scholarly journals Optimization of the Mean-Variance Model Investment Portfolio in Five Mining Stocks Traded on the IDX

2021 ◽  
Vol 2 (2) ◽  
pp. 64-70
Author(s):  
Guskenoly Fauziah
Keyword(s):  
Industrial Development ◽  
Raw Materials ◽  
Energy Resource ◽  
Optimization Method ◽  
Capital Allocation ◽  
Investment Portfolio ◽  
Variance Model ◽  
The Mean ◽  
Investment Portfolios ◽  
Mean Variance

The Mining and Energy sector is a major foreign exchange earner, provides the largest energy resource, and as an absorber of labor. In addition, most of the energy resources used in the Indonesian economy come from mining. namely oil and coal. Investment for mining and energy exploration in Indonesia needs to be a priority and continue to be encouraged to maintain the level of reserves as raw materials for future industrial development, including downstream. This study aims to measure the performance of investment portfolios in several stocks in the Mining and Energy sectors. The portfolio optimization method is carried out using the Mean-Variance model (Markowitz model). Based on the results of the analysis, it is obtained that the combination and proportion of capital allocation on several stocks in the formation of an investment portfolio that has better performance, where the optimum portfolio composition obtained a portfolio return of 0.000866205 with a portfolio variance of 0.000261104. In addition, the results of the analysis can be concluded that the return ratio can affect the model.

Optimal selection of financial risk investment portfolio based on random matrix method

2020 ◽  
Vol 20 (3) ◽  
pp. 859-868
Author(s):  
Jie Tian ◽  
Kun Zhao
Keyword(s):  
Covariance Matrix ◽  
Random Matrix ◽  
Financial Risk ◽  
Good Effect ◽  
Investment Portfolio ◽  
Variance Model ◽  
Minimum Risk ◽  
The Mean ◽  
Risk Investment ◽  
Mean Variance

The optimization of investment portfolio is the key to financial risk investment. In this study, the investment portfolio was optimized by removing the noise of covariance matrix in the mean-variance model. Firstly, the mean-variance model and noise in covariance matrix were briefly introduced. Then, the correlation matrix was denoised by KR method (Sharifi S, Grane M, Shamaie A) from random matrix theory (RMT). Then, an example was given to analyze the application of the method in financial stock investment portfolio. It was found that the stability of the matrix was improved and the minimum risk was reduced after denoising. The study of minimum risk under different M values and stock number suggested that calculating the optimal value of M and stock number based on RMT could achieve optimal financial risk investment portfolio result. It shows that RMT has a good effect on portfolio optimization and is worth promoting widely.

scholarly journals Multi-time state mean-variance model in continuous time

2021 ◽  
Vol 27 ◽  
pp. 92
Author(s):  
Shuzhen Yang
Keyword(s):  
Boundary Conditions ◽  
Continuous Time ◽  
Optimal Strategy ◽  
Investment Portfolio ◽  
Variance Model ◽  
Investment Plan ◽  
Terminal Time ◽  
Variance Risk ◽  
Maximum Drawdown ◽  
Mean Variance

The objective of the continuous time mean-variance model is to minimize the variance (risk) of an investment portfolio with a given mean at the terminal time. However, the investor can stop the investment plan at any time before the terminal time. To solve this problem, we consider to minimize the variances of the investment portfolio in the multi-time state. The advantage of this multi-time state mean-variance model is the minimization of the risk of the investment portfolio within the investment period. To obtain the optimal strategy of the model, we introduce a sequence of Riccati equations, which are connected by jump boundary conditions. In addition, we establish the relationships between the means and variances in the multi-time state mean-variance model. Furthermore, we use an example to verify that the variances of the multi-time state can affect the average of Maximum-Drawdown of the investment portfolio.

scholarly journals Abnormal Portfolio Asset Allocation Model: Review

2020 ◽  
Vol 1 (1) ◽  
pp. 46-54
Author(s):  
Nurfadhlina Bt Abdul Halima ◽  
Dwi Susanti ◽  
Alit Kartiwa ◽  
Endang Soeryana Hasbullah
Keyword(s):  
Asset Allocation ◽  
Asset Returns ◽  
Higher Moments ◽  
Investment Portfolio ◽  
Allocation Model ◽  
Model Review ◽  
The Mean ◽  
Mean Variance ◽  
Skewness And Kurtosis ◽  
Normally Distributed

It has been widely studied how investors will allocate their assets to an investment when the return of assets is normally distributed. In this context usually, the problem of portfolio optimization is analyzed using mean-variance. When asset returns are not normally distributed, the mean-variance analysis may not be appropriate for selecting the optimum portfolio. This paper will examine the consequences of abnormalities in the process of allocating investment portfolio assets. Here will be shown how to adjust the mean-variance standard as a basic framework for asset allocation in cases where asset returns are not normally distributed. We will also discuss the application of the optimum strategies for this problem. Based on the results of literature studies, it can be concluded that the expected utility approximation involves averages, variances, skewness, and kurtosis, and can be extended to even higher moments.

PORTFOLIO MODEL UNDER FRACTAL MARKET BASED ON MEAN-DCCA

Fractals ◽  
2020 ◽  
Vol 28 (07) ◽  
pp. 2050142
Author(s):  
WEIDE CHUN ◽  
HESEN LI ◽  
XU WU
Keyword(s):  
Analytical Solution ◽  
Correlation Analysis ◽  
Capital Market ◽  
Cross Correlation ◽  
Investment Portfolio ◽  
Cross Correlation Analysis ◽  
Risk Criterion ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance

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

Why Do We Reject the Mean-Variance Model?

1995 ◽  
Vol 97 (1) ◽  
pp. 137 ◽  
Author(s):  
W. Jos Jansen
Keyword(s):  
Variance Model ◽  
The Mean ◽  
Mean Variance

Multivariate Tests for Stochastic Dominance Efficiency of a Given Portfolio

2007 ◽  
Vol 42 (2) ◽  
pp. 489-515 ◽  
Author(s):  
Thierry Post ◽  
Philippe Versijp
Keyword(s):  
Multivariate Statistics ◽  
Stochastic Dominance ◽  
Statistical Power ◽  
Investment Portfolio ◽  
Tail Risk ◽  
Multivariate Tests ◽  
The Mean ◽  
Empirical Tests ◽  
Mean Variance ◽  
Power Properties

AbstractWe develop empirical tests for stochastic dominance efficiency of a given investment portfolio relative to all possible portfolios formed from a given set of assets. Our tests use multivariate statistics, which result in superior statistical power properties compared to existing stochastic dominance efficiency tests and increase the comparability with existing mean-variance efficiency tests. Using our tests, we demonstrate that the mean-variance inefficiency of the CRSP all-share index relative to beta-sorted portfolios can be explained by tail risk not captured by variance.

scholarly journals MEAN-VARIANCE VERSUS MEAN–EXPECTED SHORTFALL MODELS: AN APPLICATION TO WHEAT VARIETY SELECTION

2016 ◽  
Vol 48 (2) ◽  
pp. 148-172 ◽  
Author(s):  
KUNLAPATH SUKCHAROEN ◽  
DAVID LEATHAM
Keyword(s):  
Risk Measure ◽  
Management Strategies ◽  
Wheat Variety ◽  
Expected Shortfall ◽  
Wheat Varieties ◽  
Variance Model ◽  
Variety Selection ◽  
Risk Management Strategies ◽  
The Mean ◽  
Mean Variance

AbstractOne of the most popular risk management strategies for wheat producers is varietal diversification. Previous studies proposed a mean-variance model as a tool to optimally select wheat varieties. However, this study suggests that the mean–expected shortfall (ES) model (which is based on a downside risk measure) may be a better tool because variance is not a correct risk measure when the distribution of wheat variety yields is multivariate nonnormal. Results based on data from Texas Blacklands confirm our conjecture that the mean-ES framework performs better in term of selecting wheat varieties than the mean-variance method.

scholarly journals Sourcing decision under interconnected risks: an application of mean–variance preferences approach

2022 ◽  
Author(s):  
Soumyatanu Mukherjee ◽  
Sidhartha S. Padhi
Keyword(s):  
Supply Chain ◽  
Decision Maker ◽  
Raw Materials ◽  
Drug Manufacturer ◽  
Perfect Competition ◽  
Supply Side ◽  
Demand Side ◽  
The Mean ◽  
Supply Chain Risks ◽  
Mean Variance

AbstractSupply chains are customarily associated with multiple interconnected risks originated from supply side, demand side, or from the unanticipated background uncertainties faced by a firm. Also, effective functioning of supply chain hinges on sourcing decisions of inputs (raw materials). Therefore, there is a striking need to analyse the risk preference of the decision maker while going for optimal sourcing decision under varying degree of interconnected supply chain risks. This study addresses this issue by analysing the comparative static effects under interconnected supply chain risks for a risk averse decision-maker, manufacturing and selling products in a regulated market under perfect competition. The decision-maker faces not only supply-side risk (due to random input material prices) but also interconnected risks arising out of background risk (setup costs risk) and demand-side risk (output prices risk). With preferences defined over the mean and standard deviation of the uncertain final profit, this study illustrates the effects of the changes in the pairwise correlations between the three above mentioned risks on the optimum input choice of the manufacturer. To contextualise this study, an India-based generic drug manufacturer cum seller has been considered as a case in the parametric example of our model. Adaptation of the mean–variance framework helps obtaining all the results in terms of the relative trade-off between risk and return, with simple yet intuitive interpretations.

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