The Use of Quadratic Programming and Its Application to Optimal Portfolio Selection
One of the most effective techniques, which along its unique advantages, constitutes the basis of modern investment strategies is index tracking. Given the undeniable importance of this approach to prosperity of capital market, the present study is an attempt to examine and implement the strategy. Selection of optimum portfolio capable of tracking total price and cash return was studied with employing a combination of genetic algorithm and quadratic programming. To simulate the data and meet the prerequisites for implementation of the proposed algorithm, an artificial neural network was used. The results of data analysis supported high accuracy and acceptable performance of the portfolio obtained from the combined method. Achieving equal and better performance comparing with the total index was one of the unique features of the proposed method.
Abstract For a holistic view on commercial mobility, traditional monomethod approaches are not sufficient. The purpose of this chapter is to give a better understanding of the benefits of triangulation in the mobility research context, more precisely in exploring the substitution potential for BEV in commercial fleets. It is shown that the combination of multiple methods can help to cope with the challenges of commercial mobility understanding. One major finding is that the mixture of quantitative and qualitative data can reveal the differences between subjective and objective behavior in a commercial mobility context. The understanding of these differences leads to drivers and barriers for establishing the new technology and a more profound estimation of the potential for BEV substitutions.