A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

doi:10.1016/j.cam.2009.07.019

Cited by 65 publications

(20 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Their experimental result show that, for all examined investor risk preferences and investment assets, the proposed model is a fast and efficient way of solving the trade-off in the mean-variance-skewness portfolio problem. They also concluded that their proposed model [202] designed a hybrid intelligent algorithm by integrating simulated annealing algorithm, NN and fuzzy simulation techniques in order to solve portfolio selection problems. In this model, NN is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for NN.…”

Section: Portfolio Managementmentioning

confidence: 95%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

431

192

View full text Add to dashboard Cite

Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

show abstract

Section: Portfolio Managementmentioning

confidence: 95%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

431

192

View full text Add to dashboard Cite

show abstract

“…For different values of parameter ρ, we solve problem (22) via Lingo software, and solution results are reported in Table 6. Since the matrix H is positive semidefinite, it follows that problem (22) is a convex programming.…”

Section: Expectation-spread Methodsmentioning

confidence: 99%

“…Since the credibility of fuzzy event and the expected value of fuzzy variable were defined in [16], an axiomatic approach called credibility theory has been developed [17,18]. Some interesting applications about credibility to problems in quantitative finance have been studied in the literature [19][20][21][22][23], which provide us motivation to study the portfolio optimization problem from a new viewpoint.…”

Section: Introductionmentioning

confidence: 99%

Spread of fuzzy variable and expectation-spread model for fuzzy portfolio optimization problem

Liu

2010

J. Appl. Math. Comput.

View full text Add to dashboard Cite

Using Lebesgue-Stieltjes (L-S) integral, we first define the nth moment of a fuzzy variable, and derive some useful moment formulas for common possibility distributions. Particularly, the second moment, called spread, can be represented as quadratic convex functions with respect to fuzzy parameters. Then, we take the spread as a new risk criteria in fuzzy decision systems, combine it with the expectation of fuzzy variable, and develop three classes of fuzzy expectation-spread models for portfolio optimization problems. In the case when the returns are trapezoidal and triangular fuzzy variables, we employ the parametric representations of spreads to turn the proposed expectation-spread models into their equivalent parametric programming problems. In the case when the return parametric matrix is full row rank, the equivalent parametric programming problems become convex programming ones that can be solved by conventional optimization methods or general purpose softwares. Finally, we demonstrate the developed modeling ideas via three numerical examples, and also compare the expectation-spread method with traditional expectation-variance method via a number of numerical experiments.

show abstract

“…Также разработаны и совершенствуются интеллекту-альные нечеткие алгоритмы комплектования портфе-ля. В частности, рассматриваются модели на основе средней дисперсии, энтропийные модели, нейронные сети и генетические алгоритмы [3]. В ряде работ под-черкивается необходимость согласования портфеля со стратегией организации.…”

Section: анализ литературных данных и постановка проблемыunclassified

“…Коли мова йде про безпе-ку АЕС, існує дві групи ризиків: ризики зовнішніх впливів (наприклад, техногенних катастроф або ди-версійних дій) [1,2] і ризики внутрішніх процесів (наприклад, старіння обладнання та засобів контролю, тестові втручання в роботу систем, помилки персо-налу, тощо) [3][4][5]. Тому й програма супроводження систем захисту АЕС повинна оперативно реагувати на ризики обох груп, вчасно та ефективно «перемикати-ся» між ними.…”

Section: вступunclassified

Optimization of regional project portfolio by the cluster analysis method

Осауленко

2016

EEJET

View full text Add to dashboard Cite

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Cited by 65 publications

References 42 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Spread of fuzzy variable and expectation-spread model for fuzzy portfolio optimization problem

Optimization of regional project portfolio by the cluster analysis method

Contact Info

Product

Resources

About