Equivalence of linear deviation about the mean and mean absolute deviation about the mean objective functions

1999 ◽  
Vol 24 (4) ◽  
pp. 181-185 ◽  
Author(s):  
C.M Kenyon ◽  
S Savage ◽  
B Ball
Keyword(s):  
Mean Absolute Deviation ◽  
Objective Functions ◽  
Absolute Deviation ◽  
The Mean

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

Cleaning of Matched License Plate Data

2002 ◽  
Vol 1804 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Stephen D. Clark ◽  
S. Grant-Muller ◽  
Haibo Chen
Keyword(s):  
Standard Deviation ◽  
Road Traffic ◽  
Statistical Test ◽  
License Plate ◽  
Mean Absolute Deviation ◽  
Test Results ◽  
Absolute Deviation ◽  
Traffic Conditions ◽  
The Third ◽  
The Mean

Three methods for identifying outlying journey time observations collected as part of a motorway license plate matching exercise are presented. Each method is examined to ensure that it is comprehensible to transport practitioners, is able to correctly classify outliers, and is efficient in its application. The first method is a crude method based on percentiles. The second uses a mean absolute deviation test. The third method is a modification of a traditional z- or t-statistical test. Results from each method and combinations of methods are compared. The preferred method is judged to be the third method alone, which uses the median rather than the mean as its measure of location and the inter-quartile range rather than the standard deviation as its measure of variability. This method is seen to be robust to both the outliers themselves and the presence of incident conditions. The effectiveness of the method is demonstrated under a number of typical and atypical road traffic conditions. In particular, the method is applied to a different section of motorway and is shown to still produce useful results.

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 Predicting Movement of Stock of Apple Inc. using Sutte Indicator

2017 ◽  
Author(s):  
Ansari Saleh Ahmar
Keyword(s):  
Decision Making ◽  
Moving Average ◽  
Investment Decision ◽  
Technical Analysis ◽  
Percentage Error ◽  
Decision Making Process ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Investment Decision Making ◽  
The Mean

The purpose of this study is to apply technical analysis e.g. Sutte Indicator in Stock Market that will assist in the investment decision-making process to buy or sell of stocks. This study took data from Apple Inc. which listed in the NasdaqGS in the period of 1 January 2008 to 26 September 2016. Performance of the Sutte Indicator can be seen with comparison with other technical analysis e.g. Simple Moving Average (SMA) and Moving Average Convergence/Divergence (MACD). Comparison of the reliability of prediction from Sutte Indicator, SMA, and MACD using the mean of square error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE).

scholarly journals Properties of the Standard Deviation that are Rarely Mentioned in Classrooms

2016 ◽  
Vol 38 (3) ◽  
Author(s):  
Mohammad Fraiwan Al-Saleh ◽  
Adil Eltayeb Yousif
Keyword(s):  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Sample Variance ◽  
Mean Absolute Deviation ◽  
Hidden Information ◽  
Absolute Deviation ◽  
Sample Mean ◽  
The Mean ◽  
Vague Concept ◽  
Chebyshev's Inequality

Unlike the mean, the standard deviation ¾ is a vague concept. In this paper, several properties of ¾ are highlighted. These properties include the minimum and the maximum of ¾, its relationship to the mean absolute deviation and the range of the data, its role in Chebyshev’s inequality and the coefficient of variation. The hidden information in the formula itself is extracted. The confusion about the denominator of the sample variance being n ¡ 1 is also addressed. Some properties of the sample mean and varianceof normal data are carefully explained. Pointing out these and other properties in classrooms may have significant effects on the understanding and the retention of the concept.

scholarly journals PROPOSTA DI UNA FUNZIONE UTILITÀ DIPENDENTE DALL’UTILITÀ ATTESA E DALLA DISPERSIONE DELLE UTILITÀ

2014 ◽  
Author(s):  
Vittorio B. Frosini
Keyword(s):  
Standard Deviation ◽  
Expected Utility ◽  
Dispersion Measure ◽  
Risk Averse ◽  
Mean Absolute Deviation ◽  
Summary Statistic ◽  
Absolute Deviation ◽  
The Mean ◽  
Condition C ◽  
Substitution Axiom

The author develops the properties and implications of a proposal, concerning a summary statistic of the random prospect of utilities. Following a suggestion of Maurice Allais, such a statistic is increasing with expected utility, and decreasing – for most people, who are risk averse – with the mean absolute deviation of utilities; a parameter multiplying this dispersion measure allows for risk averse or risk prone behaviour, according to its sign, and also for more or less departure from a certain prospect. It is demonstrated that this statistic (a) satisfies the first stochastic dominance, (b) satisfies the independence condition, (c) satisfies the so called “problem of probabilistic insurance”, (d) resolves the paradoxes of Allais, Ellsberg and Kahneman-Tversky (paradox of the substitution axiom), (e) the mean absolute deviation from the mean cannot be replaced by the standard deviation.

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