scholarly journals Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Haifeng Guo ◽  
BaiQing Sun ◽  
Hamid Reza Karimi ◽  
Yuanjing Ge ◽  
Weiquan Jin
Keyword(s):  
Portfolio Selection ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Risk Preference ◽  
Expected Returns ◽  
Investment Risk ◽  
Investment Portfolio ◽  
Optimal Resolution ◽  
Portfolio Selection Model ◽  
Mean Variance

This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV) portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.

scholarly journals Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances

2021 ◽  
Author(s):  
Jose Blanchet ◽  
Lin Chen ◽  
Xun Yu Zhou
Keyword(s):  
Portfolio Selection ◽  
Wasserstein Distance ◽  
Empirical Measure ◽  
Back Testing ◽  
Robust Version ◽  
Distributionally Robust ◽  
Mean Variance Portfolio ◽  
Portfolio Selection Model ◽  
Mean Variance ◽  
Wasserstein Distances

We revisit Markowitz’s mean-variance portfolio selection model by considering a distributionally robust version, in which the region of distributional uncertainty is around the empirical measure and the discrepancy between probability measures is dictated by the Wasserstein distance. We reduce this problem into an empirical variance minimization problem with an additional regularization term. Moreover, we extend the recently developed inference methodology to our setting in order to select the size of the distributional uncertainty as well as the associated robust target return rate in a data-driven way. Finally, we report extensive back-testing results on S&P 500 that compare the performance of our model with those of several well-known models including the Fama–French and Black–Litterman models. This paper was accepted by David Simchi-Levi, finance.

scholarly journals A Mean-Variance Portfolio Selection Model with Interval-Valued Possibility Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yunyun Sui ◽  
Jiangshan Hu ◽  
Fang Ma
Keyword(s):  
Portfolio Selection ◽  
Return Rate ◽  
Selection Model ◽  
Expected Return ◽  
Interval Numbers ◽  
Numerical Example ◽  
Mean Variance Portfolio ◽  
Portfolio Selection Model ◽  
Mean Variance ◽  
Interval Valued

In recent years, fuzzy set theory and possibility theory have been widely used to deal with an uncertain decision environment characterized by vagueness and ambiguity in the financial market. Considering that the expected return rate of investors may not be a fixed real number but can be an interval number, this paper establishes an interval-valued possibilistic mean-variance portfolio selection model. In this model, the return rate of assets is regarded as a fuzzy number, and the expected return rate of assets is measured by the interval-valued possibilistic mean of fuzzy numbers. Therefore, the possibilistic portfolio selection model is transformed into an interval-valued optimization model. The optimal solution of the model is obtained by using the order relations of interval numbers. Finally, a numerical example is given. Through the numerical example, it is shown that, when compared with the traditional possibilistic model, the proposed model has more constraints and can better reflect investor psychology. It is an extension of the traditional possibilistic model and offers greater flexibility in reflecting investor expectations.

The Optimization on the Expected Utility Portfolio Selection Model without Short Sales

2011 ◽  
Vol 225-226 ◽  
pp. 1071-1074
Author(s):  
Peng Zhang ◽  
Hui Li Wang
Keyword(s):  
Portfolio Selection ◽  
Expected Utility ◽  
Risk Preference ◽  
Optimal Investment ◽  
Investment Strategy ◽  
Selection Model ◽  
Short Sales ◽  
Expected Return ◽  
Portfolio Selection Model ◽  
Preference Coefficient

A new expected utility (EU) portfolio selection model without short sales is proposed. In the model, the expected utility function is quadratic. The model is solved by the pivoting algorithm. The paper showed in the EU portfolio selection model without the short sales, the relationship between the risk preference coefficient and the expected return is not linear but more complex. The risk preference coefficient could just reflect the investors’ preference in some intervals. We wrote program to calculate the optimal portfolios with the different coefficient. Investors could choose the optimal investment strategy according to both their own risk preference and the expected return of the portfolio.

scholarly journals A New Adaptive Entropy Portfolio Selection Model

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 951
Author(s):  
Ruidi Song ◽  
Yue Chan
Keyword(s):  
Portfolio Selection ◽  
Stock Exchange ◽  
Selection Model ◽  
Portfolio Performance ◽  
Entropy Model ◽  
Data Set ◽  
Entropy Measurement ◽  
Self Adaptation ◽  
Portfolio Selection Model ◽  
Mean Variance

In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz’s mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange 50 (SSE50) index constituent stocks data set. Our outcomes show, compared with the traditional portfolio selection model, that AEM tends to make our investments more decentralized and hence helps to neutralize unsystematic risks. Due to the existence of self-adaptation, AEM turns out to be more adaptable to market fluctuations and helps to maintain the balance between the decentralized and concentrated investments in order to meet investors’ expectations. Our model applies equally well to portfolio optimizations for other financial markets.

scholarly journals An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Chen
Keyword(s):  
Portfolio Selection ◽  
Artificial Bee Colony ◽  
Mean Value ◽  
Investment Risk ◽  
Entropy Model ◽  
Uncertain Variables ◽  
Bee Colony ◽  
Proposed Model ◽  
Security Returns ◽  
Mean Variance

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

SOME FURTHER ANALYTICAL PROPERTIES OF THE CONSTANT CORRELATION MODEL FOR PORTFOLIO SELECTION

2006 ◽  
Vol 09 (07) ◽  
pp. 1071-1091 ◽  
Author(s):  
CLARENCE C. Y. KWAN
Keyword(s):  
Portfolio Selection ◽  
Correlation Matrix ◽  
Correlation Model ◽  
Simple Alternative ◽  
Computational Advantage ◽  
Analytical Properties ◽  
Important Input ◽  
Mean Variance Portfolio ◽  
Portfolio Selection Model ◽  
Mean Variance

The constant correlation model is a mean-variance portfolio selection model where, for a given set of risky securities, the correlation of returns between any pair of different securities is considered to be the same. Support for the model is from previous empirical evidence that sample averages of correlations outperform various more sophisticated models in forecasting the correlation matrix, an important input component for portfolio analysis. To enable a better understanding of the constant correlation model, this study identifies some additional analytical properties of the model and relates them to familiar portfolio concepts. By comparing computational times for portfolio construction with and without simplifying the correlation matrix in a simulation study, this study also confirms the model's computational advantage. This study is intended to provide further analytical support for the model as a viable, simple alternative to those portfolio selection models where input requirements and the attendant computations are more burdensome.

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