Decreasing the Computational Workload in Portfolio Optimization | Chapter 8 | Advanced Aspects of Engineering Research Vol. 15

 This chapter demonstrates how market indices can be used to estimate portfolio problem parameters. An evaluation approach is developed for reducing the computing workload in the specification and solution of portfolio optimization problems. The programme uses numerical relationships to transform the traditional portfolio problem into an optimization problem with Capital Market Theory parameters. The addition of the beta coefficient leads in a reduction in computing workload. As a result, asset characteristics are not estimated based on an individual assessment of each asset return. Only by estimating the market index and subsequent evaluations based on asset risk and return relationships can the features be discovered. This reduces the number of evaluations required for the portfolio problem's covariance matrix. The algorithm is demonstrated using indexes and mutual funds from the Bulgarian Stock Exchange to solve a portfolio problem. The collected results aid in the decision-making process for stock market investments.

Author (S) Details

T. Stoilov
Institute of Information and Communication Technologies – Bulgarian Academy of Sciences, Sofia, Bulgaria.

K. Stoilova
Institute of Information and Communication Technologies – Bulgarian Academy of Sciences, Sofia, Bulgaria.

M. Vladimirov
Varna University of Economics, Varna, Bulgaria.

View Book :- https://stm.bookpi.org/AAER-V15/article/view/1744

Experimental Study on Optimal Portfolio Strategies under a Shortfall Constraint | Chapter 6 | Recent Advances in Science and Technology Research Vol. 5

 We impose dynamically, a shortfall constraint in terms of Tail Conditional Expectation on the portfolio selection problem in continuous time, in order to obtain optimal strategies. The financial market is composed of n risky assets driven by geometric Brownian motion and one risk-free asset. The method of Lagrange multipliers is combined with the Hamilton-Jacobi-Bellman equation to insert the constraint into the resolution framework. The constraint is re-calculated at short intervals of time throughout the investment horizon. A numerical method is applied to obtain an approximate solution to the problem. We find that the imposition of the constraint curbs investment in the risky assets.

Author (s) Details

Daniel Akume
Department of Computer Science, University of Buea, P.O. Box 63, Buea, Cameroon.

Bernd Luderer
Faculty of Mathematics, Chemnitz University of Technology, P.O. Box 964, Chemnitz, 09107, Germany.

Ralf Wunderlich
Department of Mathematics, Zwickau University of Applied Sciences, P.O. Box 201037, Zwickau, 08012, Germany.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/241