performance and the stability of optimal portfolio compositions obtained with robust optimization and with traditional optimization techniques. The results indicated that, for simulated data, robust optimization performed better (both in terms of Sharpe ratios and portfolio turnover) than Markowitz's mean-variance portfolios and similarly to minimum-variance portfolios. The results for real market data indicated that the differences in risk-adjusted performance were not statistically different, but the portfolio compositions associated to robust optimization were more stable over time than traditional portfolio selection techniques.

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Fague, Jeremy, and Caio Almeida. "Robust optimization of time series momentum portfolios." Brazilian Review of Finance 19, no. 1 (2021): 52–69. http://dx.doi.org/10.12660/rbfin.v19n1.2021.82045.

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Mean-Variance Optimization (MVO) is well-known to be extremely sensitive to slight differences in the expected returns and covariances: if these measures change day to day, MVO can specify very different portfolios. Making wholesale changes in portfolio composition can cause the incremental gains to be negated by trading costs. We present a method for regularizing portfolio turnover by using the ℓ1 penalty, with the amount of penalization informed by recent historical data. We find that this method dramatically reduces turnover, while preserving the efficiency of mean-variance optimization in terms of risk-adjusted return. Factoring in reasonable estimates of transaction costs, the turnover-regularized MVO portfolio substantially outperforms a leverage-constrained MVO approach, in terms of risk-adjusted return.

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Fischer, Markus, and Giulia Livieri. "Continuous time mean-variance portfolio optimization through the mean field approach." ESAIM: Probability and Statistics 20 (2016): 30–44. http://dx.doi.org/10.1051/ps/2016001.

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García, Sandra, David Quintana, Inés M. Galván, and Pedro Isasi. "Extended mean–variance model for reliable evolutionary portfolio optimization." AI Communications 27, no. 3 (2014): 315–24. http://dx.doi.org/10.3233/aic-140600.

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Björk, Tomas, Agatha Murgoci, and Xun Yu Zhou. "MEAN-VARIANCE PORTFOLIO OPTIMIZATION WITH STATE-DEPENDENT RISK AVERSION." Mathematical Finance 24, no. 1 (2012): 1–24. http://dx.doi.org/10.1111/j.1467-9965.2011.00515.x.

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Gao, Jianjun, and Duan Li. "Multiperiod Mean-Variance Portfolio Optimization with General Correlated Returns." IFAC Proceedings Volumes 47, no. 3 (2014): 9007–12. http://dx.doi.org/10.3182/20140824-6-za-1003.01347.

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Yanushevsky, Rafael, and Daniel Yanushevsky's. "An approach to improve mean-variance portfolio optimization model." Journal of Asset Management 16, no. 3 (2015): 209–19. http://dx.doi.org/10.1057/jam.2015.13.

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Georgiev, Boris. "Constrained Mean-Variance Portfolio Optimization with Alternative Return Estimation." Atlantic Economic Journal 42, no. 1 (2014): 91–107. http://dx.doi.org/10.1007/s11293-013-9400-4.

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SAGHIR, AHSEN, and SYED MUHAMMAD ALI TIRMIZI. "An Empirical Assessment of Alternative Methods of Variance-Covariance Matrix." International Review of Management and Business Research 9, no. 4 (2020): 390–401. http://dx.doi.org/10.30543/9-4(2020)-33.

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The current study aims at the estimation of a group of variance-covariance methods using the data set of the non-financial sector of the Pakistan stock exchange. The study compares nine covariance estimators using two assessment criteria of root mean square error and standard deviation of minimum variance portfolios to gauge on accuracy and effectiveness of estimators. The findings of the study based on RMSE and risk behaviour of MVPs suggest that portfolio managers receive no additional benefit for using more sophisticated measures against equally weighted variance-covariance estimators in the construction of portfolios. Keywords: Variance-Covariance Estimators, Portfolio Construction, Mean-Variance Optimization.

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Fernandez, Pedro Jesus, Marcelo de Souza Lauretto, Carlos Alberto de Bragança Pereira, and Julio Michael Stern. "A new media optimizer based on the mean-variance model." Pesquisa Operacional 27, no. 3 (2007): 427–56. http://dx.doi.org/10.1590/s0101-74382007000300003.

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In the financial markets, there is a well established portfolio optimization model called generalized mean-variance model (or generalized Markowitz model). This model considers that a typical investor, while expecting returns to be high, also expects returns to be as certain as possible. In this paper we introduce a new media optimization system based on the mean-variance model, a novel approach in media planning. After presenting the model in its full generality, we discuss possible advantages of the mean-variance paradigm, such as its flexibility in modeling the optimization problem, its ability of dealing with many media performance indices - satisfying most of the media plan needs - and, most important, the property of diversifying the media portfolios in a natural way, without the need to set up ad hoc constraints to enforce diversification.

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van Staden, Pieter M., Duy-Minh Dang, and Peter A. Forsyth. "Mean-Quadratic Variation Portfolio Optimization: A Desirable Alternative to Time-Consistent Mean-Variance Optimization?" SIAM Journal on Financial Mathematics 10, no. 3 (2019): 815–56. http://dx.doi.org/10.1137/18m1222570.

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Kirby, Chris, and Barbara Ostdiek. "It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification." Journal of Financial and Quantitative Analysis 47, no. 2 (2012): 437–67. http://dx.doi.org/10.1017/s0022109012000117.

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AbstractDeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and extreme turnover. We find that mean-variance optimization often outperforms naïve diversification, but turnover can erode its advantage in the presence of transaction costs. To address this issue, we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.

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Fernández-Navarro, Francisco, Luisa Martínez-Nieto, Mariano Carbonero-Ruz, and Teresa Montero-Romero. "Mean Squared Variance Portfolio: A Mixed-Integer Linear Programming Formulation." Mathematics 9, no. 3 (2021): 223. http://dx.doi.org/10.3390/math9030223.

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The mean-variance (MV) portfolio is typically formulated as a quadratic programming (QP) problem that linearly combines the conflicting objectives of minimizing the risk and maximizing the expected return through a risk aversion profile parameter. In this formulation, the two objectives are expressed in different units, an issue that could definitely hamper obtaining a more competitive set of portfolio weights. For example, a modification in the scale in which returns are expressed (by one or percent) in the MV portfolio, implies a modification in the solution of the problem. Motivated by this issue, a novel mean squared variance (MSV) portfolio is proposed in this paper. The associated optimization problem of the proposed strategy is very similar to the Markowitz optimization, with the exception of the portfolio mean, which is presented in squared form in our formulation. The resulting portfolio model is a non-convex QP problem, which has been reformulated as a mixed-integer linear programming (MILP) problem. The reformulation of the initial non-convex QP problem into an MILP allows for future researchers and practitioners to obtain the global solution of the problem via the use of current state-of-the-art MILP solvers. Additionally, a novel purely data-driven method for determining the optimal value of the hyper-parameter that is associated with the MV and MSV approaches is also proposed in this paper. The MSV portfolio has been empirically tested on eight portfolio time series problems with three different estimation windows (composing a total of 24 datasets), showing very competitive performance in most of the problems.

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Alrabadi, Dima Waleed Hanna. "Portfolio optimization using the generalized reduced gradient nonlinear algorithm." International Journal of Islamic and Middle Eastern Finance and Management 9, no. 4 (2016): 570–82. http://dx.doi.org/10.1108/imefm-06-2015-0071.

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Purpose This study aims to utilize the mean–variance optimization framework of Markowitz (1952) and the generalized reduced gradient (GRG) nonlinear algorithm to find the optimal portfolio that maximizes return while keeping risk at minimum. Design/methodology/approach This study applies the portfolio optimization concept of Markowitz (1952) and the GRG nonlinear algorithm to a portfolio consisting of the 30 leading stocks from the three different sectors in Amman Stock Exchange over the period from 2009 to 2013. Findings The selected portfolios achieve a monthly return of 5 per cent whilst keeping risk at minimum. However, if the short-selling constraint is relaxed, the monthly return will be 9 per cent. Moreover, the GRG nonlinear algorithm enables to construct a portfolio with a Sharpe ratio of 7.4. Practical implications The results of this study are vital to both academics and practitioners, specifically the Arab and Jordanian investors. Originality/value To the best of the author’s knowledge, this is the first study in Jordan and in the Arab world that constructs optimum portfolios based on the mean–variance optimization framework of Markowitz (1952) and the GRG nonlinear algorithm.

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Santos, André Alves Portela, and Cristina Tessari. "Técnicas Quantitativas de Otimização de Carteiras Aplicadas ao Mercado de Ações Brasileiro." Brazilian Review of Finance 10, no. 3 (2012): 369. http://dx.doi.org/10.12660/rbfin.v10n3.2012.3865.

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In this paper we assess the out-of-sample performance of two alternative quantitative portfolio optimization techniques - mean-variance and minimum variance optimization – and compare their performance with respect to a naive 1/N (or equally-weighted) portfolio and also to the market portfolio given by the Ibovespa. We focus on short selling-constrained portfolios and consider alternative estimators for the covariance matrices: sample covariance matrix, RiskMetrics, and three covariance estimators proposed by Ledoit and Wolf (2003), Ledoit and Wolf (2004a) and Ledoit and Wolf (2004b). Taking into account alternative portfolio re-balancing frequencies, we compute out-of-sample performance statistics which indicate that the quantitative approaches delivered improved results in terms of lower portfolio volatility and better risk-adjusted returns. Moreover, the use of more sophisticated estimators for the covariance matrix generated optimal portfolios with lower turnover over time.

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Mussafi, Noor Saif Muhammad. "Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan Dengan Keterbatasan Manusia Dalam Memprediksi Masa Depan Dalam Perspektif Al-Qur`an." Jurnal Fourier 1, no. 1 (2012): 27. http://dx.doi.org/10.14421/fourier.2012.11.27-35.

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Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function 𝑄𝑥with constraints𝜇 𝑇 𝑥 ≥ 𝑅and𝐴𝑥 = 𝑏. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis.

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Jadhav, Deepak, and T. V. Ramanathan. "Portfolio optimization based on modified expected shortfall." Studies in Economics and Finance 36, no. 3 (2019): 440–63. http://dx.doi.org/10.1108/sef-05-2018-0160.

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Purpose An investor is expected to analyze the market risk while investing in equity stocks. This is because the investor has to choose a portfolio which maximizes the return with a minimum risk. The mean-variance approach by Markowitz (1952) is a dominant method of portfolio optimization, which uses variance as a risk measure. The purpose of this paper is to replace this risk measure with modified expected shortfall, defined by Jadhav et al. (2013). Design/methodology/approach Modified expected shortfall introduced by Jadhav et al. (2013) is found to be a coherent risk measure under univariate and multivariate elliptical distributions. This paper presents an approach of portfolio optimization based on mean-modified expected shortfall for the elliptical family of distributions. Findings It is proved that the modified expected shortfall of a portfolio can be represented in the form of expected return and standard deviation of the portfolio return and modified expected shortfall of standard elliptical distribution. The authors also establish that the optimum portfolio through mean-modified expected shortfall approach exists and is located within the efficient frontier of the mean-variance portfolio. The results have been empirically illustrated using returns from stocks listed in National Stock Exchange of India, Shanghai Stock Exchange of China, London Stock Exchange of the UK and New York Stock Exchange of the USA for the period February 2005-June 2018. The results are found to be consistent across all the four stock markets. Originality/value The mean-modified expected shortfall portfolio approach presented in this paper is new and is a natural extension of the Markowitz’s mean-variance and mean-expected shortfall portfolio optimization discussed by Deng et al. (2009).

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Keykhaei, Reza, and Bardia Panahbehagh. "Static Mean-Variance portfolio optimization under general sources of uncertainty." Pakistan Journal of Statistics and Operation Research 14, no. 2 (2018): 387. http://dx.doi.org/10.18187/pjsor.v14i2.1963.

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Costa, Oswaldo L. V., and Michael V. Araujo. "Multi-period mean-variance portfolio optimization with markov switching parameters." Sba: Controle & Automação Sociedade Brasileira de Automatica 19, no. 2 (2008): 138–46. http://dx.doi.org/10.1590/s0103-17592008000200003.

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In this paper we deal with a multi-period mean-variance portfolio selection problem with the market parameters subject to Markov random regime switching. We analytically derive an optimal control policy for this mean-variance formulation in a closed form. Such a policy is obtained from a set of interconnected Riccati difference equations. Additionally, an explicit expression for the efficient frontier corresponding to this control law is identified and numerical examples are presented.

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Ötken, Çelen N., Z. Batuhan Organ, E. Ceren Yıldırım, et al. "An extension to the classical mean–variance portfolio optimization model." Engineering Economist 64, no. 3 (2019): 310–21. http://dx.doi.org/10.1080/0013791x.2019.1636440.

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Lai, Tze Leung, Haipeng Xing, and Zehao Chen. "Mean–variance portfolio optimization when means and covariances are unknown." Annals of Applied Statistics 5, no. 2A (2011): 798–823. http://dx.doi.org/10.1214/10-aoas422.

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Corsaro, Stefania, Valentina De Simone, and Zelda Marino. "Split Bregman iteration for multi-period mean variance portfolio optimization." Applied Mathematics and Computation 392 (March 2021): 125715. http://dx.doi.org/10.1016/j.amc.2020.125715.

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Siqi, Zhao. "MEAN VARIANCE PORTFOLIO OPTIMIZATION - INSIGHTS DURING THE COVID-19 PERIOD." European Journal of Economics and Management Sciences, no. 2 (2021): 68–75. http://dx.doi.org/10.29013/ejems-21-2-68-76.

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Glensk, Barbara, and Reinhard Madlener. "Fuzzy Portfolio Optimization of Power Generation Assets." Energies 11, no. 11 (2018): 3043. http://dx.doi.org/10.3390/en11113043.

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Fuzzy theory is proposed as an alternative to the probabilistic approach for assessing portfolios of power plants, in order to capture the complex reality of decision-making processes. This paper presents different fuzzy portfolio selection models, where the rate of returns as well as the investor’s aspiration levels of portfolio return and risk are regarded as fuzzy variables. Furthermore, portfolio risk is defined as a downside risk, which is why a semi-mean-absolute deviation portfolio selection model is introduced. Finally, as an illustration, the models presented are applied to a selection of power generation mixes. The efficient portfolio results show that the fuzzy portfolio selection models with different definitions of membership functions as well as the semi-mean-absolute deviation model perform better than the standard mean-variance approach. Moreover, introducing membership functions for the description of investors’ aspiration levels for the expected return and risk shows how the knowledge of experts, and investors’ subjective opinions, can be better integrated in the decision-making process than with probabilistic approaches.

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Wenzelburger, Jan. "Mean-variance analysis and the Modified Market Portfolio." Journal of Economic Dynamics and Control 111 (February 2020): 103821. http://dx.doi.org/10.1016/j.jedc.2019.103821.

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Xiong, Jie, Yong Zeng, and Shuaiqi Zhang. "Mean-Variance Portfolio Selection for Partially Observed Point Processes." SIAM Journal on Control and Optimization 58, no. 6 (2020): 3041–61. http://dx.doi.org/10.1137/19m1265491.

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Gouglas, Dimitrios, and Kevin Marsh. "Prioritizing investments in rapid response vaccine technologies for emerging infections: A portfolio decision analysis." PLOS ONE 16, no. 2 (2021): e0246235. http://dx.doi.org/10.1371/journal.pone.0246235.

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This study reports on the application of a Portfolio Decision Analysis (PDA) to support investment decisions of a non-profit funder of vaccine technology platform development for rapid response to emerging infections. A value framework was constructed via document reviews and stakeholder consultations. Probability of Success (PoS) data was obtained for 16 platform projects through expert assessments and stakeholder portfolio preferences via a Discrete Choice Experiment (DCE). The structure of preferences and the uncertainties in project PoS suggested a non-linear, stochastic value maximization problem. A simulation-optimization algorithm was employed, identifying optimal portfolios under different budget constraints. Stochastic dominance of the optimization solution was tested via mean-variance and mean-Gini statistics, and its robustness via rank probability analysis in a Monte Carlo simulation. Project PoS estimates were low and substantially overlapping. The DCE identified decreasing rates of return to investing in single platform types. Optimal portfolio solutions reflected this non-linearity of platform preferences along an efficiency frontier and diverged from a model simply ranking projects by PoS-to-Cost, despite significant revisions to project PoS estimates during the review process in relation to the conduct of the DCE. Large confidence intervals associated with optimization solutions suggested significant uncertainty in portfolio valuations. Mean-variance and Mean-Gini tests suggested optimal portfolios with higher expected values were also accompanied by higher risks of not achieving those values despite stochastic dominance of the optimal portfolio solution under the decision maker’s budget constraint. This portfolio was also the highest ranked portfolio in the simulation; though having only a 54% probability of being preferred to the second-ranked portfolio. The analysis illustrates how optimization modelling can help health R&D decision makers identify optimal portfolios in the face of significant decision uncertainty involving portfolio trade-offs. However, in light of such extreme uncertainty, further due diligence and ongoing updating of performance is needed on highly risky projects as well as data on decision makers’ portfolio risk attitude before PDA can conclude about optimal and robust solutions.

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

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