Journal articles: 'Mean-Variance Portfolio'

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Vanti, Eka Nur, and Epha Diana Supandi. "Pembentukan Portofolio Optimal dengan Menggunakan Mean Absolute Deviation dan Conditional Mean Variance." Jurnal Fourier 9, no. 1 (April 30, 2020): 25–34. http://dx.doi.org/10.14421/fourier.2020.91.25-34.

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Penelitian ini membahas tentang pembentukan portofolio optimal menggunakan model Mean Absolute Deviation (MAD) dan model Conditional Mean Variance (CMV). Pada model MAD risiko portofolio diukur menggunakan rata–rata deviasi standar sehingga portofolio optimal dapat diperoleh dengan menggunakan pemrograman linear. Sedangkan portofolio model CMV, rata–rata return diestimasi menggunakan model Autoregressive (AR) dan risiko (variansi) diestimasi menggunakan model GARCH. Selanjutnya kedua model portofolio diterapkan dalam membentuk portofolio optimal pada saham–saham yang terdaftar dalam Indeks Saham Syariah Indonesia (ISSI) periode 4 Juli 2016 sampai 4 Juli 2018. Kinerja kedua portofolio dianalisis menggunakan indeks Sortino. Hasilnya menunjukan bahwa kinerja portofolio model CMV lebih baik dibandingkan model portofolio MAD. [This study discusses the formation of optimal portfolios using the Mean Absolute Deviation (MAD) model and the Conditional Mean Variance (CMV) model. The MAD portfolio model measures portfolio risk by using average standard deviations so that optimal portfolios solved by using linear programming. Meanwhile the CMV portfolio model, the average return estimated by using the Autoregressive (AR) model and the risk (variance) estimated by using the GARCH model. Furthermore, both portfolio models applied in forming optimal portfolios for stocks listed in the Indonesian Syariah Stock Index (ISSI) for the period 4 July 2016 to 4 July 2018. The performance of both portfolios analyzed by using the Sortino index. The results show that the portfolio performance of the CMV model is better than MAD portfolio model.]

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Kumar, Ronald Ravinesh, Peter Josef Stauvermann, and Aristeidis Samitas. "An Application of Portfolio Mean-Variance and Semi-Variance Optimization Techniques: A Case of Fiji." Journal of Risk and Financial Management 15, no. 5 (April 19, 2022): 190. http://dx.doi.org/10.3390/jrfm15050190.

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In this paper, we apply the Markowitz portfolio optimization technique based on mean-variance and semi-variance as measures of risk on stocks listed on the South Pacific Stock Exchange, Fiji. We document key market characteristics and consider monthly returns data from SEP-2019 to FEB-2022 (T = 30) of 17/19 listed companies on the stock exchange to construct various portfolios like 1/N (naïve), maximum return, and market and minimum-variance with and without short-selling constraints. Additionally, we compute each stock’s beta using the market capitalization-weighted stock price index data. We note that well-diversified portfolios (market portfolio and minimum-variance portfolio) with short-selling constraints have relatively higher expected returns with lower risk. Moreover, well-diversified portfolios perform better than the naïve and maximum portfolios in terms of risk. Moreover, we find that both the mean-variance and the semi-variance measures of risk yields a unique market portfolio in terms of expected returns, although the latter has a lower standard deviation and a higher Sharpe ratio. However, for the minimum-variance portfolios and market portfolios without short selling, we find relatively higher returns and risks using the mean-variance than the semi-variance approach. The low beta of individual stock indicates the low sensitivity of its price to the movement of the market index. The study is an initial attempt to provide potential investors with some practical strategies and tools in developing a diversified portfolio. Since not all the portfolios based on mean-variance and the semi-variance analyses are unique, additional methods of investment analysis and portfolio construction are recommended. Subsequently, for investment decisions, our analysis can be complemented with additional measures of risk and an in-depth financial statement/company performance analysis.

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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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Lefebvre, William, Grégoire Loeper, and Huyên Pham. "Mean-Variance Portfolio Selection with Tracking Error Penalization." Mathematics 8, no. 11 (November 1, 2020): 1915. http://dx.doi.org/10.3390/math8111915.

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This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in the case of misspecified parameters, by “fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean–Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.

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Deng, Zhixiang, and Yujia Han. "The Application of ARIMA and Mean-variance Models on Financial Market." BCP Business & Management 26 (September 19, 2022): 1051–57. http://dx.doi.org/10.54691/bcpbm.v26i.2069.

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This study centers on forecasting return and constructing proper portfolios with 5 typical assets rarely focused on the Chinese A-share market. This paper applies the fittest ARIMA models for each of the selected stocks to predict their trend of returns in the next 20 days. Besides, we create the efficient frontier by Monte Carlo simulation under Markowitz’s Mean-Variance framework to focus on two portfolios, i.e., the maximum Sharpe ratio portfolio and the minimum volatility portfolio. The empirical results of the ARIMA model indicate a rational prediction of return for assets in the A-share market. The maximum Sharpe ratio portfolio and the minimum volatility portfolio show that stock of Foshan Haitian Flavouring and Food Company Ltd. and stock of China Merchants Bank Co., Ltd. account for the largest proportion in the two portfolios. Further empirical results show that returns for two portfolios are higher than the market index return, which illuminates the two portfolios outperform the market index. The results in this paper will surely benefit related investors in the financial market.

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Vasant, Jiten, Laurent Irgolic, Ryan Kruger, and Kanshukan Rajaratnam. "A Comparison Of Mean-Variance And Mean-Semivariance Optimisation On The JSE." Journal of Applied Business Research (JABR) 30, no. 6 (October 21, 2014): 1587. http://dx.doi.org/10.19030/jabr.v30i6.8876.

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This study investigates the effectiveness of semivariance versus mean-variance optimisation on a risk-adjusted basis on the JSE. We compare semivariance and mean-variance optimisation prior to, during and after the recent financial crisis period. Additionally, we investigate the inclusion of a fixed-income asset in the optimal portfolio. The results suggest that semivariance optimisation on the JSE in a pure equity case produces lower absolute returns, yet superior risk-adjusted returns. Further investigation suggests that semivariance metrics are effective within a certain range of portfolio sizes and diminishes in benefit once portfolios become larger. A fixed income asset scenario tested under the hypothesis of semivariance optimisation favoured greater bond weightings in optimal portfolios.

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Fontana, Claudio, and Martin Schweizer. "Simplified mean-variance portfolio optimisation." Mathematics and Financial Economics 6, no. 2 (April 3, 2012): 125–52. http://dx.doi.org/10.1007/s11579-012-0067-4.

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Pedersen, Jesper Lund, and Goran Peskir. "Optimal mean-variance portfolio selection." Mathematics and Financial Economics 11, no. 2 (June 20, 2016): 137–60. http://dx.doi.org/10.1007/s11579-016-0174-8.

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9

Bi, Junna, Hanqing Jin, and Qingbin Meng. "Behavioral mean-variance portfolio selection." European Journal of Operational Research 271, no. 2 (December 2018): 644–63. http://dx.doi.org/10.1016/j.ejor.2018.05.065.

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10

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 (April 15, 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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Gusliana, Shindi Adha, and Yasir Salih. "MEAN-VARIANCE INVESTMENT PORTFOLIO OPTIMIZATION MODEL WITHOUT RISK-FREE ASSETS IN JII70 SHARE." International Journal of Business, Economics, and Social Development 3, no. 4 (November 4, 2022): 168–73. http://dx.doi.org/10.46336/ijbesd.v3i4.352.

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In investing, investors will try to limit all the risks in managing their investments. Investor strategies to minimize investment risk are diversification by forming investment portfolios, one of which is the Mean-Variance without risk-free assets. The calculation results will show the composition of the optimum portfolio return for each stock that forms the portfolio. Optimum portfolio obtained with wT = (0.39853, 0.25519, 0.13644, 0.09788, 0.11196) sequential weight composition for TLKM, KLBF, INCO, HRUM, and FILM stocks. The composition of this optimal portfolio return is ???? 0.04 with a return of 0.00209 and a portfolio variance of 0.00015. The formation of this portfolio optimization model is expected to be additional literature in optimizing the investment portfolio with the Mean-Variance.

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Gusliana, Shindi Adha, and Yasir Salih. "Mean-Variance Investment Portfolio Optimization Model Without Risk-Free Assets in Jii70 Share." Operations Research: International Conference Series 3, no. 3 (September 4, 2022): 101–6. http://dx.doi.org/10.47194/orics.v3i3.185.

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In investing, investors will try to limit all the risks in managing their investments. Investor strategies to minimize investment risk are diversification by forming investment portfolios, one of which is the Mean-Variance without risk-free assets. The calculation results will show the composition of the optimum portfolio return for each stock that forms the portfolio. Optimum portfolio obtained with wT = (0.39853, 0.25519, 0.13644, 0.09788, 0.11196) sequential weight composition for TLKM, KLBF, INCO, HRUM, and FILM stocks. The composition of this optimal portfolio return is 𝜏 0.04 with a return of 0.00209 and a portfolio variance of 0.00015. The formation of this portfolio optimization model is expected to be additional literature in optimizing the investment portfolio with the Mean-Variance.

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Gubu, La, Dedi Rosadi, and Abdurakhman. "Time Series Clustering for Robust Mean-Variance Portfolio Selection: Comparison of Several Dissimilarity Measures." Journal of Physics: Conference Series 2123, no. 1 (November 1, 2021): 012021. http://dx.doi.org/10.1088/1742-6596/2123/1/012021.

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Abstract This paper shows how to create a robust portfolio selection with time series clustering by using some dissimilarity measure. Based on such dissimilarity measures, stocks are initially sorted into multiple clusters using the Partitioning Around Medoids (PAM) time series clustering approach. Following clustering, a portfolio is constructed by selecting one stock from each cluster. Stocks having the greatest Sharpe ratio are selected from each cluster. The optimum portfolio is then constructed using the robust Fast Minimum Covariance Determinant (FMCD) and robust S MV portfolio model. When there are a big number of stocks accessible for the portfolio formation process, we can use this approach to quickly generate the optimum portfolio. This approach is also resistant to the presence of any outliers in the data. The Sharpe ratio was used to evaluate the performance of the portfolios that were created. The daily closing price of stocks listed on the Indonesia Stock Exchange, which are included in the LQ-45 indexed from August 2017 to July 2018, was utilized as a case study. Empirical study revealed that portfolios constructed using PAM time series clustering with autocorrelation dissimilarity and a robust FMCD MV portfolio model outperformed portfolios created using other approaches.

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Lima de Paulo, Wanderlei, Marta Ines Velazco Fontova, and Renato Canil de Souza. "An analysis of a mean-variance enhanced index tracking problem with weights constraints." Investment Management and Financial Innovations 15, no. 4 (November 19, 2018): 183–92. http://dx.doi.org/10.21511/imfi.15(4).2018.15.

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In this paper, the authors deal with a mean-variance enhanced index tracking (EIT) problem with weights constraints. Using a shrinkage approach, they show that constructing the constrained EIT portfolio is equivalent to constructing the unconstrained EIT portfolio. This equivalence allows to study the effect of weights constraints on the covariance matrix and on the EIT portfolio. In general, the effects of weights constraints on the EIT portfolio are different from those observed in the case of global minimum variance portfolio. Finally, the authors present a numerical asset allocation example, where the S&amp;P 500 index is used as the market index to be tracked using a portfolio composed of ten stocks, in which the constrained EIT portfolio shows a satisfactory performance when compared to the unconstrained case.

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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 (March 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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Labbé, Chantal, and Andrew J. Heunis. "Convex duality in constrained mean-variance portfolio optimization." Advances in Applied Probability 39, no. 01 (March 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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Manzoor, Alia, and Safia Nosheen. "Portfolio Optimization Using Mean-Semi Variance approach with Artificial Neural Networks: Empirical Evidence from Pakistan." Journal of Accounting and Finance in Emerging Economies 8, no. 2 (June 30, 2022): 399–410. http://dx.doi.org/10.26710/jafee.v8i2.2364.

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Purpose: The challenge of managing a portfolio effectively is allocating capital among numerous stock holdings to achieve maximum profit. Therefore, the purpose of this study is to guide investors in developing optimal portfolios in the stock market of Pakistan. Design/Methodology/Approach: To pick and optimize a portfolio in the most effective way possible, we used the daily closing stock prices of a sample of listed firms at the Pakistan stock exchange. The study applied the mean semi-variance approach and compared the performance of portfolios with equally weighted portfolios under artificial neural networks and historical-based return estimation in Pakistan. Findings: The result shows that artificial neural network-based estimation of the expected return vector has outperformed the historical return estimation under mean semi-variance portfolio optimization and constrained mean semi-variance portfolios based on the Sharp ratio in Pakistan. Implications/Originality/Value: The study suggests that investors, fund managers, and portfolio analysts should focus on the more sophisticated neural network-based choice for the development of portfolios in the equity market of Pakistan.

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Agustina, Dina, Devni Prima Sari, Rara Sandhy Winanda, Muhammad Rashif Hilmi, and Dina Fakhriyana. "Comparison of Portfolio Mean-Variance Method with the Mean-Variance-Skewness-Kurtosis Method in Indonesia Stocks." EKSAKTA: Berkala Ilmiah Bidang MIPA 23, no. 02 (June 30, 2022): 88–97. http://dx.doi.org/10.24036/eksakta/vol23-iss02/316.

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In this paper, we compare the optimal portfolio weight of mean-variance (MV) method with mean-variance-skewness-kurtosis (MVSK) method. MV is a method to get weight on a portfolio. This method can be developed into the method of MVSK with attention to the higher-order moment of return distribution; skewness and kurtosis. In determining the weight of portfolio is also important to consider the skewness and kurtosis of return distribution. This method of considering the aspect of skewness and kurtosis is called the MVSK method with the aim of maximizing the level of return and skewness and minimizing the risks and exceeding of kurtosis. The result indicate that the optimal portfolio return of all methods is MVSK method with minimize variance priority.

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Wakil, Anmar Al. "A Probabilistic-Based Portfolio Resampling Under the Mean-Variance Criterion." Econometric Research in Finance 6, no. 1 (June 1, 2021): 45–56. http://dx.doi.org/10.2478/erfin-2021-0003.

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Abstract An abundant amount of literature has documented the limitations of traditional unconstrained mean-variance optimization and Efficient Frontier (EF) considered as an estimation-error maximization that magnifies errors in parameter estimates. Originally introduced by Michaud (1998), empirical superiority of portfolio resampling supposedly lies in the addressing of parameter uncertainty by averaging forecasts that are based on a large number of bootstrap replications. Nevertheless, averaging over resampled portfolio weights in order to obtain the unique Resampled Efficient Frontier (REF, U.S. patent number 6,003,018) has been documented as a debated statistical procedure. Alternatively, we propose a probabilistic extension of the Michaud resampling that we introduce as the Probabilistic Resampled Efficient Frontier (PREF). The originality of this work lies in addressing the information loss in the REF by proposing a geometrical three-dimensional representation of the PREF in the mean-variance-probability space. Interestingly, this geometrical representation illustrates a confidence region around the naive EF associated to higher probabilities; in particular for simulated Global-Mean-Variance portfolios. Furthermore, the confidence region becomes wider with portfolio return, as is illustrated by the dispersion of simulated Maximum-Mean portfolios.

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Fahmy, Hany. "Mean-variance-time: An extension of Markowitz's mean-variance portfolio theory." Journal of Economics and Business 109 (May 2020): 105888. http://dx.doi.org/10.1016/j.jeconbus.2019.105888.

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Olanrewaju, Rasaki, and Adejare Sodiq Olanrewaju. "An alternative mean-variance portfolio theoretical framework:Nigeria banks’ market shares analysis." Global Journal of Business, Economics and Management: Current Issues 11, no. 3 (November 30, 2021): 220–34. http://dx.doi.org/10.18844/gjbem.v11i3.5358.

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The ground-laying objective of portfolio conception is nothing but to allot optimally, the investment among financial assets, and a wide range of products held by investors for immediate or long-time decision. The article aims to provide both the theoretical and experimental analysis of estimating portfolio asset indexes. The technique for estimating mixing weights of each asset for proper optimization of the portfolio was described and the Ordinary Least Squares (OLS) technique was employed in the estimation of their returns and volatilities. Twelve (12) new generation (commercial and merchant) banks’ yearly market shares’ portfolios from 2001 to 2017 were analyzed. The mixing weights describing the contributing efﬁcient frontiers carved-out U.B.A and Zenith banks to be the frontiers in the commercial banks’ shares portfolio with 0.272 and 0.202 mixing weights respectively. Additionally, the 99% confidence level of the Expected-Shortfall (ES), was higher in WEMA, UNION, ACCESS, Diamond, and FCMB banks with 20.6004%, 14.7637%, 14.6458%, 15.3011%, and 16.9373% respectively. Keywords: Asset; Expected-Shortfall; Mixing Weight; Ordinary Least Squares; Portfolio

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Hjalmarsson, Erik, and Peter Manchev. "Characteristic-Based Mean-Variance Portfolio Choice." International Finance Discussion Paper 2009, no. 981 (October 2009): 1–25. http://dx.doi.org/10.17016/ifdp.2009.981.

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LIU, YAN, NGAI HANG CHAN, CHI TIM NG, and SAMUEL PO SHING WONG. "SHRINKAGE ESTIMATION OF MEAN-VARIANCE PORTFOLIO." International Journal of Theoretical and Applied Finance 19, no. 01 (February 2016): 1650003. http://dx.doi.org/10.1142/s0219024916500035.

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This paper studies the optimal expected gain/loss of a portfolio at a given risk level when the initial investment is zero and the number of stocks [Formula: see text] grows with the sample size [Formula: see text]. A new estimator of the optimal expected gain/loss of such a portfolio is proposed after examining the behavior of the sample mean vector and the sample covariance matrix based on conditional expectations. It is found that the effect of the sample mean vector is additive and the effect of the sample covariance matrix is multiplicative, both of which over-predict the optimal expected gain/loss. By virtue of a shrinkage method, a new estimate is proposed when the sample covariance matrix is not invertible. The superiority of the proposed estimator is demonstrated by matrix inequalities and simulation studies.

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Hjalmarsson, Erik, and Petar Manchev. "Characteristic-based mean-variance portfolio choice." Journal of Banking & Finance 36, no. 5 (May 2012): 1392–401. http://dx.doi.org/10.1016/j.jbankfin.2011.12.002.

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Yamphram, Pornnapat, Phiraphat Sutthimat, and Udomsak Rakwongwan. "Pricing and Hedging Index Options under Mean-Variance Criteria in Incomplete Markets." Computation 11, no. 2 (February 7, 2023): 30. http://dx.doi.org/10.3390/computation11020030.

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This paper studies the portfolio selection problem where tradable assets are a bank account, and standard put and call options are written on the S&P 500 index in incomplete markets in which there exist bid–ask spreads and finite liquidity. The problem is mathematically formulated as an optimization problem where the variance of the portfolio is perceived as a risk. The task is to find the portfolio which has a satisfactory return but has the minimum variance. The underlying is modeled by a variance gamma process which can explain the extreme price movement of the asset. We also study how the optimized portfolio changes subject to a user’s views of the future asset price. Moreover, the optimization model is extended for asset pricing and hedging. To illustrate the technique, we compute indifference prices for buying and selling six options namely a European call option, a quadratic option, a sine option, a butterfly spread option, a digital option, and a log option, and propose the hedging portfolios, which are the portfolios one needs to hold to minimize risk from selling or buying such options, for all the options. The sensitivity of the price from modeling parameters is also investigated. Our hedging strategies are decent with the symmetry property of the kernel density estimation of the portfolio payout. The payouts of the hedging portfolios are very close to those of the bought or sold options. The results shown in this study are just illustrations of the techniques. The approach can also be used for other derivatives products with known payoffs in other financial markets.

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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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CHUN, WEIDE, HESEN LI, and XU WU. "PORTFOLIO MODEL UNDER FRACTAL MARKET BASED ON MEAN-DCCA." Fractals 28, no. 07 (November 2020): 2050142. http://dx.doi.org/10.1142/s0218348x2050142x.

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Under the realistic background that the capital market nowadays is a fractal market, this paper embeds the detrended cross-correlation analysis (DCCA) into the return-risk criterion to construct a Mean-DCCA portfolio model, and gives an analytical solution. Based on this, the validity of Mean-DCCA portfolio model is verified by empirical analysis. Compared to the mean-variance portfolio model, the Mean-DCCA portfolio model is more conducive for investors to build a sophisticated investment portfolio under multi-time-scale, improve the performance of portfolios, and overcome the defect that the mean-variance portfolio model has not considered the existence of fractal correlation characteristics between assets.

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Guo, Wei, Yichao Wang, and Danping Qiu. "Mean-Variance Portfolio Choice with Uncertain Variance-Covariance Matrix." Journal of Financial Risk Management 09, no. 02 (2020): 57–81. http://dx.doi.org/10.4236/jfrm.2020.92004.

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Zheng, Zichun. "Application of LSTM and portfolio optimization in Chinese stock market." BCP Business & Management 30 (October 24, 2022): 380–87. http://dx.doi.org/10.54691/bcpbm.v30i.2450.

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The purpose of this paper is to examine the application of LSTM and mean variance portfolio optimization in Chinese stock market. 20 stocks are selected from CSI 300 components, we collect their High, Low, Open, Adjust Close and trade volume from June 16th 2020 to June 16th 2022. Then we use LSTM model to forecast the stock price. The forecast results are used to construct 2 portfolios. One portfolio maximize Sharpe ratio, the other portfolio minimize variance. From April 6th to June 16th 2022, the Maximize Sharpe Ratio portfolio outperformed CSI 300 index, the Minimize Variance Portfolio did not beat the market but the return was very close to CSI. Therefore, the combination of LSTM and Mean Variance Portfolio Optimization theory is effective in Chinese stock market.

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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 (June 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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Yusup, Adi Kurniawan. "Mean-Variance and Single-Index Model Portfolio Optimisation:Case in the Indonesian Stock Market." Asian Journal of Business and Accounting 15, no. 2 (December 31, 2022): 79–109. http://dx.doi.org/10.22452/ajba.vol15no2.3.

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Manuscript type: Research paper Research aims: This study aims to compare the performance of meanvariance and single-index models in creating the optimal portfolio. Design/Methodology/Approach: This study creates optimal portfolios using the mean-variance and single-index models with daily stock return data of 38 companies listed on the LQ45 index, IDX Composite index and Bank Indonesia’s 7-Day (Reverse) Repo Rate from January 1, 2012 to December 31, 2019. The two models are compared using the Sharpe ratio. Research findings: The result shows that the single-index model dominates the Indonesian Stock Exchange (IDX), more so than the meanvariance model. BBCA has the highest proportion for both mean-variance and single-index portfolios. Theoretical contribution/Originality: This study compares two popular portfolio models in the Indonesian stock market. Practitioner/Policy implication: This study helps investors to create optimal portfolios using a model that is more suited to the IDX. Research limitation/Implication: This study creates the optimal portfolio without differentiating risk preferences (i.e., risk averse, risk moderate and risk taker). In addition, this research only uses daily return data and does not compare it with weekly and monthly data.

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Alemanni, Barbara, Mario Maggi, and Pierpaolo Uberti. "Unleveraged Portfolios and Pure Allocation Return." Journal of Risk and Financial Management 14, no. 11 (November 13, 2021): 550. http://dx.doi.org/10.3390/jrfm14110550.

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In asset management, the portfolio leverage affects performance, and can be subject to constraints and operational limitations. Due to the possible leverage aversion of the investors, the comparison between portfolio performances can be incomplete or misleading. We propose a procedure to unleverage the mean-variance efficient portfolios to satisfy a leverage requirement. We obtain a class of unleveraged portfolios that are homogeneous in terms of leverage, so therefore properly comparable. The proposed unleverage procedure permits isolating the pure allocation return, i.e., the return component, due to the qualitative choice of portfolio allocation, from the return component due to the portfolio leverage. Theoretical analysis and empirical evidence on actual data show that efficient mean-variance portfolios, once unleveraged, uncover mean-variance dominance relations hidden by the leverage contribution to portfolio return. Our approach may be useful to practitioners proposing to take long positions on “short assets” (e.g. inverse ETF), thereby considering short positions as active investment choices, in contrast with the usual interpretation where are used to overweight long positions.

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Wu, Weiping, Lifen Wu, Ruobing Xue, and Shan Pang. "Constrained Dynamic Mean-Variance Portfolio Selection in Continuous-Time." Algorithms 14, no. 8 (August 23, 2021): 252. http://dx.doi.org/10.3390/a14080252.

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This paper revisits the dynamic MV portfolio selection problem with cone constraints in continuous-time. We first reformulate our constrained MV portfolio selection model into a special constrained LQ optimal control model and develop the optimal portfolio policy of our model. In addition, we provide an alternative method to resolve this dynamic MV portfolio selection problem with cone constraints. More specifically, instead of solving the correspondent HJB equation directly, we develop the optimal solution for this problem by using the special properties of value function induced from its model structure, such as the monotonicity and convexity of value function. Finally, we provide an example to illustrate how to use our solution in real application. The illustrative example demonstrates that our dynamic MV portfolio policy dominates the static MV portfolio policy.

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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 (December 7, 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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Et. al., Adil Moghara. "Mean- Adjusted Variance Model for Portfolio Optimization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 10, 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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Shen, Yang, and Bin Zou. "Mean-Variance Portfolio Selection in Contagious Markets." SIAM Journal on Financial Mathematics 13, no. 2 (April 7, 2022): 391–425. http://dx.doi.org/10.1137/20m1320560.

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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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Xiong, Jie, and Xun Yu Zhou. "Mean‐Variance Portfolio Selection under Partial Information." SIAM Journal on Control and Optimization 46, no. 1 (January 2007): 156–75. http://dx.doi.org/10.1137/050641132.

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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 (December 23, 2016): 123404. http://dx.doi.org/10.1088/1742-5468/aa4f9c.

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Chiu, Mei Choi, and Hoi Ying Wong. "Mean–variance portfolio selection with correlation risk." Journal of Computational and Applied Mathematics 263 (June 2014): 432–44. http://dx.doi.org/10.1016/j.cam.2013.12.050.

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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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Zhou, Yuan, and Zhe Wu. "Mean-Variance Portfolio Selection with Margin Requirements." Journal of Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/726297.

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We study the continuous-time mean-variance portfolio selection problem in the situation when investors must pay margin for short selling. The problem is essentially a nonlinear stochastic optimal control problem because the coefficients of positive and negative parts of control variables are different. We can not apply the results of stochastic linearquadratic (LQ) problem. Also the solution of corresponding Hamilton-Jacobi-Bellman (HJB) equation is not smooth. Li et al. (2002) studied the case when short selling is prohibited; therefore they only need to consider the positive part of control variables, whereas we need to handle both the positive part and the negative part of control variables. The main difficulty is that the positive part and the negative part are not independent. The previous results are not directly applicable. By decomposing the problem into several subproblems we figure out the solutions of HJB equation in two disjoint regions and then prove it is the viscosity solution of HJB equation. Finally we formulate solution of optimal portfolio and the efficient frontier. We also present two examples showing how different margin rates affect the optimal solutions and the efficient frontier.

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Xia, Jianming. "MEAN-VARIANCE PORTFOLIO CHOICE: QUADRATIC PARTIAL HEDGING." Mathematical Finance 15, no. 3 (July 2005): 533–38. http://dx.doi.org/10.1111/j.1467-9965.2005.00231.x.

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Maccheroni, Fabio, Massimo Marinacci, Aldo Rustichini, and Marco Taboga. "PORTFOLIO SELECTION WITH MONOTONE MEAN-VARIANCE PREFERENCES." Mathematical Finance 19, no. 3 (July 2009): 487–521. http://dx.doi.org/10.1111/j.1467-9965.2009.00376.x.

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Best, Michael J., and Robert R. Grauer. "Sensitivity Analysis for Mean-Variance Portfolio Problems." Management Science 37, no. 8 (August 1991): 980–89. http://dx.doi.org/10.1287/mnsc.37.8.980.

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Chiu, Mei Choi, and Hoi Ying Wong. "Mean–variance portfolio selection of cointegrated assets." Journal of Economic Dynamics and Control 35, no. 8 (August 2011): 1369–85. http://dx.doi.org/10.1016/j.jedc.2011.04.003.

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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 (May 26, 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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Stein, Roberto, and Orlando E. Contreras-Pacheco. "Optimizing the performance of mean-variance portfolios in various markets: an “old-school” approach." Investment Management and Financial Innovations 15, no. 1 (March 2, 2018): 190–207. http://dx.doi.org/10.21511/imfi.15(1).2018.17.

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The authors study the performance of mean-variance optimized (MVO) equity portfolios for retail investors in various markets in the U.S. and around the world. Actively managed equity mutual funds have relatively high fees and tend to underperform their benchmark. Index funds such as exchange traded funds still charge appreciable fees, and only deliver the performance of the benchmark. The authors find that MVO portfolios are relatively easy to manage by a retail investor, and that they tend to outperform their benchmark or, at worst, equal its performance, even after adjusting for risk. Moreover, they show that the performance of these funds is not particularly sensitive to the frequency at which they are rebalanced so that, in the limit, an investor might have to rebalance his/her portfolio only once a year. This last finding translates into very low trading costs, even for retail investors. Thus, the authors conclude that MVOs offer an easy, cheap alternative to invest in the world’s equity markets.

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Liu, Yanyu. "Broad Asset Portfolio Designed Based on the Mean-Variance Model." BCP Business & Management 26 (September 19, 2022): 714–23. http://dx.doi.org/10.54691/bcpbm.v26i.2031.

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Contemporarily, broad asset class allocation has gradually become an ideal investment strategy for investors and institutions. This paper constructs the optimal asset class allocation and portfolio design with python based on the mean-variance model, using stocks, gold, crude oil, bonds, futures, foreign exchange, funds, commodities, digital currencies and treasury bonds as the main underlying assets. To compare the asset allocation portfolios constructed by different approaches (the equally weighted investment model, the minimum variance model and the maximum Sharpe ratio model), the comparative analysis is implemented in terms of five indicators, including the annualised return, annualised volatility, Sharpe ratio, maximum drawdown and return-to-drawdown ratio. After the comparison, the advantages of the maximum Sharpe ratio model are demonstrated. According to the results, the mean-variance model, as a risk management model from the investor’s perspective, is consistent with the investment logic of investors and financial institutions that it outperforms the traditional minimum variance model and equally weighted model in terms of profitability and risk control. Therefore, the mean-variance model has certain theoretical guidance for broad asset class allocation. Overall, these results shed light on portfolio designed for investments.

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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 (April 30, 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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Journal articles on the topic 'Mean-Variance Portfolio'

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