scholarly journals Possibilistic Sharpe Ratio Based Novice Portfolio Selection Models

2013 ◽  
Author(s):  
Rupak Bhattacharyya
Keyword(s):  
Portfolio Selection ◽  
Sharpe Ratio ◽  
Selection Models

scholarly journals Risk Return Relationship in the Portfolio Selection Models

2018 ◽  
Vol 08 (03) ◽  
pp. 358-366
Author(s):  
Ken Hung ◽  
C. W. Yang ◽  
Yifan Zhao ◽  
Kuo-Hao Lee
Keyword(s):  
Portfolio Selection ◽  
Selection Models ◽  
Risk Return

Petroleum Exploration Risk in Prospect Portfolio Selection

2017 ◽  
pp. 40-65
Keyword(s):  
Standard Deviation ◽  
Portfolio Selection ◽  
Six Sigma ◽  
Efficient Frontier ◽  
Sharpe Ratio ◽  
Petroleum Exploration ◽  
Management Approach ◽  
Portfolio Optimisation ◽  
Exploration Risk ◽  
Return Variance

Presented method is applied to petroleum exploration for prospect portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to risk management. Optimisation resolves Efficient Frontier of portfolios for desired range of expected return with initially defined increment. Simulation measures Efficient Frontier portfolios calculating mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target limits. Analysis considers mean return, Six Sigma metrics and Sharpe Ratio and selects the portfolio with maximal Sharpe Ratio as initially the best portfolio. Optimisation resolves Efficient Frontier in a narrow interval with smaller increments. Simulation measures Efficient Frontier performance including mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target. Analysis identifies the maximal Sharpe Ratio portfolio, i.e. the best portfolio for implementation. Selected prospects in the portfolio are individual projects. So, Project Management approach is used for control.

Joint tails impact in stochastic volatility portfolio selection models

2020 ◽  
Vol 292 (2) ◽  
pp. 833-848
Author(s):  
Marco Bonomelli ◽  
Rosella Giacometti ◽  
Sergio Ortobelli Lozza
Keyword(s):  
Stochastic Volatility ◽  
Portfolio Selection ◽  
Selection Models

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.

Problems in the application of portfolio selection models

Omega ◽  
1976 ◽  
Vol 4 (6) ◽  
pp. 699-709 ◽  
Author(s):  
Stewart D Hodges
Keyword(s):  
Portfolio Selection ◽  
Selection Models

scholarly journals A Parametric Sharpe Ratio Optimization Approach for Fuzzy Portfolio Selection Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Ying Liu ◽  
Ya-Nan Li
Keyword(s):  
Portfolio Selection ◽  
Domain Decomposition Method ◽  
Equivalent Form ◽  
Performance Criterion ◽  
Performance Measure ◽  
Sharpe Ratio ◽  
Selection Problem ◽  
Optimization Approach ◽  
Fuzzy Variables ◽  
Portfolio Selection Problem

When facing to make a portfolio decision, investors may care more about every portfolio’s performance on a return and risk trade-off. In this paper, a new low partial moment measurement that only punishes the loss risk is defined for selection variables based on L-S integral. Furthermore, a new performance measure for portfolio evaluation is proposed to generalize the Sharpe ratio in the fuzzy context. With the optimal performance criterion, a new parametric Sharpe ratio portfolio optimization model is developed wherein uncertain returns are presented as parametric interval-valued fuzzy variables. To make the proposed model easy to solve, we transform the fractional programming into an equivalent form and solve it with domain decomposition method (DDM). Finally, we apply the proposed performance measure into a portfolio selection problem, compare the computational results in different cases, and analyze the influence of different parameters on the optimal portfolio.

scholarly journals Maximization of utility and portfolio selection models

2017 ◽  
Vol 0 (11) ◽  
Author(s):  
João Francisco Neves ◽  
Patrícia Nunes da Silva ◽  
Carlos Frederico Fragoso de Barros e Vasconcellos
Keyword(s):  
Portfolio Selection ◽  
Selection Models

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.

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