Although the mean‐variance portfolio selection model has been investigated in the literature, the difficulty associated with the application of the model when dealing with large‐scale problems is limited. The aim of this paper is to close the gap by using the quadratic risk (standard deviation risk) function and finite iteration technique to remove difficulties in quadratic programming.
Using van de Panne' approach, this paper proposes a finite technique to optimize large‐scale portfolio selection problem.
The proposal of parallel algorithm structure to the model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process.
The proposal of parallel algorithm structure to the mean‐variance portfolio selection model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process. An empirical example that illustrates the application and benefits of the method is provided.
