Portfolio selection using neural networks

Fernández, Alberto; Gómez, Sergio

doi:10.1016/j.cor.2005.06.017

Cited by 264 publications

(143 citation statements)

References 13 publications

Supporting

Mentioning

140

Contrasting

Unclassified

Order By: Relevance

“…The optimal portfolio selection problem in two, appropriate model selection and efficient and effective method to achieve the optimal solution are very important. Usually, the traditional mathematical methods and algorithms for solving these models are not appropriate and accurate solutions for this kind of problem, mathematical programming algorithms effective and efficient programs exist (Fernandez and Gomez, 2007).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Evaluation of Particle Swarm Algorithm and Genetic Algorithms Performance at Optimal Portfolio Selection

Zarra-Nezhad

Hajiabad

Fakherinia

2015

Asian Economic and Financial Review

View full text Add to dashboard Cite

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Evaluation of Particle Swarm Algorithm and Genetic Algorithms Performance at Optimal Portfolio Selection

Zarra-Nezhad

Hajiabad

Fakherinia

2015

Asian Economic and Financial Review

View full text Add to dashboard Cite

show abstract

“…The PO model defined by Eq. (3)- (5) and (7)- (9) is called the general form of portfolio optimization model (Fernandez and Gomez, 2007). This model is an integer quadratic programming model.…”

Section: ( )mentioning

confidence: 99%

Optimizing short-term production plan using a portfolio optimization model

Osanloo

Rahmanpour

2017

REM, Int. Eng. J.

View full text Add to dashboard Cite

Short-term production plans are the basis for operational mine production schedules. They concentrate on making long-term mine plans operationally feasible. It ensures a steady flow of product for meeting blending targets. Due to the quality variation of material, blend optimization is an uncertainty based optimization problem. There are different approaches toward uncertainty management and the current paper investigates a portfolio optimization model in order to minimize the risks in short-term plans. In this paper, a fuzzy linear programming model is formulated to provide a set of options for the mine plan. These blending options are treated as portfolios. Then a model for the optimal selection of a portfolio is introduced. The objective of the model is to maximize the expected return of the portfolio under constraints limiting its variance. The model is applied in a limestone mine complex.

show abstract

“…Additionally, dealers give diverse significance to these components, contingent upon their exchanging identities, for example the amount of risk they take. This requires an instrument that can accurately weigh different types of risk and reward [4]. Of course, this is precisely what a multi-criteria unbiased optimization framework is designed to do.…”

Section: Introductionmentioning

confidence: 99%

A Novel Portfolio Optimization Model Based on Pattern Recognition Algorithms

Tan¹,

Wang²,

Xiong³

2017

Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)

View full text Add to dashboard Cite

There are two types of modern portfolio theory: narrow and broad. A narrowly conceived portfolio theory is often referred to by the name Markowitz portfolio theory. Often generalized portfolio theory, in addition to classical portfolio theory, and various alternative portfolio theories, include the capital asset pricing model and an effective securities market theory of capital markets. At the same time, because the traditional EMH cannot explain market anomalies, the various portfolio theories are have been challenged by behavioral financial theory. This study uses pattern recognition algorithms to improve the traditional portfolio optimization models. By utilizing improved particle swarm optimization algorithms, a hybrid optimization model based on this would be the result of the experiment, compared with that based on the traditional neural network model for quantitative model effect. Particle swarm optimization algorithms were originally generated to produce graphical simulations of flocks of birds and other such unpredictable movements. The developmental basis of the algorithms used in this experiment are observations of animal social behavior, demonstrating that sharing of information society provides groups with an evolutionary advantage. By joining near the speed of matching, taking multidimensional search into account, and according to speeds and distances, we can thus form the original version of particle swarm optimization. After introducing the parameters of inertia weight to better control the development of swarm behavior, we have produced the standard version. This study will focus on analysis to improve the process of generating particle swarm optimization models.

show abstract

Portfolio selection using neural networks

Cited by 264 publications

References 13 publications

Evaluation of Particle Swarm Algorithm and Genetic Algorithms Performance at Optimal Portfolio Selection

Evaluation of Particle Swarm Algorithm and Genetic Algorithms Performance at Optimal Portfolio Selection

Optimizing short-term production plan using a portfolio optimization model

A Novel Portfolio Optimization Model Based on Pattern Recognition Algorithms

Contact Info

Product

Resources

About