The admissible portfolio selection problem with transaction costs and a neural network scheme

Abstract

In this paper, we study the portfolio optimization model with transaction costs under
the assumption that there exist admissible errors on expected returns and risks of assets.
We propose an admissible efficient portfolio selection problem and design a neural network for our proposed problem. Finally, The presented
neural network framework guarantees to obtain the optimal solution of the admissible portfolio problems. The existence and
convergence of the trajectories of the network are studied. The Lyapunov stability and globally convergence of the considered neural network are also
shown. we offer a numerical example to
illustrate the proposed effective approach.