scholarly journals Fuzzy Portfolio Optimization of Power Generation Assets

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3043
Author(s):  
Barbara Glensk ◽  
Reinhard Madlener
Keyword(s):  
Power Generation ◽  
Decision Making ◽  
Portfolio Selection ◽  
Probabilistic Approach ◽  
Mean Absolute Deviation ◽  
Membership Functions ◽  
Absolute Deviation ◽  
Selection Models ◽  
Fuzzy Portfolio Selection ◽  
Aspiration Levels

Fuzzy theory is proposed as an alternative to the probabilistic approach for assessing portfolios of power plants, in order to capture the complex reality of decision-making processes. This paper presents different fuzzy portfolio selection models, where the rate of returns as well as the investor’s aspiration levels of portfolio return and risk are regarded as fuzzy variables. Furthermore, portfolio risk is defined as a downside risk, which is why a semi-mean-absolute deviation portfolio selection model is introduced. Finally, as an illustration, the models presented are applied to a selection of power generation mixes. The efficient portfolio results show that the fuzzy portfolio selection models with different definitions of membership functions as well as the semi-mean-absolute deviation model perform better than the standard mean-variance approach. Moreover, introducing membership functions for the description of investors’ aspiration levels for the expected return and risk shows how the knowledge of experts, and investors’ subjective opinions, can be better integrated in the decision-making process than with probabilistic approaches.

scholarly journals PORTFOLIO SELECTION MODELS: COMPARATIVE ANALYSIS AND APPLICATIONS TO THE BRAZILIAN STOCK MARKET.

2015 ◽  
Vol 4 (3) ◽  
Author(s):  
Christiano Alves Farias ◽  
Wilson da Cruz Vieira ◽  
Maurinho Luiz Dos Santos
Keyword(s):  
Comparative Analysis ◽  
Stock Market ◽  
Portfolio Selection ◽  
Mean Absolute Deviation ◽  
Optimal Portfolios ◽  
Absolute Deviation ◽  
Selection Models ◽  
Time Periods ◽  
Mean Variance

This paper presents a comparison of three portfolio selection models,Mean-Variance (MV), Mean Absolute Deviation (MAD), and Minimax, as applied tothe Brazilian Stock Market (BOVESPA). For this comparison, we used BOVESPAdata from three different 12 month time periods: 1999 to 2000, 2001, and 2002 to 2003.Each model generated three optimal portfolios for each period, with performancedetermined by monthly returns over the period. In general, the accumulated returnsfrom the Minimax modeled portfolios were superior to the BOVESPA’s principalindex, the IBOVESPA. The MV model was the least efficient for portfolio selection.

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).

A novel probabilistic hesitant fuzzy portfolio selection model with value-at-risk and safety level of score

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue Deng ◽  
Weimin Li
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Selection Model ◽  
Selection Models ◽  
Content Type ◽  
Fuzzy Portfolio Selection ◽  
Fuzzy Environment ◽  
Portfolio Selection Model

Purpose This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment. Design/methodology/approach It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models. Findings The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns. Originality/value This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.

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