scholarly journals Analisis Portofolio Investasi dengan Metode Multi Objektif

Jurnal Varian ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 6-12
Author(s):  
Gilang Primajati ◽  
Ahmad Zuli Amrullah ◽  
Ahmad Ahmad
Keyword(s):  
Minimum Variance ◽  
Optimal Portfolio ◽  
Risk Level ◽  
Risk Averse ◽  
Expected Return ◽  
Risky Assets ◽  
Variant Method ◽  
The Mean ◽  
Mean Variance ◽  
Constraint Method

In the formation of an efficient portfolio, many methods can be used. Of course with its own assumptions and advantages. In the process, reasonable investor assumptions tend to be risk averse. Investors who are risk averse are investors who, when faced with two investments with the same expected return, will choose an investment with a lower risk level. If an investor has several efficient portfolio choices, then the most optimal portfolio will be chosen. Optimal portfolio with mean-variance efficient portfolio criteria, investors only invest in risky assets. Investors do not include risk free assets in their portfolios. Mean-variance efficient portfolio is defined as a portfolio that has a minimum variance among all possible portfolio that can be formed, at the mean level of the same expected return. The mean variant method of the two constraints can be used as a basis in determining the optimal portfolio weight by minimizing the risk of portfolio return with two constraints. In this article the problem referred to is symbolized by lamda and beta. With this two-constraint method, the results obtained are more detailed so that they can describe the results of a sharper analysis for an investor.

ANALISIS PORTOFOLIO INVESTASI PADA SAHAM LQ45 DENGAN METODE MEAN VARIAN SATU KONSTRAIN

Jurnal Varian ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 22-29
Author(s):  
Gilang Primajati
Keyword(s):  
Capital Markets ◽  
Minimum Variance ◽  
Return Rate ◽  
Optimal Portfolio ◽  
Portfolio Diversification ◽  
Portfolio Risk ◽  
Expected Return ◽  
Positive Weight ◽  
The Mean

In the capital markets, especially the investment market, the establishment of a portfolio is something that must be understood by investors. Portfolio formation by investors to maximize profits as much as possible by minimizing the risk of losses that may occur. Portfolio diversification is defined as portfolio formation in such a way that it can reduce portfolio risk without sacrificing returns. Optimal portfolio with efficient-portfolio mean criteria, investors only invest in risk assets only. Investors do not include risk free assets in their portfolios. The efficient variance portfolio is defined as a portfolio that has minimum variance among the overall possible portfolio that can be formed, at the same expected return rate. The mean method of one constraint variant can be used as the basis for optimal portfolio determination. The shares of LQ-45 used are shares of AALI, BBCA, UNVR, TLKM and ADHI. AALI shares received a positive weight of 7%, BBCA 48%, UNVR 16%, TLKM 26% and ADHI 3%

Markowitz Theory in Portfolio Optimization for Heavy Tailed Assets

2019 ◽  
Vol 1 (3) ◽  
pp. 1-14
Author(s):  
Wong Ghee Ching ◽  
Che Mohd Imran Che Taib
Keyword(s):  
Portfolio Optimization ◽  
Composite Index ◽  
Minimum Variance ◽  
Optimization Techniques ◽  
Expected Return ◽  
Markowitz Model ◽  
The Mean ◽  
Heavy Tailed ◽  
Mean Variance Portfolio ◽  
Mean Variance

This paper aims at solving an optimization problem in the presence of heavy tail behavior of financial assets. The question of minimizing risk subjected to a certain expected return or maximizing return for a given expected risk are two objective functions to be solved using Markowitz model. The Markowitz based strategies namely the mean variance portfolio, minimum variance portfolio and equally weighted portfolio are proposed in conjunction with mean and variance analysis of the portfolio. The historical prices of stocks traded at Bursa Malaysia are used for empirical analysis. We employed CAPM in order to investigate the performance of the Markowitz model which was benchmarked with risk adjusted KLSE Composite Index. We performed a backtesting study of portfolio optimization techniques defined under modern portfolio theory in order to find the optimal portfolio. Our findings showed that the mean variance portfolio outperformed the other two strategies in terms of performance of investment for heavy tailed assets.

scholarly journals Optimal Risk Budgeting under a Finite Investment Horizon

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 86
Author(s):  
Marcos López de Prado ◽  
Ralph Vince ◽  
Qiji Jim Zhu
Keyword(s):  
Quantitative Methods ◽  
Optimal Portfolio ◽  
Investment Horizon ◽  
Risk Averse ◽  
Growth Optimal Portfolio ◽  
Risk Budgeting ◽  
Risk Parity ◽  
Risk Neutral ◽  
Mean Variance

The Growth-Optimal Portfolio (GOP) theory determines the path of bet sizes that maximize long-term wealth. This multi-horizon goal makes it more appealing among practitioners than myopic approaches, like Markowitz’s mean-variance or risk parity. The GOP literature typically considers risk-neutral investors with an infinite investment horizon. In this paper, we compute the optimal bet sizes in the more realistic setting of risk-averse investors with finite investment horizons. We find that, under this more realistic setting, the optimal bet sizes are considerably smaller than previously suggested by the GOP literature. We also develop quantitative methods for determining the risk-adjusted growth allocations (or risk budgeting) for a given finite investment horizon.

scholarly journals Mean-Variance Portfolio Selection with Tracking Error Penalization

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1915
Author(s):  
William Lefebvre ◽  
Grégoire Loeper ◽  
Huyên Pham
Keyword(s):  
Portfolio Selection ◽  
Tracking Error ◽  
Efficient Frontier ◽  
Minimum Variance ◽  
Sharpe Ratio ◽  
Portfolio Strategy ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Variance Criterion

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.

scholarly journals SCALED AND STABLE MEAN-VARIANCE-EVAR PORTFOLIO SELECTION STRATEGY WITH PROPORTIONAL TRANSACTION COSTS

2017 ◽  
Vol 18 (4) ◽  
pp. 561-584 ◽  
Author(s):  
Ebenezer Fiifi Emire ATTA MILLS ◽  
Bo YU ◽  
Jie YU
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Downside Risk ◽  
Estimation Risk ◽  
Expected Return ◽  
Proportional Transaction Costs ◽  
Portfolio Returns ◽  
L2 Norm ◽  
The Mean ◽  
Mean Variance

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. The results of this paper extend the classical Markowitz model to the case of proportional transaction costs and enhance the efficiency of portfolio selection by alleviating estimation risk and controlling the downside risk of portfolio returns. The model seeks to meet the requirements of regulators and fund managers as it represents a balance between short tails and variance. The practical implications of the findings of this study are that the model when applied, will increase the amount of capital for investment, lower transaction cost and minimize risk associated with the deviation around the expected return at the expense of a small additional risk in short tails.

scholarly journals PERHITUNGAN PORTOFOLIO OPTIMAL DENGAN METODE MEAN-SEMIVARIANCE DAN MEAN ABSOLUTE DEVIATION

2021 ◽  
Vol 10 (2) ◽  
pp. 65
Author(s):  
NI KADEK NITA SILVANA SUYASA ◽  
KOMANG DHARMAWAN ◽  
KARTIKA SARI
Keyword(s):  
Optimal Portfolio ◽  
Stock Index ◽  
Mean Absolute Deviation ◽  
Portfolio Risk ◽  
Investment Portfolio ◽  
Expected Return ◽  
Absolute Deviation ◽  
Index Data ◽  
The Mean ◽  
Deviation Method

Knowing and managing investment portfolio risk is the most important factor in growing and preserving capital. The purpose of this study is to determine the optimal portfolio using Mean-Semivariance and Mean Absolute Deviation methods. The Mean-Semivariance method is a method that uses semivariance-semicovariance as a measure of risk while the Mean Absolute Deviation method uses the absolute deviation between realized return and expected return as a measure of risk. This study uses stock index data of LQ45 period February 2017-July 2019. The results of this study are that the Mean Absolute Deviation method gives higher return and risk than the Mean-Semivariance method.

scholarly journals A Scalable Algorithm for Sparse Portfolio Selection

2022 ◽  
Author(s):  
Dimitris Bertsimas ◽  
Ryan Cory-Wright
Keyword(s):  
Portfolio Selection ◽  
Minimum Variance ◽  
Outer Approximation ◽  
Approximation Procedure ◽  
Expected Return ◽  
Risky Assets ◽  
Scalable Algorithm ◽  
Second Order Cone ◽  
Real World Problem ◽  
Sparse Portfolio

The sparse portfolio selection problem is one of the most famous and frequently studied problems in the optimization and financial economics literatures. In a universe of risky assets, the goal is to construct a portfolio with maximal expected return and minimum variance, subject to an upper bound on the number of positions, linear inequalities, and minimum investment constraints. Existing certifiably optimal approaches to this problem have not been shown to converge within a practical amount of time at real-world problem sizes with more than 400 securities. In this paper, we propose a more scalable approach. By imposing a ridge regularization term, we reformulate the problem as a convex binary optimization problem, which is solvable via an efficient outer-approximation procedure. We propose various techniques for improving the performance of the procedure, including a heuristic that supplies high-quality warm-starts, and a second heuristic for generating additional cuts that strengthens the root relaxation. We also study the problem’s continuous relaxation, establish that it is second-order cone representable, and supply a sufficient condition for its tightness. In numerical experiments, we establish that a conjunction of the imposition of ridge regularization and the use of the outer-approximation procedure gives rise to dramatic speedups for sparse portfolio selection problems.

scholarly journals Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan Dengan Keterbatasan Manusia Dalam Memprediksi Masa Depan Dalam Perspektif Al-Qur`an

2012 ◽  
Vol 1 (1) ◽  
pp. 27
Author(s):  
Noor Saif Muhammad Mussafi
Keyword(s):  
Quadratic Programming ◽  
Geometric Mean ◽  
Minimum Variance ◽  
Arithmetic Mean ◽  
Theoretical Frameworks ◽  
Expected Return ◽  
Research And Practice ◽  
Graphic Analysis ◽  
Minimum Variance Portfolio ◽  
Mean Variance

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.

scholarly journals Analisis Portofolio Optimal : Pendekatan Mean Variance Pada Harga Komoditas Pangan di Kota Padang

2020 ◽  
Vol 15 (2) ◽  
pp. 48
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Data Processing ◽  
Expected Profit ◽  
Optimal Portfolio ◽  
Cooking Oil ◽  
The Mean ◽  
Food Commodities ◽  
Asset Selection ◽  
Mean Variance

Investment is a number of commitments or a number of funds or resources made at this time with the aim of obtaining future profits. One method that can be used to form an optimal portfolio is to use the mean variace approach. Asset selection is carried out on food commodities namely rice, eggs, cooking oil, granulated sugar, and red chili. From the data processing it is found that the weight of each commodity is cooking oil (99.95%), eggs (0.03%), granulated sugar (0.04%), red chili is negative (-0.02%), and rice (0.00%). So that it can be estimated that the expected profit is -0.0024% and risk is 0.0001%.

Mean-Variance Analysis

2017 ◽  
Author(s):  
Kerry E. Back
Keyword(s):  
Global Minimum ◽  
Minimum Variance ◽  
Variance Analysis ◽  
Affine Function ◽  
Market Portfolio ◽  
Minimum Variance Portfolio ◽  
The Mean ◽  
Global Minimum Variance Portfolio ◽  
Mean Variance

The mean‐variance frontier is characterized with and without a risk‐free asset. The global minimum variance portfolio and tangency portfolio are defined, and two‐fund spanning is explained. The frontier is characterized in terms of the return defined from the SDF that is in the span of the assets. This is related to the Hansen‐Jagannathan bound. There is an SDF that is an affine function of a return if and only if the return is on the mean‐variance frontier. Separating distributions are defined and shown to imply two‐fund separation and mean‐variance efficiency of the market portfolio.

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