scholarly journals Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

2019 ◽  
Vol 8 (3) ◽  
pp. 7818-7822
Keyword(s):  
Portfolio Optimization ◽  
Covariance Matrix ◽  
Stock Exchange ◽  
Optimal Portfolio ◽  
Risk Level ◽  
Mean Absolute Deviation ◽  
Investment Portfolio ◽  
Absolute Deviation ◽  
Singular Covariance Matrix ◽  
The Mean

Investing in the stock sector, investors often face risk problems. Usually, forming an investment portfolio is done to minimize risk. In this research, investment portfolio optimization is discussed. The data analyzed are 8 shares traded on the capital market in Indonesia through the Indonesia Stock Exchange (IDX). Optimization is performed using the Mean-Absolute Deviation model with the singular covariance matrix to determine the optimal weights. The results of portfolio optimization Mean-Absolute Deviation model with singular covariance matrix method, was obtained optimal portfolio weights that is of 17.22% for BBCA shares; 26.64% for TKIM shares; 9.96% for BBRI shares; 9.96% for BBNI shares; 8.70% for BMRI shares; 3.75% for ADRO shares; 6.52% for GGRM shares; and 17.25% for UNTR shares. Where the optimal portfolio composition is obtained the expected rate of return (expected return) of 0.18% with a portfolio risk level (standard deviation) of 0.07%.

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 Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1266
Author(s):  
Weng Siew Lam ◽  
Weng Hoe Lam ◽  
Saiful Hafizah Jaaman
Keyword(s):  
Portfolio Optimization ◽  
Performance Ratio ◽  
Programming Approach ◽  
Mean Absolute Deviation ◽  
Diversification Strategy ◽  
Absolute Deviation ◽  
Multi Objective ◽  
Proposed Model ◽  
The Mean ◽  
Naïve Diversification

Investors wish to obtain the best trade-off between the return and risk. In portfolio optimization, the mean-absolute deviation model has been used to achieve the target rate of return and minimize the risk. However, the maximization of entropy is not considered in the mean-absolute deviation model according to past studies. In fact, higher entropy values give higher portfolio diversifications, which can reduce portfolio risk. Therefore, this paper aims to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by incorporating the maximization of entropy. In addition, the proposed model incorporates the optimal value of each objective function using a goal-programming approach. The objective functions of the proposed model are to maximize the mean return, minimize the absolute deviation and maximize the entropy of the portfolio. The proposed model is illustrated using returns of stocks of the Dow Jones Industrial Average that are listed in the New York Stock Exchange. This study will be of significant impact to investors because the results show that the proposed model outperforms the mean-absolute deviation model and the naive diversification strategy by giving higher a performance ratio. Furthermore, the proposed model generates higher portfolio mean returns than the MAD model and the naive diversification strategy. Investors will be able to generate a well-diversified portfolio in order to minimize unsystematic risk with the proposed model.

scholarly journals Kajian Adaptive Neuro-Fuzzy Inference System (ANFIS) Dalam Memprediksi Penerimaan Mahasiswa Baru Pada Universitas Buana Perjuangan Karawang

2021 ◽  
Vol 6 (1) ◽  
pp. 44-54
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

Abstract - The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang.   Keywords: ANFIS, Backpropagation, Hybrid, Prediction

A mean-absolute deviation-skewness portfolio optimization model

1993 ◽  
Vol 45 (1) ◽  
pp. 205-220 ◽  
Author(s):  
Hiroshi Konno ◽  
Hiroshi Shirakawa ◽  
Hiroaki Yamazaki
Keyword(s):  
Portfolio Optimization ◽  
Optimization Model ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Portfolio Optimization Model

Portfolio optimization using the generalized reduced gradient nonlinear algorithm

2016 ◽  
Vol 9 (4) ◽  
pp. 570-582 ◽  
Author(s):  
Dima Waleed Hanna Alrabadi
Keyword(s):  
Portfolio Optimization ◽  
Stock Exchange ◽  
Arab World ◽  
Content Type ◽  
Optimization Framework ◽  
Monthly Return ◽  
Reduced Gradient ◽  
The Mean ◽  
Generalized Reduced Gradient ◽  
Mean Variance

Purpose This study aims to utilize the mean–variance optimization framework of Markowitz (1952) and the generalized reduced gradient (GRG) nonlinear algorithm to find the optimal portfolio that maximizes return while keeping risk at minimum. Design/methodology/approach This study applies the portfolio optimization concept of Markowitz (1952) and the GRG nonlinear algorithm to a portfolio consisting of the 30 leading stocks from the three different sectors in Amman Stock Exchange over the period from 2009 to 2013. Findings The selected portfolios achieve a monthly return of 5 per cent whilst keeping risk at minimum. However, if the short-selling constraint is relaxed, the monthly return will be 9 per cent. Moreover, the GRG nonlinear algorithm enables to construct a portfolio with a Sharpe ratio of 7.4. Practical implications The results of this study are vital to both academics and practitioners, specifically the Arab and Jordanian investors. Originality/value To the best of the author’s knowledge, this is the first study in Jordan and in the Arab world that constructs optimum portfolios based on the mean–variance optimization framework of Markowitz (1952) and the GRG nonlinear algorithm.

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.

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