Relevant bibliographies by topics / Markowitz Mean Variance model

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Academic literature on the topic 'Markowitz Mean Variance model'

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Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Markowitz Mean Variance model"

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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 the efficient frontier and total wealth of both models, our model can provide a more measurable standard of judgment when investors do their investment.
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Negara, I. Nyoman Wijaya, Yohanes A. R. Langi, and Tohap Manurung. "Analisis Portofolio Saham Model Mean – Variance Markowitz Menggunakan Metode Lagrange." d'CARTESIAN 9, no. 2 (January 7, 2021): 173. http://dx.doi.org/10.35799/dc.9.2.2020.29656.

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Investor yang berinvestasi dalam bentuk portofolio perlu melakukan analisis terhadap tingkat keuntungan dan risiko yang berhubungan searah, yang artinya besar keuntungan akan sesuai dengan besar risikonya. Pada penelitian ini digunakan model mean – variance Markowitz yang kemudian dioptimasi menggunakan metode Lagrange. Tujuan penelitian ini untuk mengetahui saham – saham apa saja yang layak dimasukkan kedalam portofolio dengan menentukan proporsi bobot masing – masing saham sehingga menjadi portofolio optimal. Penelitian ini menggunakan data closing price harian selama 1 tahun pada saham – saham yang terdaftar pada periode listing IDX30 Agustus 2018 dan Februari 2019. Dari hasil penelitian ini, diperoleh 18 saham yang layak dimasukkan kedalam portofolio yang sebelumnya berjumlah 28 saham. Portofolio yang terbentuk ada 2, portofolio yang pertama dengan expected return 0,002% diperoleh risiko sebesar 0,0095% dengan proporsi bobot tertinggi yaitu saham BBCA sebesar 0,4654% dan portofolio kedua dengan expected return 0,003% diperoleh risiko sebesar 0,0136% dengan proporsi bobot tertinggi yaitu saham BBCA sebesar 0,5663%.
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Basuki, Basuki, F. Sukono, and Ema Carnia. "Model Optimisasi Portofolio Investasi Mean-Variance Tanpa dan Dengan Aset Bebas Risiko pada Saham Idx30." Jurnal Matematika Integratif 12, no. 2 (July 13, 2017): 107. http://dx.doi.org/10.24198/jmi.v12.n2.11927.107-116.

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Dalam paper ini, model optimisasi portofolio investasi Mean-Variance tanpa aset bebas risiko, ataudisebut model dasar dari Markowitz telah dikaji untuk mendapatkan portofolio optimum.Berdasarkanmodel dasar dari Markowitz, kemudian dilakukan studi lebih lanjut pada model Mean-Variance denganaset bebas risiko. Selanjutnya, kedua model tersebut digunakan untuk menganalisis optimisasi portofolioinvestasi pada beberapa saham IDX30. Dalam paper ini diasumsikan bahwa proporsi sebesar 10%diinvestasikan pada aset bebas risiko, berupa deposito yang memberikan return sebesar 7% per tahun.Berdasarkan hasil analisis optimisasi portofolio investasi pada lima saham yang dipilih didapatkan grafikpermukaan efisien dari optimisasi portofolio Mean-Variance dengan aset bebas risiko, berada lebih tinggidibandingkan optimisasi portofolio Mean-Variance tanpa aset bebas risiko. Dalam hal ini menunjukkanbahwa portofolio investasi kombinasi dari aset bebas risiko dan aset tanpa bebas risiko, lebihmenguntungkan dibandingkan portofolio investasi yang hanya pada aset tanpa bebas risiko.
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Basuki, Basuki, F. Sukono, and Ema Carnia. "Model Optimisasi Portofolio Investasi Mean-Variance Tanpa dan Dengan Aset Bebas Risiko pada Saham Idx30." Jurnal Matematika Integratif 12, no. 2 (July 11, 2017): 41. http://dx.doi.org/10.24198/jmi.v12.n2.11927.41-50.

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Dalam paper ini, model optimisasi portofolio investasi Mean-Variance tanpa aset bebas risiko, ataudisebut model dasar dari Markowitz telah dikaji untuk mendapatkan portofolio optimum.Berdasarkanmodel dasar dari Markowitz, kemudian dilakukan studi lebih lanjut pada model Mean-Variance denganaset bebas risiko. Selanjutnya, kedua model tersebut digunakan untuk menganalisis optimisasi portofolioinvestasi pada beberapa saham IDX30. Dalam paper ini diasumsikan bahwa proporsi sebesar 10%diinvestasikan pada aset bebas risiko, berupa deposito yang memberikan return sebesar 7% per tahun.Berdasarkan hasil analisis optimisasi portofolio investasi pada lima saham yang dipilih didapatkan grafikpermukaan efisien dari optimisasi portofolio Mean-Variance dengan aset bebas risiko, berada lebih tinggidibandingkan optimisasi portofolio Mean-Variance tanpa aset bebas risiko. Dalam hal ini menunjukkanbahwa portofolio investasi kombinasi dari aset bebas risiko dan aset tanpa bebas risiko, lebihmenguntungkan dibandingkan portofolio investasi yang hanya pada aset tanpa bebas risiko.
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Basuki, Basuki, F. Sukono, and Ema Carnia. "Model Optimisasi Portofolio Investasi Mean-Variance Tanpa dan Dengan Aset Bebas Risiko pada Saham Idx30." Jurnal Matematika Integratif 12, no. 2 (July 13, 2017): 107. http://dx.doi.org/10.24198/jmi.v12i2.11927.

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Dalam paper ini, model optimisasi portofolio investasi Mean-Variance tanpa aset bebas risiko, ataudisebut model dasar dari Markowitz telah dikaji untuk mendapatkan portofolio optimum.Berdasarkanmodel dasar dari Markowitz, kemudian dilakukan studi lebih lanjut pada model Mean-Variance denganaset bebas risiko. Selanjutnya, kedua model tersebut digunakan untuk menganalisis optimisasi portofolioinvestasi pada beberapa saham IDX30. Dalam paper ini diasumsikan bahwa proporsi sebesar 10%diinvestasikan pada aset bebas risiko, berupa deposito yang memberikan return sebesar 7% per tahun.Berdasarkan hasil analisis optimisasi portofolio investasi pada lima saham yang dipilih didapatkan grafikpermukaan efisien dari optimisasi portofolio Mean-Variance dengan aset bebas risiko, berada lebih tinggidibandingkan optimisasi portofolio Mean-Variance tanpa aset bebas risiko. Dalam hal ini menunjukkanbahwa portofolio investasi kombinasi dari aset bebas risiko dan aset tanpa bebas risiko, lebihmenguntungkan dibandingkan portofolio investasi yang hanya pada aset tanpa bebas risiko.
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Naqvi, Hassan. "On the validity of the Capital Asset Pricing Model." LAHORE JOURNAL OF ECONOMICS 5, no. 1 (January 1, 2000): 73–92. http://dx.doi.org/10.35536/lje.2000.v5.i1.a4.

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One of the most important developments of modern finance is the Capital Asset Pricing Model (CAPM) of Sharpe, Lintner and Mossin. Although the model has been the subject of several academic papers, it is still exposed to theoretical and empirical criticisms. The CAPM is based on Markowitz’s (1959) mean variance analysis. Markowitz demonstrated that rational investors would hold assets, which offer the highest possible return for a given level of risk, or conversely assets with the minimum level of risk for a specific level of return.
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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 (September 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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Steinbach, Marc C. "Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis." SIAM Review 43, no. 1 (January 2001): 31–85. http://dx.doi.org/10.1137/s0036144500376650.

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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 (January 2016): 124–51. http://dx.doi.org/10.1137/15m1016357.

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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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Dissertations / Theses on the topic "Markowitz Mean Variance model"

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Dantas, Allan Leão. "Otimização multiperíodo por média-variância sem posições a descoberto em ativos de risco." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-13122006-174247/.

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Inicialmente neste trabalho são apresentados os conceitos básicos de média e variância e como estes se aplicam na caracterização de um ativo ou carteira de investimento. Posteriormente são apresentadas as estratégias ótimas de investimento para o modelo de Markowitz sem posições a descoberto em ativos de risco, e sem tal restrição. Ainda neste trabalho é apresentada uma breve revisão do modelo de tempo contínuo para o problema de média-variância sem posições a descoberto em ativos de risco, e como objetivo principal do mesmo é proposto um modelo em tempo discreto multiperíodo a partir do modelo de tempo contínuo, o qual é implementado computacionalmente para o mercado de capitais brasileiro. O resultado obtido é comparado com a estratégia de período único do modelo de Markowitz sem posições a descoberto em ativos de risco, sendo este modelo aplicado sequencialmente no horizonte de tempo considerado para o modelo multiperíodo.
Initially in this work are presented the basics concepts of mean and variance and how they are applied to quantify an asset or a portfolio. After this we present the optimal investment strategy of the Markowitz no-shorting constraints mean-variance portfolio selection in single period and the Markowitz optimal investment strategy without such constrain. Following this, we present a short review of the continuous-time dynamic model for the mean-variance portfolio selection with no-shorting constraints in risky assets problem. As the main objective of this work we propose a discrete time multiperiod model based on the continuous-time portfolio selection with no-shorting constraints in risky assets, that is applied to the Brazilian financial market. This result is compared with the investment strategy of the Markowitz no-shorting constraints mean-variance portfolio selection in single period applied sequentially in the multiperiod case.
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Sowunmi, Ololade. "Finanční optimalizace." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417164.

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This thesis presents two Models of portfolio optimization, namely the Markowitz Mean Variance Optimization Model and the Rockefeller and Uryasev CVaR Optimization Model. It then presents an application of these models to a portfolio of clean energy assets for optimal allocation of financial resources in terms of maximum returns and low risk. This is done by writing GAMS programs for these optimization problems. An in-depth analysis of the results is conducted, and we see that the difference between both models is not very significant even though these results are data-specific.
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Isaksson, Daniel. "Robust portfolio optimization with Expected Shortfall." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187888.

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This thesis project studies robust portfolio optimization with Expected Short-fall applied to a reference portfolio consisting of Swedish linear assets with stocks and a bond index. Specifically, the classical robust optimization definition, focusing on uncertainties in parameters, is extended to also include uncertainties in log-return distribution. My contribution to the robust optimization community is to study portfolio optimization with Expected Shortfall with log-returns modeled by either elliptical distributions or by a normal copula with asymmetric marginal distributions. The robust optimization problem is solved with worst-case parameters from box and ellipsoidal un-certainty sets constructed from historical data and may be used when an investor has a more conservative view on the market than history suggests. With elliptically distributed log-returns, the optimization problem is equivalent to Markowitz mean-variance optimization, connected through the risk aversion coefficient. The results show that the optimal holding vector is almost independent of elliptical distribution used to model log-returns, while Expected Shortfall is strongly dependent on elliptical distribution with higher Expected Shortfall as a result of fatter distribution tails. To model the tails of the log-returns asymmetrically, generalized Pareto distributions are used together with a normal copula to capture multivariate dependence. In this case, the optimization problem is not equivalent to Markowitz mean-variance optimization and the advantages of using Expected Shortfall as risk measure are utilized. With the asymmetric log-return model there is a noticeable difference in optimal holding vector compared to the elliptical distributed model. Furthermore the Expected Shortfall in-creases, which follows from better modeled distribution tails. The general conclusions in this thesis project is that portfolio optimization with Expected Shortfall is an important problem being advantageous over Markowitz mean-variance optimization problem when log-returns are modeled with asymmetric distributions. The major drawback of portfolio optimization with Expected Shortfall is that it is a simulation based optimization problem introducing statistical uncertainty, and if the log-returns are drawn from a copula the simulation process involves more steps which potentially can make the program slower than drawing from an elliptical distribution. Thus, portfolio optimization with Expected Shortfall is appropriate to employ when trades are made on daily basis.
Examensarbetet behandlar robust portföljoptimering med Expected Shortfall tillämpad på en referensportfölj bestående av svenska linjära tillgångar med aktier och ett obligationsindex. Specifikt så utvidgas den klassiska definitionen av robust optimering som fokuserar på parameterosäkerhet till att även inkludera osäkerhet i log-avkastningsfördelning. Mitt bidrag till den robusta optimeringslitteraturen är att studera portföljoptimering med Expected Shortfall med log-avkastningar modellerade med antingen elliptiska fördelningar eller med en norma-copul med asymmetriska marginalfördelningar. Det robusta optimeringsproblemet löses med värsta tänkbara scenario parametrar från box och ellipsoid osäkerhetsset konstruerade från historiska data och kan användas när investeraren har en mer konservativ syn på marknaden än vad den historiska datan föreslår. Med elliptiskt fördelade log-avkastningar är optimeringsproblemet ekvivalent med Markowitz väntevärde-varians optimering, kopplade med riskaversionskoefficienten. Resultaten visar att den optimala viktvektorn är nästan oberoende av vilken elliptisk fördelning som används för att modellera log-avkastningar, medan Expected Shortfall är starkt beroende av elliptisk fördelning med högre Expected Shortfall som resultat av fetare fördelningssvansar. För att modellera svansarna till log-avkastningsfördelningen asymmetriskt används generaliserade Paretofördelningar tillsammans med en normal-copula för att fånga det multivariata beroendet. I det här fallet är optimeringsproblemet inte ekvivalent till Markowitz väntevärde-varians optimering och fördelarna med att använda Expected Shortfall som riskmått används. Med asymmetrisk log-avkastningsmodell uppstår märkbara skillnader i optimala viktvektorn jämfört med elliptiska fördelningsmodeller. Därutöver ökar Expected Shortfall, vilket följer av bättre modellerade fördelningssvansar. De generella slutsatserna i examensarbetet är att portföljoptimering med Expected Shortfall är ett viktigt problem som är fördelaktigt över Markowitz väntevärde-varians optimering när log-avkastningar är modellerade med asymmetriska fördelningar. Den största nackdelen med portföljoptimering med Expected Shortfall är att det är ett simuleringsbaserat optimeringsproblem som introducerar statistisk osäkerhet, och om log-avkastningar dras från en copula så involverar simuleringsprocessen flera steg som potentiellt kan göra programmet långsammare än att dra från en elliptisk fördelning. Därför är portföljoptimering med Expected Shortfall lämpligt att använda när handel sker på daglig basis.
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McLeod, Warren. "Enhancements to the Markowitz mean-variance optimisation process of asset allocation." Master's thesis, University of Cape Town, 1998. http://hdl.handle.net/11427/9687.

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Includes bibliographical references.
[The focus of this thesis is on the practical application of portfolio selection. It is a field that receives much attention, no more so than after the world market crashes (i.e. October 1997) which highlighted the importance of risk management. Consequently there is a need to examine the current tools in current use to create our portfolios and to look at ways in which they could be improved. The Bayesians have certainly contributed in this area, and more noticeably in the 1990's. We shall examine their contributions quite extensively in this thesis.
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Djehiche, Younes, and Erik Bröte. "Implementation of mean-variance and tail optimization based portfolio choice on risky assets." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-198071.

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An asset manager's goal is to provide a high return relative the risk taken, and thus faces the challenge of how to choose an optimal portfolio. Many mathematical methods have been developed to achieve a good balance between these attributes and using di erent risk measures. In thisthesis, we test the use of a relatively simple and common approach: the Markowitz mean-variance method, and a more quantitatively demanding approach: the tail optimization method. Using active portfolio based on data provided by the Swedish fund management company Enter Fonderwe implement these approaches and compare the results. We analyze how each method weighs theunderlying assets in order to get an optimal portfolio.
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Fan, Kevin, and Rasmus Larsson. "Portföljoptimering med courtageavgifter." Thesis, KTH, Optimeringslära och systemteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146748.

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Ever since it was first introduced in an article in the Journal of Finance 1952, Harry Markowitz’ mean - variance model for portfolio selection has become one of the best known models in finance. The model was one of the first in the world to deal with portfolio optimization mathematically and have directly or indirectly inspired the rest of the world to develop new portfolio optimization methods. Although the model is one of the greatest contributions to modern portfolio theory, critics claim that it may have practical difficulties. Partly because the Markowitz model is based on various assumptions which do not necessarily coincide with the reality. The assumptions which are based on the financial markets and investor behavior contain the simplification that there are no transaction costs associated with financial trading. However, in reality, all financial products are subject to transaction costs such as brokerage fees and taxes. To determine whether this simplification leads to inaccurate results or not, we derive an extension of the mean-variance optimization model which includes brokerage fees occurred under the construction of an investment portfolio. We then compare our extension of the Markowitz model, including transaction costs, with the standard model. The results indicate that brokerage fees have a negligible effect on the standard model if the investor's budget is relatively large. Hence the assumption that no brokerage fees occur when trading financial securities seems to be an acceptable simplification if the budget is relatively high. Finally, we suggest that brokerage fees are negligible if the creation of the portfolio and hence the transactions only occurs once. However if an investor is active and rebalances his portfolio often, the brokerage fees could be of great importance.
Harry Markowitz portföljoptimeringsmodell har sedan den publicerades år 1952 i en artikel i the journal of Finance, blivit en av de mest använda modellerna inom finansvärlden. Modellen var en av dem första i världen att hantera portföljoptimering matematiskt och har direkt eller indirekt inspirerat omvärlden att utveckla nya portföljoptimeringsmetoder. Men trots att Markowitz modell är ett av de största bidragen till dagens portföljoptimeringsteori har kritiker hävdat att den kan ha praktiska svårigheter. Detta delvis på grund av att modellen bygger på olika antaganden som inte nödvändigtvis stämmer överens med verkligheten. Antagandena, som är baserad på den finansiella marknaden och individers investeringsbeteende, leder till förenklingen att transaktionskostnader inte förekommer i samband med finansiell handel. Men i verkligheten förekommer transaktions-kostnader som courtageavgifter och skatter nästintill alltid vid handel av finansiella produkter som t.ex. värdepapper. För att avgöra om modellen påvisar felaktiga resultat på grund av bortfallet av courtageavgifter härleds en utvidgning av Markowitz modell som inkluderar courtageavgifter. Utvidgningen av Markowitz modell jämförs sedan med originalmodellen. Resultaten tyder på att courtageavgifter har en försumbar effekt på originalmodellen om investeraren har en stor investeringsbudget. Slutsatsen är därför att, förenklingen att inga courtageavgifter förekommer är en acceptabel förenkling om investeringsbudgeten är stor. Det föreslås slutligen att courtageavgiften är försumbar om transaktionen av aktier endast sker en gång. Men om en investerare är aktiv och ombalanserar sin portfölj flitigt, kan courtageavgifterna vara av stor betydelse.
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Abdumuminov, Shuhrat, and David Emanuel Esteky. "Black-Litterman Model: Practical Asset Allocation Model Beyond Traditional Mean-Variance." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-32427.

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This paper consolidates and compares the applicability and practicality of Black-Litterman model versus traditional Markowitz Mean-Variance model. Although well-known model such as Mean-Variance is academically sound and popular, it is rarely used among asset managers due to its deficiencies. To put the discussion into context we shed light on the improvement made by Fisher Black and Robert Litterman by putting the performance and practicality of both Black- Litterman and Markowitz Mean-Variance models into test. We will illustrate detailed mathematical derivations of how the models are constructed and bring clarity and profound understanding of the intuition behind the models. We generate two different portfolios, composing data from 10-Swedish equities over the course of 10-year period and respectively select 30-days Swedish Treasury Bill as a risk-free rate. The resulting portfolios orientate our discussion towards the better comparison of the performance and applicability of these two models and we will theoretically and geometrically illustrate the differences. Finally, based on extracted results of the performance of both models we demonstrate the superiority and practicality of Black-Litterman model, which in our particular case outperform traditional Mean- Variance model.
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Naidoo, Lushan. "A Markowitz mean-variance analysis of hedge fund investments for multi-asset class portfolio holders in South Africa." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/28981.

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This research aims to provide insight into the hedge fund industry in South Africa. The focus is on retirement funds and the use of hedge funds in a multi-asset class portfolio. Diversification is an important tool for portfolio managers who make use of correlation to achieve higher risk-adjusted returns for investors. As such this paper tests whether higher risk-adjusted returns can be achieved in well diversified multi-asset class portfolios if hedge funds are included. To test for the optimal risk-adjusted returns that can be achieved, mean-variance, mean-semi variance and Omega portfolios were created. The results suggest that portfolios that include hedge fund investments outperformed those that exclude it using mean-variance, mean-semi variance and Omega analysis. Furthermore it was found that portfolios that included Pure Hedge Funds outperformed those that included Fund of Hedge Funds. The evidence suggests that hedge fund investments should be included in a well-diversified South African multi-asset class portfolio, with Pure Hedge Funds being preferred to Fund of Hedge Funds.
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Anane, Asomani Kwadwo. "Sustainability for Portfolio Optimization." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44560.

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The 2007-2008 financial crash and the looming climate change and global warming have heightened interest in sustainable investment. But whether the shift is as a result of the financial crash or a desire to preserve the environment, a sustainable investment might be desirable. However, to maintain this interest and to motivate investors in indulging in sustainability, there is the need to show the possibility of yielding positive returns. The main objective of the thesis is to investigate whether the sustainable investment can lead to higher returns. The thesis focuses primarily on incorporating sustainability into Markowitz portfolio optimization. It looks into the essence of sustainability and its impact on companies by comparing different concepts. The analysis is based on the 30 constituent stocks from the Dow Jones industrial average or simply the Dow. The constituents stocks of the Dow, from 2007-12-31 to 2018-12-31 are investigated. The thesis compares the cumulative return of the Dow with the sustainable stocks in the Dow based on their environmental, social and governance (ESG) rating. The results are then compared with the Dow Jones Industrial Average denoted by the symbol (^DJI) which is considered as the benchmark for my analysis. The constituent stocks are then optimized based on the Markowitz mean-variance framework and a conclusion is drawn from the constituent stocks, ESG, environmental, governance and social asset results. It was realized that the portfolio returns for stocks selected based on their environmental and governance ratings were the highest performers. This could be due to the fact that most investors base their investment selection on the environmental and governance performance of companies and the demand for stocks in that category could have gone up over the period, contributing significantly to their performance.
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Strid, Alexander, and Daniel Liu. "Evaluation of a Portfolio in Dow Jones Industrial Average Optimized by Mean-Variance Analysis." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275662.

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This thesis evaluates the mean-variance analysis framework by comparing the performance of an optimized portfolio consisting of stocks from the Dow Jones Industrial Average to the performance of the Dow Jones Industrial Average index itself. The results show that the optimized portfolio performs better than the corresponding index when evaluated on the period between 2015 and 2019. However, the variance of the returns are high and therefore it is difficult to determine if mean-variance analysis performs better than its corresponding index in the general case. Furthermore, it is shown that individual stocks can still influence the movement of an optimized portfolio significantly, even though the model is supposed to diversify firm-specific risk. Thus, the authors recommend modifying the model by restricting the amount that is allowed to be invested in a single stock, if one wishes to apply mean-variance analysis in reality. To be able to draw further conclusions, more practical research within the subject needs to be done.
Denna uppsats utvärderar ramverket ”mean-variance analysis” genom att jämföra prestandan av en optimerad portfölj bestående av aktier från Dow Jones Industrial Average med prestandan av indexet Dow Jones Industrial Average självt. Resultaten visar att att den optimerade portföljen presterar bättre än motsvarande index när de utvärderas på perioden 2015 till 2019. Dock är variansen av avkastningen hög och det är därför svårt att bedöma om mean-variance analysis generellt sett presterar bättre än sitt motsvarande index. Vidare visas det att individuella aktier fortfarande kan påverka den optimerade portföljens rörelser, fastän modellen antas diversifiera företagsspecifik risk. På grund av detta rekommenderar författarna att modifiera modellen genom att begränsa mängden som kan investeras i en individuell aktie, om man önskar att tillämpa mean-variance analysis i verkligheten. För att kunna dra vidare slutsatser så krävs mer praktisk forskning inom området.
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Books on the topic "Markowitz Mean Variance model"

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McEntegart, Karen. A comparison of mean-variance and mean-semivariance capital asset models : evidence from the Irish stock market. Dublin: University College Dublin, 1994.

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Back, Kerry E. Portfolio Choice. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0002.

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The portfolio choice model is introduced, and the first‐order condition is derived. Properties of the demand for a single risky asset are derived from second‐order risk aversion and decreasing absolute risk aversion. Optimal investments are independent of initial wealth for investors with constant absolute risk aversion. Optimal investments are affine functions of initial wealth for investors iwth linear risk tolerance. The optimal portfolio for an investor with constant absolute risk aversion is derived when asset returns are normally distributed. Investors with quadratic utility have mean‐variance preferences, and investors have mean‐variance preferences when returns are elliptically distributed.
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Back, Kerry E. Factor Models. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0006.

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The CAPM and factor models in general are explained. Factors can be replaced by the returns or excess returns that are maximally correlated (the projections of the factors). A factor model is equivalent to an affine representation of an SDF and to spanning a return on the mean‐variance frontier. The use of alphas for performance evaluation is explained. Statistical factor models are defined as models in which factors explain the covariance matrix of returns. A proof is given of the Arbitrage Pricing Theory, which states that statistical factors are approximate pricing factors. The CAPM and the Fama‐French‐Carhart model are evaluated relative to portfolios based on sorts on size, book‐to‐market, and momentum.
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Milliken, Christopher, Ehsan Nikbakht, and Andrew Spieler. Traditional Asset Allocation Securities. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190269999.003.0020.

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Asset allocation models have evolved in complexity with the development of modern portfolio theory, but they continue to operate under the assumption of investor rationality and other assumptions that do not hold in the real world. For this reason, academics and industry professionals make efforts to understand the behavioral biases of decision makers and the implications these biases have on asset allocation strategies. This chapter reviews the building blocks of asset allocation, involving stocks, bonds, real estate, and cash. It also examines the history and theory behind two of the most popular portfolio management strategies: mean-variance optimization and the Black-Litterman Model. Finally, the chapter examines five common behavioral biases that have direct implications for asset allocation: familiarity, status quo, framing, mental accounting, and overconfidence. Each behavioral bias discussion contains examples, warning signs, and steps to correct the emotional or cognitive errors in decision making.
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Nielsen, François. Genes and Status Achievement. Edited by Rosemary L. Hopcroft. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190299323.013.22.

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A number of human traits that are predictive of socioeconomic success (e.g., intelligence, certain personality traits, and educational attainment) or reflective of success (e.g., occupational prestige and earnings) have been found to be substantially affected by individual genetic endowments; some outcomes, such as educational attainment, are also affected by the family environment, although usually to a lesser extent. The associations among status-related traits are themselves largely due to genetic causes. By reshuffling the genes of parents at each generation, sexual reproduction produces a regression of status-relevant traits of offspring toward the population mean—downward for high-status parents, upward for low-status parents—generating social mobility in an achievement-oriented society. Incorporating the quantitative genetic decomposition of trait variance into genetic, shared environmental, and nonshared environmental sources into the classic sociological model of status achievement allows for a better understanding and measurement of central social stratification concepts, such as opportunity and ascription.
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Book chapters on the topic "Markowitz Mean Variance model"

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Evstigneev, Igor V., Thorsten Hens, and Klaus Reiner Schenk-Hoppé. "Mean-Variance Portfolio Analysis: The Markowitz Model." In Springer Texts in Business and Economics, 11–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16571-4_2.

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Deng, Guang-Feng, and Woo-Tsong Lin. "Ant Colony Optimization for Markowitz Mean-Variance Portfolio Model." In Swarm, Evolutionary, and Memetic Computing, 238–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17563-3_29.

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Ang, Clifford S. "Markowitz Mean-Variance Optimization." In Springer Texts in Business and Economics, 209–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14075-9_7.

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Ang, Clifford S. "Markowitz Mean–Variance Optimization." In Springer Texts in Business and Economics, 197–223. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64155-9_7.

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Hangay, George, Susan V. Gruner, F. W. Howard, John L. Capinera, Eugene J. Gerberg, Susan E. Halbert, John B. Heppner, et al. "Mean-Variance Model." In Encyclopedia of Entomology, 2313. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_1761.

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Chung, Kai Lai, and Farid AitSahlia. "Mean-Variance Pricing Model." In Undergraduate Texts in Mathematics, 329–58. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21548-8_9.

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Qin, Zhongfeng. "Credibilistic Mean-Variance-Skewness Model." In Uncertainty and Operations Research, 29–52. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1810-7_2.

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Qin, Zhongfeng. "Uncertain Random Mean-Variance Model." In Uncertainty and Operations Research, 131–49. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1810-7_8.

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Board, John L. G., Charles M. S. Sutcliffe, and William T. Ziemba. "Portfolio Theory: Mean-Variance Model." In Encyclopedia of Operations Research and Management Science, 1142–48. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_775.

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Hens, Thorsten, and Marc Oliver Rieger. "Two-Period Model: Mean-Variance Approach." In Financial Economics, 95–140. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-36148-0_3.

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Conference papers on the topic "Markowitz Mean Variance model"

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Du, Shouyan, Yongze Sun, Yonghong Hu, and Zhonghua Lu. "Implementation of Markowitz Mean-Variance Model Based on Matrix-Valued Factor Algorithm." In 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 2019. http://dx.doi.org/10.1109/bigdatasecurity-hpsc-ids.2019.00025.

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Wan, Shuping. "Mean-variance Portfolio Model with Consumption." In 2006 9th International Conference on Control, Automation, Robotics and Vision. IEEE, 2006. http://dx.doi.org/10.1109/icarcv.2006.345085.

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Hoe, Lam Weng, and Lam Weng Siew. "Portfolio optimization with mean-variance model." In INNOVATIONS THROUGH MATHEMATICAL AND STATISTICAL RESEARCH: Proceedings of the 2nd International Conference on Mathematical Sciences and Statistics (ICMSS2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4952526.

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Chen, Guohua, and Xiaolian Liao. "Credibility Mean-Variance-skewness Portfolio Selection Model." In 2010 2nd International Workshop on Database Technology and Applications (DBTA). IEEE, 2010. http://dx.doi.org/10.1109/dbta.2010.5659059.

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Pan, Qiming, and Xiaoxia Huang. "Mean-Variance Model for International Portfolio Selection." In 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2008. http://dx.doi.org/10.1109/euc.2008.16.

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Zhao, Zhenwu. "Mean-Variance Model for Venture Investment Decision-Making." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2356.

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Lin, Lili, and Li Li. "Different mean-variance model based on compositional data." In 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2017. http://dx.doi.org/10.1109/iciea.2017.8282929.

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Li, Jiangfeng, and Qiong Wu. "Mean-Variance Newsvendor Model with a Background Risk." In 5th International Asia Conference on Industrial Engineering and Management Innovation (IEMI 2014). Paris, France: Atlantis Press, 2014. http://dx.doi.org/10.2991/iemi-14.2014.11.

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Bai, Linquan, Qinran Hu, Fangxing Li, Tao Ding, and Hongbin Sun. "Robust mean-variance optimization model for grid-connected microgrids." In 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015. http://dx.doi.org/10.1109/pesgm.2015.7286489.

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Yao, Hai-xiang, and Qing-hua Ma. "Continuous-Time Mean-Variance Model with Uncertain Exit Time." In 2010 International Conference on Management and Service Science (MASS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmss.2010.5576367.

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