Operations research: deterministic optimization models
Operations research: deterministic optimization models
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A model for portfolio selection with order of expected returns
Computers and Operations Research
Portfolio selection under independent possibilistic information
Fuzzy Sets and Systems - Special issue on soft decision analysis
A possibilistic approach to selecting portfolios with highest utility score
Fuzzy Sets and Systems - Special issue: Soft decision analysis
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Possibilistic mean-variance models and efficient frontiers for portfolio selection problem
Information Sciences: an International Journal
Asset portfolio optimization using fuzzy mathematical programming
Information Sciences: an International Journal
Portfolio optimization of equity mutual funds with fuzzy return rates and risks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new Chance-Variance optimization criterion for portfolio selection in uncertain decision systems
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Based on possibilistic mean and variance theory, this paper deals with the portfolio adjusting problem for an existing portfolio under the assumption that the returns of risky assets are fuzzy numbers and there exist transaction costs in portfolio adjusting precess. We propose a portfolio optimization model with V-shaped transaction cost which is associated with a shift from the current portfolio to an adjusted one. A sequential minimal optimization (SMO) algorithm is developed for calculating the optimal portfolio adjusting strategy. The algorithm is based on deriving the shortened optimality conditions for the formulation and solving 2-asset sub-problems. Numerical experiments are given to illustrate the application of the proposed model and the efficiency of algorithm. The results also show clearly the influence of the transaction costs in portfolio selection.