Fuzzy Portfolio Research Articles

Multi-period fuzzy portfolio selection model with preference-regret criterion

Multi-period fuzzy portfolio selection model with preference-regret criterion

Fuzzy Portfolio with a Novel Power Membership Function Based on GARCH and Black–Litterman Model

Fuzzy Portfolio with a Novel Power Membership Function Based on GARCH and Black–Litterman Model

A Fuzzy Portfolio Model With Cardinality Constraints Based on Differential Evolution Algorithms

Uncertain information in the securities market exhibits fuzziness. In this article, expected returns and liquidity are considered as trapezoidal fuzzy numbers. The possibility mean and mean absolute deviation of expected returns represent the returns and risks of securities assets, while the possibility mean of expected turnover represents the liquidity of securities assets. Taking into account practical constraints such as cardinality and transaction costs, this article establishes a fuzzy portfolio model with cardinality constraints and solves it using the differential evolution algorithm. Finally, using fuzzy c-means clustering algorithm, 12 stocks are selected as empirical samples to provide numerical calculation examples. At the same time, fuzzy c-means clustering algorithm is used to cluster the stock yield data and analyse the stock data comprehensively and accurately, which provides a reference for establishing an effective portfolio.

An Interval-Valued Fuzzy Portfolio Decision Model with Entropy and VaR Constraints

An Interval-Valued Fuzzy Portfolio Decision Model with Entropy and VaR Constraints

Fuzzy Portfolio Selection Using Stochastic Correlation.

The online version contains supplementary material available at 10.1007/s10614-023-10371-w.

Fuzzy Portfolio Selection in the Risk Attitudes of Dimension Analysis under the Adjustable Security Proportions

Fuzzy portfolio models have received many researchers’ focus on the issue of risk preferences. The portfolio based on guaranteed return rates has been developing and considering the dimension of excess investment for the investors in different risk preferences. However, not only excess investment but also shortage investment to the selected portfolio should be considered for risk preferences, including risk-seeking, risk-neutral, and risk-averse, by different degrees of dimensions in excess investment and shortage investment. A comparison to the degree of dimensions for the excess investment and shortage investment indicates that a risk-seeker would like to have excess investment for securities whose return rates are bigger than the guaranteed return rates and shortage investment for securities whose return rates are smaller than the guaranteed return rates. Finally, we present three experiments to illustrate the proposed model. The results show that the different risk preferences derive different fuzzy portfolio selections under s and t dimensions, where a lower value of s is suggested for a risk-seeker as t > s, and we suggest the values of s and t to be smaller than or equal to 3. By contrast, for the risk-neutral investor, we suggest s = t; t < s is suggested to the investor who is risk-averse.

Multi-criteria fuzzy portfolio selection based on three-way decisions and cumulative prospect theory

Portfolio selection is one of the hottest issue in decision-making and management engineering. But due to the capital market natural complexity and investors’ irrational behaviors, it is not easy for investors to achieve their predefined goals. In this study, we propose a novel three-way decisions model based on cumulative prospect theory and outranking relations. Compared with traditional two-way decisions, the introduction of a boundary region into the three-way decisions theory makes it possible to reduce decision risk. By constructing an outranked set for each alternative and a hybrid multi-criteria decision-making matrix, three strategies are proposed by us to design the three-way decisions model. In order to test the effectiveness of the proposed model, we introduce it into a fuzzy multi-period portfolio selection case and design an improved particle swarm optimization as the solution algorithm. Finally, the effectiveness of the algorithm is validated by some test functions. And an experiment based on real market data validates the proposed multi-period portfolio selection model outperforms other compared models in terms of return, risk and risk-adjusted criteria.

Dimensions Analysis to Excess Investment in Fuzzy Portfolio Model from the Threshold of Guaranteed Return Rates

Portfolio selection is a major topic for investors to allocate their assets and maximize their profit under constrained risk. For uncertain investment behavior in a vagueness environment, some researchers have devoted themselves to this field of fuzzy portfolio models for portfolio selection. Especially, Tsaur, Chiu and Huang in 2021 defined guaranteed return rates to excess investment for securities whose return rates are bigger than the guaranteed return rates in the fuzzy portfolio selection. However, an independent investor has original ideas in investment, and thus we need to consider more types of risk attitudes for an investor’s portfolio selection when the guaranteed return rates are used to excess investment. To manage the excess investment by the risk preference, a new concept of s dimensions of excess investment is introduced to perceive the risk attitude of an investor for portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model. This example shows that the higher dimensions of excess investment derive lower expected return rates with lower constrained risk than that of dimension s = 1; and we suggest lower risk preference should select a higher dimension of excess investment. Then, the dimension of excess investment s = 2 can be applied for portfolio selection when the risk preference is lower.

OPTIMIZATION OF PORTFOLIO USING FUZZY SELECTION

The problem of portfolio optimization concerns the allocation of the investor’s wealth between several security alternatives so that the maximum profit can be obtained. One of the methods used is Fuzzy Portfolio Selection to understand it better. This method separates the objective function of return and the objective function of risk to determine the limit of the membership function that will be used. The goal of this study is to understand the application of the Fuzzy Portfolio Selection method over shares that have been chosen on a portfolio optimization problem, understand return and risk, and understand the budget proportion of each claim. The subject of this study is the shares of 20 companies included in Bursa Efek Indonesia from 1 January 2021 until 1 January 2022. The result of this study shows that from 20 shares, there are 10 shares that is suitable in the forming of optimal portfolio, those are ADRO (0%), ANTM (43.3%), ASII (0%), BBCA (0%), BBRI (0%), BBTN (0%), BRPT (0%), BSDE (0%), ERAA (16%), and INCO (40.7%). The expected return from the portfolio is 0.0878895207 or 8.8% for the return and 0.0226022117 or 2.3% for the risk.

A multi-objective approach based on Markowitz and DEA cross-efficiency models for the intuitionistic fuzzy portfolio selection problem

Nowadays, the main concerns of investors are choosing the best portfolio in a way that the highest possible return of investment can be achieved by accepting the least risk. In this regard, the classical Markowitz model is one of the most widely used models which helps investors get closer to their goals. On the other hand, data envelopment analysis (DEA) is also a practical technique that can analyze the efficiency of enterprises. However, in real problems, we have faced with several uncertainty issues and the intuitionistic fuzzy set (IFS) is one of the best tools to handle these phenomena. Therefore, in this paper, we combine all these tools and with returns of intuitionistic fuzzy numbers, propose a new combined Markowitz and the cross DEA models. Furthermore, to get the best portfolio of assets, this model obtains the efficiency of all companies and at the same time, fully covers all constraints of the Markowitz model. To show the practicality of the model, we studied a case study based on information of 50 active enterprises in the Tehran Stock Exchange and solved the proposed model using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The obtained results as well as the comparisons made with the existing approaches show the effectiveness of the proposed model.

Sustainable Fuzzy Portfolio Selection Concerning Multi-Objective Risk Attitudes in Group Decision

Fuzzy portfolio selection has resulted in many researchers to focus on this field. Based on the risk attitudes, this study discusses the risk attitudes in a decision group for portfolio selection. Therefore, we adopt the risk attitudes to describe the experts’ risk preferences and subjective judgments, and then we suppose that the risk seeker considers a higher return for an excess investment based on the selected guaranteed rate of return; the risk averter considers a shortage in investment for the securities whose return rates are smaller than the selected guaranteed rate of return; and finally, the risk neutral pursues the regular return rate. In order to solve the multi-objective return rate functions under the corresponding investment risks, the SMART-ROC weighting method is used to hybridize the multi-objective programming model to a linear programming model for solving the portfolio selection. Finally, we illustrate a numerical example and two risk scenarios to show the optimal portfolio selection under different investment risks. The results show that the proposed model can obtain a more robust portfolio than the compared models under different risk priorities in a decision group.

A Multi-period Fuzzy Portfolio Optimization Model with Short Selling Constraints

A Multi-period Fuzzy Portfolio Optimization Model with Short Selling Constraints

Multi-Criteria Fuzzy Portfolio Selection Based on Three-Way Decisions and Cumulative Prospect Theory

Multi-Criteria Fuzzy Portfolio Selection Based on Three-Way Decisions and Cumulative Prospect Theory

Estimating weight of stocks using ambiguity index and its application to type-2 intuitionistic fuzzy portfolio selection

Estimating weight of stocks using ambiguity index and its application to type-2 intuitionistic fuzzy portfolio selection

Hesitant fuzzy portfolio selection model with score and novel hesitant semi-variance

Because of the effectiveness in describing uncertain information, the hesitant fuzzy set (HFS) has been well applied in decision making and portfolio selection. Although there have been some risk measures based on the HFS, most of them consider all the membership degrees in the calculation. In fact, investors usually think that only the low membership degrees are undesirable, but this fact is ignored by some researchers in the existing hesitant fuzzy portfolio selection models. Therefore, to better capture investors’ risk attitudes, we extend the downside risk from stochastic environment to hesitant fuzzy environment and define a new hesitant semi-variance (HSV). Then, the corresponding theoretical analysis on the novel definitions is given by strictly mathematical deduction. Moreover, two new score-HSV portfolio selection models are proposed, in which the portfolio return is measured by the score, whereas the risk is measured by the HSV instead of deviation. The two new models can avoid neglecting the portfolios with distinctly high membership degrees, which are desirable for investors in practice. Finally, a case study is provided to illustrate the effectiveness of these models. Sensitivity analysis implies that results of the models are consistent with investors’ preferences for assets.

Adjustable Security Proportions in the Fuzzy Portfolio Selection under Guaranteed Return Rates

Based on the concept of high returns as the preference to low returns, this study discusses the adjustable security proportion for excess investment and shortage investment based on the selected guaranteed return rates in a fuzzy environment, in which the return rates for selected securities are characterized by fuzzy variables. We suppose some securities are for excess investment because their return rates are higher than the guaranteed return rates, and the other securities whose return rates are lower than the guaranteed return rates are considered for shortage investment. Then, we solve the proposed expected fuzzy returns by the concept of possibility theory, where fuzzy returns are quantified by possibilistic mean and risks are measured by possibilistic variance, and then we use linear programming model to maximize the expected value of a portfolio’s return under investment risk constraints. Finally, we illustrate two numerical examples to show that the expected return rate under a lower guaranteed return rate is better than a higher guaranteed return rates in different levels of investment risks. In shortage investments, the investment proportion for the selected securities are almost zero under higher investment risks, whereas the portfolio is constructed from those securities in excess investments.

A new approach to the bi-criteria multi-period fuzzy portfolio selection

The proposed approach to the bi-criteria multi-period fuzzy portfolio selection is based on the observation that the treating the variance as a measure of portfolio risk provides sometimes questionable results. Therefore, the simple criteria of portfolio risk and return are proposed. Based on them and three popular methods for local criteria aggregation, a new fuzzy portfolio selection one-period model has been developed. It is shown that this model provides reasonable results coinciding with common sense. Based on this model, a new two-stage bi-criteria optimization approach to portfolio selection has been developed, tested and used as a main component of proposed multi-period portfolio selection model. A method for obtaining fuzzy distributions of stocks returns based on real market data is developed and used for the optimal portfolio selection. In some cases, to make the presentation of developed methods features more transparent, the problem simplification, when all market decisions (Buy, Sell and Hold) were considered as right ones was used. But finally, the real-world market decisions which are generated using the stock trading expert system based on the real market data were used for the portfolio selection. To do this, the known stock trading expert system has been applied, which was adapted for the conditions of the considered stock markets (NYSE and NASDAQ) providing best results after optimization. Using the real-world examples, it is shown that incorporating the stock signals (decisions) Buy, Sell and Hold in the multi-period portfolio selection models improves strongly the models results making them closer to reality.

A novel fuzzy dominant goal programming for portfolio selection with systematic risk and non-systematic risk.

In this paper, we consider a fuzzy portfolio selection problem with systematic risk and non-systematic risk simultaneously. These two kinds of risks are measured by beta coefficient and random error variance obtained by Sharp Single Index Model. The total risk as the objective of portfolio decision is obtained by weighting the two kinds of risk. Among them, the weight of systematic risk lambda is regarded as the degree of investors' attention to system risk in economic sense. In addition, the fuzzy return and the degree of diversification are measured by triangular fuzzy number and entropy, respectively. And they are also considered the goal of investment decisions. Hence, a tri-objective portfolio is proposed in this paper. For the fuzzy objectives in the model, a goal programming method based on fuzzy dominance is proposed, which can help investors better capture the ideal point of fuzzy returns according to their risk preference. Finally, combined with the systematic impact of COVID-19 on the financial market, we make an empirical analysis based on our proposed model. The results show that the total risk will be on the high side when lambda value is too large or too small. That means paying too much or little attention to the systematic risk will lead investors to bear more risk. In addition, when investors ignore the systematic risk; that is, the lambda value is low, and investors will concentrate their funds in the same industry.

Fuzzy Portfolio Selection with Sugeno Type Fuzzy Neural Network: Investing in the Mexican Stock Market

The objective of this research is to compare the returns of the portfolios developed by the proposed methodology called Fuzzy Portfolio Selection with Sugeno Type Fuzzy Neural Network against Markowitz’s portfolio theory; to identify the best investment model. For this purpose, we used ten stock time series of the Mexican market in daily format from January 2, 2015, to May 15, 2020, to get the portfolios every week from May 15 to June 12, 2020. The principal result is that our methodology recognized the behavior of each share, generates better risk management, and higher returns in comparison with the traditional techniques. The recommendation is to evaluate other stocks and markets to verify the efficiency of our model, the limitation is that a fundamental analysis must precede the tool, and the originality is the new technique proposed. The main conclusion is that the portfolio selection model based on fuzzy neural networks generated two models that do not have negative returns in any week, the cumulative return obtained was up to 15.68%.

Fuzzy Portfolio Selection in COVID-19 Spreading Period Using Fuzzy Goal Programming Model

While the international lockdown for the COVID-19 pandemic has greatly influenced the global economy, we are still confronted with the dilemma about the economy recession when the stock market hits record highs repeatedly. As we know, since portfolio selection is often affected by many non-probabilistic factors, it is of not easiness to obtain the precise probability distributions of the return rates. Therefore, fuzzy portfolio model is proposed for solving the efficient portfolio when the economy environment remains in vagueness for the future. To manage such an investment, we revise the Chen and Tsaur’s fuzzy portfolio model by using fuzzy goal programming model. Then, two numerical examples are illustrated by the proposed model which shows that the portfolio selection can be solved by the linguistic imprecise goal of the expected return. With different linguistic descriptions for the imprecise goal of expected return for the future stock market, the optimal portfolio selection that can be solved under different investment risks which is considered better than Chen and Tsaur’s model in real world situations. The sensitivity analysis with some parameter groups can be provided for more invested risk selection in the portfolio analysis.