Mean Target Semi-Absolute Deviation Model for Portfolio Selection with Uncertain Returns

2014 ◽  
Vol 1079-1080 ◽  
pp. 707-710
Author(s):  
Ming Qiang Yin ◽  
Wei Yi Qian
Keyword(s):  
Portfolio Selection ◽  
Historical Data ◽  
Search Algorithm ◽  
Risk Measure ◽  
Selection Problem ◽  
Absolute Deviation ◽  
Portfolio Selection Problem ◽  
Numerical Example ◽  
Proposed Model ◽  
Security Returns

This paper discusses theuncertain portfolio selection problem when security returns are hard to be wellreflected by historical data. The security returns are regarded as uncertainvariables. A target semi-absolute deviation risk measure is introduced. Basedon the concept of target semi-absolute deviation, a mean target semi-absolutedeviation model is proposed. In addition, thegravitation search algorithm is introduced to solvethe proposed model. Finally, a numerical example is given to illustratethe application of the proposed model.

scholarly journals Two-Stage Fuzzy Portfolio Selection Problem with Transaction Costs

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yanju Chen ◽  
Ye Wang
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Optimal Solution ◽  
Single Stage ◽  
Selection Problem ◽  
Stage Model ◽  
Two Stage ◽  
Portfolio Selection Problem ◽  
Fuzzy Portfolio Selection ◽  
Proposed Model

This paper studies a two-period portfolio selection problem. The problem is formulated as a two-stage fuzzy portfolio selection model with transaction costs, in which the future returns of risky security are characterized by possibility distributions. The objective of the proposed model is to achieve the maximum utility in terms of the expected value and variance of the final wealth. Given the first-stage decision vector and a realization of fuzzy return, the optimal value expression of the second-stage programming problem is derived. As a result, the proposed two-stage model is equivalent to a single-stage model, and the analytical optimal solution of the two-stage model is obtained, which helps us to discuss the properties of the optimal solution. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness. The computational results provided by the proposed model show that the more risk-averse investor will invest more wealth in the risk-free security. They also show that the optimal invested amount in risky security increases as the risk-free return decreases and the optimal utility increases as the risk-free return increases, whereas the optimal utility increases as the transaction costs decrease. In most instances the utilities provided by the proposed two-stage model are larger than those provided by the single-stage model.

scholarly journals Estimating Market Expectations for Portfolio Selection Using Penalized Statistical Models

2020 ◽  
Vol 38 (2) ◽  
pp. 133-146
Author(s):  
Carlos Felipe Valencia-Arboleda ◽  
Diego Hernan Segura-Acosta
Keyword(s):  
Portfolio Selection ◽  
Historical Data ◽  
Selection Problem ◽  
Expected Returns ◽  
Portfolio Performance ◽  
Market Prices ◽  
Portfolio Selection Problem ◽  
Implicit Information ◽  
Decision Variables ◽  
Out Of Sample

The portfolio selection problem can be viewed as an optimization problem that maximizes the risk–return relationship. It consists of a number of elements, such as an objective function, decision variables and input parameters, which are used to predict expected returns and the covariance between the said returns. However, the real values of these parameters cannot be directly observed; thus, estimations based on historical data are required. Historical data, however, can often result in modelling errors when the parameters are replaced by their estimations. We propose to address this by using some regularization mechanisms in the optimization.  In addition, we explore the use of implicit information to improve the portfolio performance, such as options market prices, which are a rich source of investor expectations. Accordingly, we propose a new estimator for risk and return that combines historical and implicit information in the portfolio selection problem. We implement the new estimators for the mean-VAR and mean-VaR2 problems using an elastic-net model that reduces the risk of all estimations performed. The results suggest that the model has a good out-of-sample performance that is superior to models with pure historical estimations.

scholarly journals Multi-stage stochastic model in portfolio selection problem

Filomat ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 991-1001
Author(s):  
Shokoofeh Banihashemi ◽  
Ali Azarpour ◽  
Marziye Kaveh
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Problem ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Portfolio Selection Problem ◽  
Multi Stage

This paper is a novel work of portfolio-selection problem solving using multi objective model considering four parameters, Expected return, downside beta coefficient, semivariance and conditional value at risk at a specified confidence level. Multi-period models can be defined as stochastic models. Early studies on portfolio selection developed using variance as a risk measure; although, theories and practices revealed that variance, considering its downsides, is not a desirable risk measure. To increase accuracy and overcoming negative aspects of variance, downside risk measures like semivarinace, downside beta covariance, value at risk and conditional value at risk was other risk measures that replaced in models. These risk measures all have advantages over variance and previous works using these parameters have shown improvements in the best portfolio selection. Purposed models are solved using genetic algorithm and for the topic completion, numerical example and plots to measure the performance of model in four dimensions are provided.

scholarly journals Uncertain portfolio optimization problem with liquidity and diversification

2020 ◽  
Author(s):  
Ranran Zhang ◽  
Bo Li
Keyword(s):  
Portfolio Selection ◽  
Selection Problem ◽  
Investment Risk ◽  
Deterministic Models ◽  
Entropy Model ◽  
Portfolio Selection Problem ◽  
Uncertain Variables ◽  
Proposed Model ◽  
Portfolio Optimization Problem ◽  
Mean Variance

This paper deals with a portfolio selection problem with uncertain returns. Here, the returns of the assets are regarded as uncertain variables which are estimated by experienced experts. First, an uncertain mean-variance-entropy model for portfolio selection problem is presented by taking into account four criteria viz., return, risk, liquidity and diversification degree of portfolio. In the proposed model, the investment return is quantified by uncertain expected value, the investment risk is characterized by uncertain variance and entropy is used to measure the diversification degree of portfolio. Moreover, different from the previous bi-objective optimization model, our model achieves both the maximum return and the minimum risk in a single objective form by introducing a risk aversion factor and the dimensional influence caused by different units is eliminated by normalization. Then, two auxiliary portfolio selection models are transformed into different equivalent deterministic models. Finally, a numerical simulation is given to verify the practicability of our model.

A scenario-based mathematical approach to a robust project portfolio selection problem under fuzzy uncertainty

2021 ◽  
pp. 1-14
Author(s):  
Saeed Karimi ◽  
Saeed Mirzamohammadi ◽  
MirSaman Pishvaee
Keyword(s):  
Portfolio Selection ◽  
Confidence Level ◽  
Optimization Model ◽  
Selection Problem ◽  
Programming Approach ◽  
Project Portfolio ◽  
Portfolio Selection Problem ◽  
Project Portfolio Selection ◽  
Proposed Model ◽  
Uncertainty Of Parameters

As a major concern of chief managers in each organization, project portfolio selection has a special place in their responsibilities. To assist managers in making decisions, applicable optimization models play an essential role in such processes. In this regard, this paper provides a stochastic optimization model for a project portfolio selection problem under different scenarios. Providing the novelty in the model along with making it closer to reality, the interdependency between revenue and cost of projects is considered. Due to the inherent uncertainty of parameters, the revenue and cost of each project, as well as contributed capital, follow triangular fuzzy parameters. Contrary to the previous model, the appreciation of assets is considered in the proposed model as the other novelty of the proposed model. To tackle the uncertainty of parameters, a robust possibilistic approach is used, which has been first-ever devised in such problems. Being both optimistic and pessimistic approaches available for decision-makers, a new measure is introduced to make the model inclusive. Moreover, by considering the confidence level as both parameter and decision variables, the robust possibilistic programming approach is adopted to solve the proposed model. Using the new proposed measure, the optimal average value of robust model are obtained under different confidence level. Finally, solving the optimization model, the results are provided by implementing the realization for uncertain parameters, and regarding the obtained results, discussions are made to provide some insights to the managers.

A DOWNSIDE RISK APPROACH FOR THE PORTFOLIO SELECTION PROBLEM WITH FUZZY RETURNS

2004 ◽  
Vol 09 (01) ◽  
Author(s):  
Teresa León ◽  
Vicente Liern ◽  
Paulina Marco ◽  
Enriqueta Vercher ◽  
José Vicente Segura
Keyword(s):  
Portfolio Selection ◽  
Downside Risk ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Risk Approach
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