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Journal articles on the topic 'Markowitz Portfolio Selection'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Ghosh, Satadal, and Sujit Kumar Majumdar. "Portfolio Selection Models and Their Discrimination." International Journal of Operations Research and Information Systems 2, no. 2 (2011): 65–91. http://dx.doi.org/10.4018/joris.2011040104.

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The stochastic nature of financial markets is a barrier for successful portfolio management. Besides traditional Markowitz’s model, many other portfolio selection models in Bayesian and Non-Bayesian frameworks have been developed. Starting with the basic Markowitz model, several cardinal models are used to find optimum portfolios with select stock set. Having developed the regression model of the return of each stock with the market return, the unsystematic part of the uncertainty was used to find the optimum portfolio and efficient risk–return frontier within each portfolio selection model. T

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2

Solanki, Dr Ashvinkumar H. "Portfolio Selection Process through Markowitz Model." Indian Journal of Applied Research 4, no. 8 (2011): 356–58. http://dx.doi.org/10.15373/2249555x/august2014/90.

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Nur Safitri, Indah Nur, Sudradjat Sudradjat, and Eman Lesmana. "STOCK PORTFOLIO ANALYSIS USING MARKOWITZ MODEL." International Journal of Quantitative Research and Modeling 1, no. 1 (2020): 47–58. http://dx.doi.org/10.46336/ijqrm.v1i1.6.

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A common problem that often occurs in investment is the selection of the optimal portfolio according to the wishes of investors. This thesis ueds the Markowitz Model as a basis to formed a model to choose the optimal portfolio that provided the lowest risk. Efforts to minimize risk were carried out by conducting a diversification strategy. After the selection of several companies with the criteria of capitalization value and DER (Debt Equity Ratio), a combination of stocks is formed to form a portfolio. The formed portfolio was then analyzed to determine the optimal proportion of each stock. U

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LI, ZHONG-FEI, KAI W. NG, KEN SENG TAN, and HAILIANG YANG. "OPTIMAL CONSTANT-REBALANCED PORTFOLIO INVESTMENT STRATEGIES FOR DYNAMIC PORTFOLIO SELECTION." International Journal of Theoretical and Applied Finance 09, no. 06 (2006): 951–66. http://dx.doi.org/10.1142/s0219024906003883.

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In this paper we propose a variant of the continuous-time Markowitz mean-variance model by incorporating the Earnings-at-Risk measure in the portfolio optimization problem. Under the Black-Scholes framework, we obtain closed-form expressions for the optimal constant-rebalanced portfolio (CRP) investment strategy. We also derive explicitly the corresponding mean-EaR efficient portfolio frontier, which is a generalization of the Markowitz mean-variance efficient frontier.

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Rodrigues, Ana Flávia P., Igor M. Guerreiro, and Charles Casimiro Cavalcante. "Deformed exponentials and portfolio selection." International Journal of Modern Physics C 29, no. 03 (2018): 1850029. http://dx.doi.org/10.1142/s0129183118500298.

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In this paper, we present a method for portfolio selection based on the consideration on deformed exponentials in order to generalize the methods based on the gaussianity of the returns in portfolio, such as the Markowitz model. The proposed method generalizes the idea of optimizing mean-variance and mean-divergence models and allows a more accurate behavior for situations where heavy-tails distributions are necessary to describe the returns in a given time instant, such as those observed in economic crises. Numerical results show the proposed method outperforms the Markowitz portfolio for the

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Mishra, Rohan, and Bhagwat Ram. "Portfolio selection using R." Yugoslav Journal of Operations Research 30, no. 2 (2020): 137–46. http://dx.doi.org/10.2298/yjor181115002m.

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In this paper, we consider the Markowitz mean-variance model to minimize the risk on two assets and develop the program in R software to improve the performance of the model for two real stocks data with various combinations of the portfolios. We have taken two real stocks data up to 4514 each from yahoo database finance using our R program to show how fast our calculations are.

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Kriens, J., and J. Th Lieshout. "Notes on the Markowitz portfolio selection method." Statistica Neerlandica 42, no. 3 (1988): 181–91. http://dx.doi.org/10.1111/j.1467-9574.1988.tb01232.x.

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8

DE FRANCO, CARMINE, JOHANN NICOLLE, and HUYÊN PHAM. "BAYESIAN LEARNING FOR THE MARKOWITZ PORTFOLIO SELECTION PROBLEM." International Journal of Theoretical and Applied Finance 22, no. 07 (2019): 1950037. http://dx.doi.org/10.1142/s0219024919500377.

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We study the Markowitz portfolio selection problem with unknown drift vector in the multi-dimensional framework. The prior belief on the uncertain expected rate of return is modeled by an arbitrary probability law, and a Bayesian approach from filtering theory is used to learn the posterior distribution about the drift given the observed market data of the assets. The Bayesian Markowitz problem is then embedded into an auxiliary standard control problem that we characterize by a dynamic programming method and prove the existence and uniqueness of a smooth solution to the related semi-linear pa

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9

Grujić, Miloš. "APPLICATION OF THE MODERN PORTFOLIO THEORY IN DIVERSIFICATION OF THE DEBT SECURITIES PORTFOLIO IN EMERGING MARKETS." ЗБОРНИК РАДОВА ЕКОНОМСКОГ ФАКУЛТЕТА У ИСТОЧНОМ САРАЈЕВУ 1, no. 13 (2017): 67. http://dx.doi.org/10.7251/zrefis1613067g.

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The term "portfolio analysis", introduced in the economic theory by Harry Markowitz, is not a new term in scientific literature. However, analysis and criticism in the papers of local and foreign authors are mainly based on the examples of developed capital markets. There are very few cases of application of the portfolio analysis in the domestic capital market. The focus of this paper is on implementation of diversification of the bonds on the Banja Luka Stock Exchange. Using Markowitz's portfolio selection, we will prove that diversification, including all limitations, is possible and applic

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10

Puji Lestari, Novi. "Simulation Of Optimal Portfolio Using Single Index Model and Markowitz Model On Lq-45 Index Shares For 2018." JBMP (Jurnal Bisnis, Manajemen dan Perbankan) 7, no. 1 (2021): 93–140. http://dx.doi.org/10.21070/jbmp.v7i1.880.

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The essence of portfolio formation is to reduce risk by means of diversification, namely allocating a number of funds to various investment alternatives that are negatively correlated. To select returns in a portfolio, you can use the Single Index Model and the Markowitz Model. This study was conducted with the aim of comparing the calculation of which company portfolios can provide a good rate of return with a small risk using the Single Index and Markowitz Model based on the sector of the company. So that the results of this study can provide recommendations to investors in decision making o

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11

Hüttner, Amelie, Jan-Frederik Mai, and Stefano Mineo. "Portfolio selection based on graphs: Does it align with Markowitz-optimal portfolios?" Dependence Modeling 6, no. 1 (2018): 63–87. http://dx.doi.org/10.1515/demo-2018-0004.

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AbstractSome empirical studies suggest that the computation of certain graph structures from a (large) historical correlation matrix can be helpful in portfolio selection. In particular, a repeated finding is that information about the portfolio weights in the minimum variance portfolio (MVP) from classical Markowitz theory can be inferred from measurements of centrality in such graph structures. The present article compares the two concepts from a purely algebraic perspective. It is demonstrated that this heuristic relationship between graph centrality and the MVP does not originate from a st

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12

Nocetti, Diego. "Markowitz meets Kahneman: Portfolio selection under divided attention." Finance Research Letters 3, no. 2 (2006): 106–13. http://dx.doi.org/10.1016/j.frl.2006.03.006.

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13

Hamid, Agustini. "Analysis Of Dynamic Portfolio Allocation Of Indonesian LQ45 During 2005 – 2011 Following The Markowitz Theowry." Winners 17, no. 2 (2016): 91. http://dx.doi.org/10.21512/tw.v17i2.1969.

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The research observed that equity portfolio and investment managers were facing challenges in determining the optimum portfolio, especially during the turbulent times. As a result, they needed to implement portfolio management strategies to overcome the risk associated with stock return volatility in turbulence periods. This research focused on selecting stocks from the LQ-45 index during 2005-2011 using The Markowitz theory combining the Solver Linear Programming. The portfolio selection method which has been introduced by Markowitz (1952) used variance or standard deviation as a risk measure

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Roy, Dilip, and Soma Panja Chowdhury. "Heuristic selection of portfolio based on coefficient of optimism." GIS Business 13, no. 6 (2018): 1–12. http://dx.doi.org/10.26643/gis.v13i6.3260.

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The mean-variance method developed by Markowitz (1959) was aimed at obtaining optimizing portfolios. But selection of portfolio in the real world mostly deviates from this optimal criterion. In this paper we have considered this issue from an altogether different aspect and developed means for aiming at nearly optimum portfolio. We considered the risk taking propensity as the main driving force and presented a heuristic method to reach the near to the optimal state. For doing so, we have introduced the coefficient of optimism in the decision making process and simultaneously considered conditi

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Simplício, Jalimar Guimarães, Celso Funcia Lemme, and Ricardo Pereira Câmara Leal. "Portfolio theory in the selection of oil investment projects." Gestão & Produção 19, no. 2 (2012): 265–72. http://dx.doi.org/10.1590/s0104-530x2012000200003.

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The objective of this article is to compare investment project selection using the efficient frontier in the mean-variance space based on optimization models introduced by Markowitz (1952) with the project ranking method according to the profitability index (PI). The selection of real assets by companies did not incorporate the mean-variance optimization procedure in the same way the selection of financial assets in investment portfolios did. The process of selection and formation of portfolios of investment projects for the oil area of a company in the energy industry was analyzed. Project po

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16

Steuer, Ralph E., Maximilian Wimmer, and Markus Hirschberger. "Overviewing the transition of Markowitz bi-criterion portfolio selection to tri-criterion portfolio selection." Journal of Business Economics 83, no. 1 (2013): 61–85. http://dx.doi.org/10.1007/s11573-012-0642-4.

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Plastun, Alex, Inna Makarenko, Yulia Yelnikova, and Diana Bychenko. "Optimal investment portfolio selection from the largest Ukrainian companies: comparative study of conventional and responsible portfolios." Public and Municipal Finance 8, no. 1 (2019): 44–53. http://dx.doi.org/10.21511/pmf.08(1).2019.04.

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This paper is devoted to the comparing stock portfolios of the largest conventional and responsible Ukrainian companies as the basis for substantiating the structure of an optimal investment portfolio in the current conditions of development of the financial market of Ukraine. The empirical basis of the research was the data of quotations of shares of 6 most liquid conventional and 6 responsible companies in the Ukrainian and Warsaw exchanges. The methodological basis of calculations was the classic Markowitz portfolio optimization model. The key hypothesis of the research was to check that th

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Paudel, Dr Rajan Bahadur, and Sujan Koirala. "Application of Markowitz and Sharpe Models in Nepalese Stock." Journal of Nepalese Business Studies 3, no. 1 (2007): 18–35. http://dx.doi.org/10.3126/jnbs.v3i1.480.

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ABSTRACT The purpose of this article is to test whether or not Markowitz and Sharpe models of portfolio selection offer better investment alternatives to Nepalese investors. It has been done by applying those models in a sample of 30 stocks traded in Nepalese stock market. The study finds that the application of these elementary models developed about a half century ago offer better options for making decision in the choice of optimal portfolios in Nepalese stock market.  Journal of Nepalese Business Studies 2006/III/1 pp. 18-35

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19

Gubu, La, Dedi Rosadi, and Abdurakhman Abdurakhman. "ROBUST PORTFOLIO SELECTION WITH CLUSTERING BASED ON BUSINESS SECTOR OF STOCKS." MEDIA STATISTIKA 14, no. 1 (2021): 33–43. http://dx.doi.org/10.14710/medstat.14.1.33-43.

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In recent years there have been numerous studies on portfolio selection using cluster analysis in conjunction with Markowitz model which used mean vectors and covariance matrix that are estimated from a highly volatile data. This study presents a more robust way of portfolio selection where stocks are grouped into clusters based on business sector of stocks. A representative from each cluster is selected from each cluster using Sharpe ratio to construct a portfolio and then optimized using robust FCMD and S-estimation. Calculation Sharpe ratio showed that this method works efficiently on large

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20

Chellathurai, Thamayanthi, and Thangaraj Draviam. "Markowitz principles for multi-period portfolio selection problems with moments of any order." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2092 (2008): 827–54. http://dx.doi.org/10.1098/rspa.2007.1911.

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The multi-period portfolio selection problem is formulated as a Markowitz mean–variance optimization problem in terms of time-varying means, covariances and higher-order and intertemporal moments of the asset prices. The crux lies in expressing the number of shares of any particular asset to be transacted on any future trading date, which is a non-anticipative process, as a polynomial of the changes in the discounted prices of all the risky assets. This results in the expected return of the portfolio being dependent on not only the means of the asset prices, but also the higher-order and inter

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21

Farias Guimarães Júnior, Francisco Roberto, Charles Ulises De Montreuil Carmona, and Luciana Gondim de Almeida Guimarães. "Strategy of asset portfolio risk diversification through value drivers." REBRAE 8, no. 1 (2015): 53. http://dx.doi.org/10.7213/rebrae.08.001.ao04.

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The risk diversification of an asset portfolio of investments is underlying in the idea that all securities have an idiosyncratic behavior which allows compensating a specific stock loss by the gain achieved by other stock into the portfolio. However, we know that the portfolio selection process should excel for choosing assets capable of creating and generating value on the long term. Thus, the objective of this research was to verify if the portfolios selected through their value drivers present the diversification benefits that were determined in prior researches. We had used the data avail

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22

Nurwahidah, Nurwahidah. "QUADRATIC PROGRAMMING: AN OPTIMIZATION TOOL FOR BUILDING GLOBAL MINIMUM VARIANCE PORTFOLIO WITH NO SHORT SALE." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 2 (2021): 305–14. http://dx.doi.org/10.30598/barekengvol15iss2pp305-314.

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Quantitative method in portfolio selection is a fascinating issue to make a decision in investment. Portfolio optimization is a very important to manage investment risk. There are many papers dealing with the Markowitz portfolio model, but not all of the papers studied about positive weight portfolio or no short sale constrained portfolio. Positive weight portfolio describes that short sale is allowed for the investor. While, short sale is banned in a certain economic condition due to its ability in decreasing stock market index. Besides, Islamic capital market does not allow speculative trans

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23

Poletaev, Anatoliy Y., and Elena M. Spiridonova. "Hierarchical Clustering as a Dimension Reduction Technique for Markowitz Portfolio Optimization." Modeling and Analysis of Information Systems 27, no. 1 (2020): 62–71. http://dx.doi.org/10.18255/1818-1015-2020-1-62-71.

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Optimal portfolio selection is a common and important application of an optimization problem. Practical applications of an existing optimal portfolio selection methods is often difficult due to high data dimensionality (as a consequence of the large number of securities available for investment). In this paper, a method of dimension reduction based on hierarchical clustering is proposed. Clustering is widely used in computer science, a lot of algorithms and computational methods have been developed for it. As a measure of securities proximity for hierarchical clustering Pearson pair correlatio

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Ismail, Amine, and Huyên Pham. "Robust Markowitz mean-variance portfolio selection under ambiguous covariance matrix." Mathematical Finance 29, no. 1 (2018): 174–207. http://dx.doi.org/10.1111/mafi.12169.

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Wang, Qiyu, and Hailin Sun. "Sparse markowitz portfolio selection by using stochastic linear complementarity approach." Journal of Industrial & Management Optimization 14, no. 2 (2018): 541–59. http://dx.doi.org/10.3934/jimo.2017059.

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Abi Jaber, Eduardo, Enzo Miller, and Huyên Pham. "Markowitz Portfolio Selection for Multivariate Affine and Quadratic Volterra Models." SIAM Journal on Financial Mathematics 12, no. 1 (2021): 369–409. http://dx.doi.org/10.1137/20m1347449.

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27

Rout, Biswajit, and Jaya Krushna Panda. "Optimal Portfolio Investment Strategy: Portfolio Selection in Indian Stock Market Using the Markowitz Model." Siddhant- A Journal of Decision Making 15, no. 1 (2015): 87. http://dx.doi.org/10.5958/2231-0657.2015.00009.9.

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Guo, Haifeng, BaiQing Sun, Hamid Reza Karimi, Yuanjing Ge, and Weiquan Jin. "Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/628295.

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This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV) portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investmen

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Zhang, Peng, and Jing Yi Zhou. "Empirical Research of Portfolio Selection under M-SAD Model." Applied Mechanics and Materials 380-384 (August 2013): 4409–12. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4409.

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The mean semi-absolute deviation is the extension and development from the mean-variance theory which proposed by Markowitz. This paper studied the Mean-SAD (semi-variance deviation) model without the short selling and used the Chinese securities markets 20 stocks to test the efficient of the model. We got the conclusion that M-SAD model can effectively direct the decision in portfolio selection. Based on the result of the empirical research, the paper prospects the application of M-SAD model in our country.

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Iqbal, Javed, Moeed Ahmad Sandhu, Shaheera Amin, and Aliya Manzoor. "Portfolio Selection and Optimization through Neural Networks and Markowitz Model: A Case of Pakistan Stock Exchange Listed Companies." Review of Economics and Development Studies 5, no. 1 (2019): 183–96. http://dx.doi.org/10.26710/reads.v5i1.354.

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This paper used artificial neural networks (ANNs) time series predictor for approximating returns of Pakistan Stock Exchange (PSX) listed 100 companies. These projected returns are then substituted into expected returns in the Markowitz’s Mean Variance (MV) portfolio Model. For comparison empirical data used is closing prices of PSX listed stocks, Karachi Inter Bank Offer Rates (KIBOR) as risk free rate and KSE-all share index as benchmark. The Portfolio returns are compared for two datasets by employing various constraints like budget, transaction costs, and turnover constraints. The value of

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Wang, Jian, and Junseok Kim. "Applying Least Squares Support Vector Machines to Mean-Variance Portfolio Analysis." Mathematical Problems in Engineering 2019 (June 27, 2019): 1–10. http://dx.doi.org/10.1155/2019/4189683.

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Portfolio selection problem introduced by Markowitz has been one of the most important research fields in modern finance. In this paper, we propose a model (least squares support vector machines (LSSVM)-mean-variance) for the portfolio management based on LSSVM. To verify the reliability of LSSVM-mean-variance model, we conduct an empirical research and design an algorithm to illustrate the performance of the model by using the historical data from Shanghai stock exchange. The numerical results show that the proposed model is useful when compared with the traditional Markowitz model. Comparing

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Shao, Shuai, Li-qun Yang, Yuan-biao Zhang, and Zhi-hui Meng. "A Modified Markowitz Multi-Period Dynamic Portfolio Selection Model Based on the LDIW-PSO." International Journal of Economics and Finance 8, no. 1 (2015): 90. http://dx.doi.org/10.5539/ijef.v8n1p90.

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<p>Modern financial market is an extremely complicated nonlinear system, while gaming and speculation in the market makes the returns and risks of financial assets a great deal of uncertainty. How to construct an effective portfolio, realize the maximization of portfolio returns and the minimization of risks, and optimize the investment capital allocation efficiency are becoming increasingly a hot topic. This paper discusses a revised Markowitz Multi-period Dynamic portfolio mode by introducing LDIW-PSO in the process of solving the optimal investment weight. The LDIW-PSO has greatly imp

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Draviam, Thangaraj, and Thamayanthi Chellathurai. "Generalized Markowitz mean–variance principles for multi–period portfolio–selection problems." Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 458, no. 2027 (2002): 2571–607. http://dx.doi.org/10.1098/rspa.2002.0983.

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García, Fernando, Jairo González-Bueno, Javier Oliver, and Rima Tamošiūnienė. "A CREDIBILISTIC MEAN-SEMIVARIANCE-PER PORTFOLIO SELECTION MODEL FOR LATIN AMERICA." Journal of Business Economics and Management 20, no. 2 (2019): 225–43. http://dx.doi.org/10.3846/jbem.2019.8317.

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Many real-world problems in the financial sector have to consider different objectives which are conflicting, for example portfolio selection. Markowitz proposed an approach to determine the optimal composition of a portfolio analysing the trade-off between return and risk. Nevertheless, this approach has been criticized for unrealistic assumptions and several changes have been proposed to incorporate investors’ constraints and more realistic risk measures. In this line of research, our proposal extends the mean-semivariance portfolio selection model to a multiobjective credibilistic model tha

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Ulf, Herold, and Maurer Raimond. "Portfolio Choice and Estimation Risk. A Comparison of Bayesian to Heuristic Approaches." ASTIN Bulletin 36, no. 01 (2006): 135–60. http://dx.doi.org/10.2143/ast.36.1.2014147.

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Estimation risk is known to have a huge impact on mean/variance optimized portfolios, which is one of the primary reasons to make standard Markowitz optimization unfeasible in practice. This issue has attracted new interest in the last years, and several approaches to incorporate estimation risk into portfolio selection have been developed only recently. In this article, we review these approaches as well as some older ones and compare them in an empirical out-of-sample study. The approaches can be classified along two criteria. First, we can differentiate heuristic approaches (restricting por

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Ulf, Herold, and Maurer Raimond. "Portfolio Choice and Estimation Risk. A Comparison of Bayesian to Heuristic Approaches." ASTIN Bulletin 36, no. 1 (2006): 135–60. http://dx.doi.org/10.1017/s0515036100014434.

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Estimation risk is known to have a huge impact on mean/variance optimized portfolios, which is one of the primary reasons to make standard Markowitz optimization unfeasible in practice. This issue has attracted new interest in the last years, and several approaches to incorporate estimation risk into portfolio selection have been developed only recently. In this article, we review these approaches as well as some older ones and compare them in an empirical out-of-sample study. The approaches can be classified along two criteria. First, we can differentiate heuristic approaches (restricting por

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37

Ledoit, Olivier, and Michael Wolf. "Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks." Review of Financial Studies 30, no. 12 (2017): 4349–88. http://dx.doi.org/10.1093/rfs/hhx052.

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Hou, Danlin, and Zuo Quan Xu. "A Robust Markowitz Mean-Variance Portfolio Selection Model with an Intractable Claim." SIAM Journal on Financial Mathematics 7, no. 1 (2016): 124–51. http://dx.doi.org/10.1137/15m1016357.

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Deng, Guang-Feng, Woo-Tsong Lin, and Chih-Chung Lo. "Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization." Expert Systems with Applications 39, no. 4 (2012): 4558–66. http://dx.doi.org/10.1016/j.eswa.2011.09.129.

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Mounir, Amine Mohammed. "Prudence and temperance in portfolio selection with Shariah-compliant investments." International Journal of Islamic and Middle Eastern Finance and Management 14, no. 4 (2021): 753–66. http://dx.doi.org/10.1108/imefm-07-2019-0292.

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Purpose This paper aims to explore the impact of Sharīʿah-compliant stocks on other investor risk preferences beyond the risk aversion, namely, prudence and temperance. Design/methodology/approach This paper uses the non-parametric model data envelopment analysis with the shortage function as a measure of performance. The model uses three specifications considering skewness and kurtosis that describe according to expected utility theory, prudence and temperance. Findings Results show that first, efficient portfolios consist mainly of conventional stocks in the three-model specification. Second

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Danko, Jakub, and Vincent Šoltés. "Portfolio creation using graph characteristics." Investment Management and Financial Innovations 15, no. 1 (2018): 180–89. http://dx.doi.org/10.21511/imfi.15(1).2018.16.

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The aim of this work is by combination of the graph theory and Markowitz portfolio theory to illustrate how some graph characteristics are related to the diversification potential of individual portfolio-forming stocks. Using the graph characteristic, the vertex eccentricity, individual stocks are divided into two groups: a group of large and group of small eccentricity. Eccentricity in this context is considered to be a very suitable metric of the centrality of individual vertices. Different price histories (5 to 30 years) of the Standard and Poor’s index are analyzed. Using the simulation an

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Chevallier, Julien, and Dinh-Tri Vo. "Portfolio allocation across variance risk premia." Journal of Risk Finance 20, no. 5 (2019): 556–93. http://dx.doi.org/10.1108/jrf-06-2019-0107.

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Purpose In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times. Design/methodology/approach To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-freq

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Pereira, José Veiga. "Estudo Comparativo dos Modelos de Markowitz e Sharpe." Review of Business and Legal Sciences, no. 8 (July 10, 2017): 79. http://dx.doi.org/10.26537/rebules.v0i8.841.

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A teoria da carteira de Harry Markowitz, originalmente publicada em 1952 no Journal of Finance, "Portfolio Selection", desenvolveu um método de solução geral do problema da estrutura das carteiras, que engloba o tratamento quantificado do risco. Propõe a determinação de um conjunto de carteiras eficientes empregando unicamente os conceitos de média para a rentabilidade que se espera obter e de variância (ou desvio padrão) para a incerteza associada a essa rentabilidade, e daí a denominação de média-variância à análise de Markowitz. Chamou também a atenção para a diversificação das carteiras, m

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ATTA MILLS, Ebenezer Fiifi Emire, Bo YU, and Jie YU. "SCALED AND STABLE MEAN-VARIANCE-EVAR PORTFOLIO SELECTION STRATEGY WITH PROPORTIONAL TRANSACTION COSTS." Journal of Business Economics and Management 18, no. 4 (2017): 561–84. http://dx.doi.org/10.3846/16111699.2017.1342272.

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This paper studies a portfolio optimization problem with variance and Entropic Value-at-Risk (evar) as risk measures. As the variance measures the deviation around the expected return, the introduction of evar in the mean-variance framework helps to control the downside risk of portfolio returns. This study utilized the squared l2-norm to alleviate estimation risk problems arising from the mean estimate of random returns. To adequately represent the variance-evar risk measure of the resulting portfolio, this study pursues rescaling by the capital accessible after payment of transaction costs.

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Feldman, Keith. "Portfolio Selection, Efficient Diversification of Investments. By Harry M. Markowitz (Basil Blackwell, 1991) £25.00." Journal of the Institute of Actuaries 119, no. 1 (1992): 165–66. http://dx.doi.org/10.1017/s0020268100019831.

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Kellner, Florian, and Sebastian Utz. "Sustainability in supplier selection and order allocation: Combining integer variables with Markowitz portfolio theory." Journal of Cleaner Production 214 (March 2019): 462–74. http://dx.doi.org/10.1016/j.jclepro.2018.12.315.

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Shen, Weiwei, Bin Wang, Jian Pu, and Jun Wang. "The Kelly Growth Optimal Portfolio with Ensemble Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1134–41. http://dx.doi.org/10.1609/aaai.v33i01.33011134.

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As a competitive alternative to the Markowitz mean-variance portfolio, the Kelly growth optimal portfolio has drawn sufficient attention in investment science. While the growth optimal portfolio is theoretically guaranteed to dominate any other portfolio with probability 1 in the long run, it practically tends to be highly risky in the short term. Moreover, empirical analysis and performance enhancement studies under practical settings are surprisingly short. In particular, how to handle the challenging but realistic condition with insufficient training data has barely been investigated. In or

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Ece, Oguzhan, and Ahmet Serhat Uludag. "Applicability of Fuzzy TOPSIS Method in Optimal Portfolio Selection and an Application in BIST." International Journal of Economics and Finance 9, no. 10 (2017): 107. http://dx.doi.org/10.5539/ijef.v9n10p107.

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General structure of saving-investment cycle and the effectiveness of this structure are included in the most significant issues of the financial system. One of the points of intervention in providing an effective saving-investment cycle is possible through channeling the savings toward optimal investment fields. This study aims at detecting the existence of alternative methods in determining optimal selection combination in the risk and revenue perspective of individual and corporate investors who would like to evaluate their savings in capital markets. For this purpose, the applicability of

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Mirza, Nawazishv, and Daniel Danny Simatupang. "Comparative Systematic Risk Analysis: Evidence on the Banking Sector in the United States, Western Europe and South East Asia." LAHORE JOURNAL OF ECONOMICS 9, no. 1 (2004): 149–73. http://dx.doi.org/10.35536/lje.2004.v9.i1.a7.

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The basis for asset pricing in financial markets was provided by Bachelier (1900) in his magnificent dissertation “Théorie de la Spéculation” submitted at Sorbonne (Université de Paris). Although from today’s perspective, the mathematics and economics he applied were flawed, yet the great genius, Markowitz, declares this early work as an inspiration for his own classical paper of “Portfolio Selection”. The risk return relationship has always been a debatable issue in financial theory. “Portfolio Selection” came up with a meaningful measure of quantifying the risk associated with investment; th

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Kuang Yu Huang, ChuenJiuan Jane, and TingCheng Chang. "An Enhanced Approach to Optimizing the Stock Portfolio Selection based on Modified Markowitz MV Method." Journal of Convergence Information Technology 6, no. 2 (2011): 226–39. http://dx.doi.org/10.4156/jcit.vol6.issue2.24.

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